2091 lines
941 KiB
Text
2091 lines
941 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 73,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"import os\n",
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"import xml.etree.ElementTree as ET\n",
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"from collections import Counter\n",
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"import math\n",
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"from lib_vhdl_gen import *\n",
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"from lib_CSTasks import *\n",
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"import warnings\n",
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"\n",
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"import json\n",
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"import argparse\n",
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"import networkx as nx\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from pulp import LpMaximize, LpProblem, LpStatus, lpSum, LpVariable , LpMinimize ,PULP_CBC_CMD ,SCIP_CMD,SCIP_PY\n",
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"\n",
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"def LinProResult(task_to_node):\n",
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" from pulp import LpMaximize, LpProblem, LpStatus, lpSum, LpVariable , LpMinimize\n",
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" # Create the model\n",
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" model = LpProblem(name='small-problem', sense=LpMinimize)\n",
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"\n",
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" t_comp=LpVariable(name='t_comp', lowBound=0)\n",
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"\n",
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" #######################################################################################################\n",
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" # Create random mapping and according cs paths for all connections\n",
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" #######################################################################################################\n",
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"\n",
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" cs_path_list=[]\n",
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" num_tasks = 20 # Total tasks\n",
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" num_nodes = 15 # Total nodes\n",
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"\n",
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" # DO NOT SET RANDOM NODES\n",
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" # task_to_node = assign_nodes_to_tasks_unique(num_tasks, num_nodes)\n",
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"\n",
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" memory_router=25\n",
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" flits_per_packet=31 # (31 data + 1 header)\n",
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"\n",
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" map_additional_path=\"/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/map_addition.xml\"\n",
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"\n",
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" for i in task_to_node:\n",
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" if i%2==1 and task_to_node.get(i+1)!=None:\n",
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" cs_path_list.append((task_to_node.get(i),task_to_node.get(i+1)))\n",
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"\n",
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"\n",
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" xml_file = \"/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/map.xml\" # Input XML file\n",
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" output_file = \"/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/map.xml\" # Output XML file\n",
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"\n",
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"\n",
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" update_xml_map(xml_file, task_to_node, output_file)\n",
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"\n",
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"\n",
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" # cs_path_list=[(0,1),(1,2),(2,3),(3,4),(4,5),(5,6),(6,7),(7,8),(8,9),(9,10)]\n",
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" cs_data_xml,cs_task_xml,cs_map_xml,dest_address_list_int=cs_tasks(source_destination = cs_path_list, start_task=30, start_PE=16)\n",
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" cs_task_xml=\"\"\n",
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" ps_link_width=32\n",
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" cs_link_width=80\n",
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"\n",
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" #######################################################################################################\n",
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" # Add the new tasks and mapping of the cs configure tasks to the xml files/xml strings and read files\n",
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" #######################################################################################################\n",
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"\n",
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"\n",
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" with open('/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/data.xml', 'r') as file:\n",
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" data_xml = file.read()\n",
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"\n",
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" closing_tag_position = data_xml.rfind('</tasks>') # Find the position of the closing </tasks>\n",
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" if closing_tag_position != -1:\n",
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" # Insert the new_line before </tasks>\n",
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" data_xml = data_xml[:closing_tag_position] + '\\n' + cs_task_xml + '\\n' + data_xml[closing_tag_position:]\n",
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"\n",
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" tree = ET.ElementTree(ET.fromstring(data_xml))\n",
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" root = tree.getroot()\n",
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"\n",
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"\n",
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" with open('/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/map.xml', 'r') as file:\n",
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" map_xml = file.read()\n",
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"\n",
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" closing_tag_position2 = map_xml.rfind('</map>')\n",
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" if closing_tag_position2 != -1:\n",
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" map_xml = map_xml[:closing_tag_position2] + '\\n' + cs_map_xml + '\\n' + map_xml[closing_tag_position2:]\n",
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"\n",
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" tree_map = ET.ElementTree(ET.fromstring(map_xml))\n",
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" root_map = tree_map.getroot()\n",
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"\n",
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"\n",
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" tasks=root.findall('tasks')[0]\n",
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"\n",
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"\n",
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"\n",
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" #######################################################################################################\n",
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" # Go through map file and extract the mappings and create a list of number of mapped tasks to PEs\n",
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" #######################################################################################################\n",
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" PE_task_pairs = [] # pair of all tasks and their PE\n",
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" for bind in root_map.findall('bind'):\n",
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" task_value = bind.find('task').get('value')\n",
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" PE_value = bind.find('node').get('value')\n",
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" \n",
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" PE_task_pairs.append((int(PE_value), int(task_value)))\n",
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"\n",
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" Task_init_num=len(PE_task_pairs) #number of tasks in the data.xml file\n",
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"\n",
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"\n",
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" PE_values = [PE for PE, task2 in PE_task_pairs] #list of just PEs\n",
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" PE_count = Counter(PE_values) #count how often PEs appear in data.xml file\n",
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"\n",
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" PE_count_start = {key: 0 for key in range(len(PE_count)+50)} # dict of each PE init with 0 to count how many destinations are created with that PE later one\n",
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"\n",
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" for PE, count in PE_count.items():\n",
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" if count>50:\n",
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" raise ValueError(\"Not implemented more than 50 task per note yet\")\n",
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"\n",
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" #######################################################################################################\n",
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" # Create a list of all tasks from the data.xml file\n",
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" #######################################################################################################\n",
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" delay_comp_send_total=0\n",
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" connection_num=0\n",
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"\n",
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" Task_list=[] # list of all tasks objects\n",
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" start_task_list=[]\n",
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"\n",
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" for task2 in tasks.findall('task'): # iterate through all tasks in data.xml\n",
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" dst_element_send_list={}\n",
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" destinations=task2.find('generates').find('possibility').find('destinations').findall('destination')\n",
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" if len(destinations)==0:\n",
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" consume_task=True\n",
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" else:\n",
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" consume_task=False\n",
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" delay=destinations[0].find('delay').get('max')\n",
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"\n",
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" requires = task2.find('requires')\n",
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"\n",
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"\n",
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" Task_num=task2.get('id')\n",
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" if requires is not None:\n",
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" sources = requires.findall('requirement')\n",
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" else:\n",
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" start_task_list.append(int(Task_num))\n",
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"\n",
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" repetition=task2.find('repeat').get('min')\n",
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" dest_num=len(destinations)\n",
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" src_num=len(sources)\n",
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"\n",
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" for dest in destinations:\n",
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" dst_element_send_list[int(dest.find('task').get('value'))]=int(dest.find('count').get('max'))\n",
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"\n",
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"\n",
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" PE_for_task = next((PE for PE, task2 in PE_task_pairs if task2 == int(Task_num)), None) #PE for the task 'Task_num'\n",
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" \n",
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" if src_num>5:\n",
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" raise ValueError(\"more then 50 source of a task are not implemented yet\")\n",
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" if src_num==0:\n",
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" raise ValueError(\"generator task with no source not implemented yet\")\n",
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" \n",
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" dst_element_list=[]\n",
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" dst_task_list=[]\n",
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" snd_out_list=[]\n",
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" for j in destinations:\n",
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" dst_task_list.append(int(j.find('task').get('value')))\n",
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" for pair in PE_task_pairs:\n",
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" if int(j.find('task').get('value'))==pair[1]:\n",
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"\n",
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" #Only the configuration (delay==-1) task youse the destination task in the data.xml file as the router number and not the destination number\n",
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" if delay==\"-1\":\n",
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" dst_element_list.append(pair[1])\n",
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" else:\n",
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" dst_element_list.append(pair[0])\n",
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" snd_out_list.append(j.find('count').get('max'))\n",
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"\n",
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" if len(dst_element_list)==1:\n",
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" dst_element_list.append(None)\n",
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" snd_out_list.append(None) \n",
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"\n",
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" req_flits_list=[]\n",
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" src_task_list=[]\n",
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" src_type_list=[]\n",
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" for n in sources:\n",
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" req_flits_list.append(n.find('count').get('max'))\n",
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" src_task_list.append(n.find('source').get('value'))\n",
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" src_type_list.append(n.find('type').get('value'))\n",
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"\n",
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" req_flits_list.append(None)\n",
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" src_task_list.append(None) \n",
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"\n",
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" ###########################################################################################################################\n",
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" # create linear programming constrains\n",
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" ###########################################################################################################################\n",
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"\n",
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" time_per_flit=0.649\n",
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" flit_number=0\n",
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" repetitions=10\n",
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" max_task_num=20\n",
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"\n",
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" # 24 as a factor for delay of mem data per flit is tested to be the most accurate\n",
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" mem_delay=(len(xyz_routing(int(Task_num),memory_router))-1)*24+math.ceil(task2memory_bits(Task_num,map_additional_path)/flits_per_packet)+2\n",
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" if len(snd_out_list) > 0:\n",
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" flit_number=snd_out_list[0]\n",
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"\n",
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" if int(Task_num) % 2!=0 and connection_num<len(dest_address_list_int):\n",
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" flit_delay=math.ceil(int(flit_number)*time_per_flit*(ps_link_width/cs_link_width)*(len(dest_address_list_int[connection_num])-1))\n",
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" connection_num+=1\n",
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" else:\n",
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" flit_delay=0\n",
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"\n",
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"\n",
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"\n",
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" delay_comp_send=int(delay)+flit_delay+mem_delay\n",
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" delay_comp=int(delay)+mem_delay\n",
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" delay_send=flit_delay\n",
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" delay_comp_send_total+=delay_comp_send\n",
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"\n",
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" # create variables\n",
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" exec(f\"t{Task_num}_0 = LpVariable(name='t{Task_num}_0', lowBound=0)\",locals())\n",
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" \n",
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" temp_str=f\"t_comp_{Task_num} = LpVariable(name='t_comp_{Task_num}', lowBound=0)\"\n",
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" send_str=f\"t_send_{Task_num} = LpVariable(name='t_send_{Task_num}', lowBound=0)\"\n",
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" \n",
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" exec(temp_str,locals())\n",
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" exec(send_str,locals())\n",
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"\n",
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" # set static constraints for comp and send time\n",
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" exec(f\"model += (t_comp_{Task_num} == {delay_comp}, 'constraint_t_comp_{Task_num}')\",locals())\n",
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" exec(f\"model += (t_send_{Task_num} == {delay_send}, 'constraint_t_send_{Task_num}')\",locals())\n",
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"\n",
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"\n",
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" src_task_list_filtered = [x for x in src_task_list if x is not None]\n",
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"\n",
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" # task constraints for first repetition\n",
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" for i in src_task_list_filtered:\n",
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" if int(i)<int(Task_num):\n",
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" exec(f\"model += (t{i}_0 + t_comp_{Task_num} + t_send_{Task_num} <= t{Task_num}_0, 'constraint_T{Task_num}_{i}_0')\",locals())\n",
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"\n",
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" # task constraints for rest of repetitions\n",
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" for m in range(repetitions-1):\n",
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" exec(f\"t{Task_num}_{m+1} = LpVariable(name='t{Task_num}_{m+1}', lowBound=0)\",locals())\n",
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" for i in src_task_list_filtered:\n",
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" if int(i)<int(Task_num):\n",
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" exec(f\"model += (t{i}_{m+1} + t_comp_{Task_num} + t_send_{Task_num}<= t{Task_num}_{m+1}, 'constraint_T{Task_num}_{i}_{m+1}')\",locals())\n",
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"\n",
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" # constraints for multi use of links\n",
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" multi_pair_list=count_consecutive_pairs_no_file(dest_address_list_int)\n",
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" for multi_pair in multi_pair_list:\n",
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" index_list=find_pair_indices(multi_pair,dest_address_list_int)\n",
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" for index in range(len(index_list)):\n",
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" prev_idx=index_list[index-1]*2+1\n",
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" if index+1>len(index_list)-1:\n",
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" next_idx=index_list[0]*2+1\n",
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" else:\n",
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" next_idx=index_list[index+1]*2+1\n",
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" curr_idx=index_list[index]*2+1\n",
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" last_idx=index_list[-1]*2+1\n",
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"\n",
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" # print(f\"index_list: {index_list} curr_idx: {curr_idx} prev_idx: {prev_idx} next_idx: {next_idx} \")\n",
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"\n",
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" for m in range(repetitions-1):\n",
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" constraint_name = f\"constraint_MultiLink{curr_idx}_{prev_idx}_{m+1}\"\n",
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" if constraint_name not in model.constraints:\n",
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" if last_idx!=prev_idx:\n",
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" prev_rep=m+1\n",
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" else:\n",
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" prev_rep=m\n",
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" exec(f\"model += (t{prev_idx}_{prev_rep} + t_send_{curr_idx} <= t{curr_idx}_{m+1}, '{constraint_name}')\",locals())\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" # constraint: first task in accel can only start when last task is done + last task in accel can only start when first task of next accel is done. Here hardcoded as always two tasks in accel\n",
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" for Task_num in range(max_task_num-2):\n",
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" for m in range(repetitions-1):\n",
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" exec(f\"model += (t{int(Task_num)+1}_{m} + t_comp_{Task_num} + t_send_{Task_num} <= t{Task_num}_{m+1}, 'constraint_Task{Task_num}_{int(Task_num)+1}_{m+1}')\",locals())\n",
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"\n",
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"\n",
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" # constraints of first task\n",
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" exec(\"model += (t_comp_0 <= t0_0, 'constraint_0_0_0')\",locals())\n",
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"\n",
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" # objective\n",
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" exec(\"model += t18_9\",locals())\n",
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"\n",
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" # print(\"CONSTRAINT LENGTH\")\n",
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" print(model)\n",
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"\n",
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" status = model.solve()\n",
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"\n",
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"\n",
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" # for var in model.variables():\n",
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" # print(f\"{var.name} = {var.varValue}\")\n",
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"\n",
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" print(f\"objective: {model.objective.value()}\")\n",
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" return(model.objective.value())\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def CreateNetworkXGraph(task_to_node={0: 12, 1: 12, 2: 2, 3: 2, 4: 14, 5: 14, 6: 7, 7: 7, 8: 13, 9: 13, 10: 1, 11: 1, 12: 3, 13: 3, 14: 10, 15: 10, 16: 4, 17: 4, 18: 8, 19: 8},detailed=False,Figure=False,flit_delay_consistent=False):\n",
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" #######################################################################################################\n",
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" # Create random mapping and according cs paths for all connections\n",
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" #######################################################################################################\n",
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"\n",
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" cs_path_list=[]\n",
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" num_tasks = 20 # Total tasks\n",
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" num_nodes = 15 # Total nodes\n",
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"\n",
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" # DO NOT SET RANDOM NODES\n",
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" # task_to_node = assign_nodes_to_tasks_unique(num_tasks, num_nodes)\n",
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"\n",
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" memory_router=25\n",
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" flits_per_packet=31 # (31 data + 1 header)\n",
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"\n",
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" map_additional_path=\"/home/sfischer/Documents/projects/wk_AI2Task/ReWrite/map_addition.xml\"\n",
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"\n",
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" for i in task_to_node:\n",
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" if i%2==1 and task_to_node.get(i+1)!=None:\n",
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" cs_path_list.append((task_to_node.get(i),task_to_node.get(i+1)))\n",
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"\n",
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" cs_data_xml,cs_task_xml,cs_map_xml,dest_address_list_int=cs_tasks(source_destination = cs_path_list,\n",
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" start_task=30,\n",
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" start_PE=16\n",
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" )\n",
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" cs_task_xml=\"\"\n",
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" ps_link_width=32\n",
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" cs_link_width=80\n",
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"\n",
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" #######################################################################################################\n",
|
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" # Add the new tasks and mapping of the cs configure tasks to the xml files/xml strings and read files\n",
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" #######################################################################################################\n",
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"\n",
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"\n",
|
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" with open('/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/data.xml', 'r') as file:\n",
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" data_xml = file.read()\n",
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"\n",
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" closing_tag_position = data_xml.rfind('</tasks>') # Find the position of the closing </tasks>\n",
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" if closing_tag_position != -1:\n",
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" # Insert the new_line before </tasks>\n",
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" data_xml = data_xml[:closing_tag_position] + '\\n' + cs_task_xml + '\\n' + data_xml[closing_tag_position:]\n",
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"\n",
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" tree = ET.ElementTree(ET.fromstring(data_xml))\n",
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" root = tree.getroot()\n",
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"\n",
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" tasks=root.findall('tasks')[0]\n",
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"\n",
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" #######################################################################################################\n",
|
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" # Create a list of all tasks from the data.xml file\n",
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" #######################################################################################################\n",
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" connection_num=0\n",
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" start_task_list=[]\n",
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" G = nx.DiGraph()\n",
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" first=True\n",
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" last =tasks.findall('task')[-1]\n",
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" last_true=False\n",
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" for task2 in tasks.findall('task'): # iterate through all tasks in data.xml\n",
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"\n",
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" if last==task2:\n",
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" last_true=True\n",
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" dst_element_send_list={}\n",
|
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" destinations=task2.find('generates').find('possibility').find('destinations').findall('destination')\n",
|
|
" if len(destinations)==0:\n",
|
|
" consume_task=True\n",
|
|
" else:\n",
|
|
" consume_task=False\n",
|
|
" delay=destinations[0].find('delay').get('max')\n",
|
|
"\n",
|
|
" requires = task2.find('requires')\n",
|
|
"\n",
|
|
"\n",
|
|
" Task_num=task2.get('id')\n",
|
|
" if requires is not None:\n",
|
|
" sources = requires.findall('requirement')\n",
|
|
" else:\n",
|
|
" start_task_list.append(int(Task_num))\n",
|
|
"\n",
|
|
" src_num=len(sources)\n",
|
|
"\n",
|
|
" for dest in destinations:\n",
|
|
" dst_element_send_list[int(dest.find('task').get('value'))]=int(dest.find('count').get('max'))\n",
|
|
"\n",
|
|
" dst_element_list=[]\n",
|
|
" dst_task_list=[]\n",
|
|
" snd_out_list=[]\n",
|
|
" for j in destinations:\n",
|
|
" dst_task_list.append(int(j.find('task').get('value')))\n",
|
|
" snd_out_list.append(j.find('count').get('max'))\n",
|
|
"\n",
|
|
" if len(dst_element_list)==1:\n",
|
|
" dst_element_list.append(None)\n",
|
|
" snd_out_list.append(None) \n",
|
|
"\n",
|
|
" req_flits_list=[]\n",
|
|
" src_task_list=[]\n",
|
|
" src_type_list=[]\n",
|
|
" for n in sources:\n",
|
|
" req_flits_list.append(n.find('count').get('max'))\n",
|
|
" src_task_list.append(n.find('source').get('value'))\n",
|
|
" src_type_list.append(n.find('type').get('value'))\n",
|
|
"\n",
|
|
" req_flits_list.append(None)\n",
|
|
" src_task_list.append(None) \n",
|
|
"\n",
|
|
" ###########################################################################################################################\n",
|
|
" # create NetworkX Graph\n",
|
|
" ###########################################################################################################################\n",
|
|
"\n",
|
|
" time_per_flit=0.649 # for 1 hop\n",
|
|
" flit_number=0\n",
|
|
" repetitions=10\n",
|
|
" max_task_num=20\n",
|
|
"\n",
|
|
" if detailed==True:\n",
|
|
" # 24 as a factor for delay of mem data per flit is tested to be the most accurate\n",
|
|
" mem_delay=(len(xyz_routing(int(Task_num),memory_router))-1)*24+math.ceil(task2memory_bits(Task_num,map_additional_path)/flits_per_packet)+2\n",
|
|
" if len(snd_out_list) > 0:\n",
|
|
" flit_number=snd_out_list[0]\n",
|
|
"\n",
|
|
" if int(Task_num) % 2!=0 and connection_num<len(dest_address_list_int):\n",
|
|
" flit_delay=math.ceil(int(flit_number)*time_per_flit*(ps_link_width/cs_link_width)*(len(dest_address_list_int[connection_num])-1))\n",
|
|
" connection_num+=1\n",
|
|
" else:\n",
|
|
" flit_delay=0\n",
|
|
" # if flit_delay_consistent==False:\n",
|
|
" # flit_delay=0\n",
|
|
" # else:\n",
|
|
" # flit_delay=math.ceil(int(flit_number)*time_per_flit*(ps_link_width/cs_link_width)*(len(dest_address_list_int[connection_num])-1))\n",
|
|
"\n",
|
|
"\n",
|
|
" delay_comp=int(delay)\n",
|
|
" delay_mem=mem_delay\n",
|
|
" delay_send=flit_delay\n",
|
|
" else:\n",
|
|
" \n",
|
|
" # simplified memory delay without taking distance to memory into account \n",
|
|
" mem_delay=math.ceil(task2memory_bits(Task_num,map_additional_path))\n",
|
|
"\n",
|
|
" # simplified flit delay, without taking distance and time for distance into account\n",
|
|
" if len(snd_out_list) > 0:\n",
|
|
" flit_number=snd_out_list[0]\n",
|
|
" flit_delay=math.ceil(int(flit_number)*(ps_link_width/cs_link_width))\n",
|
|
"\n",
|
|
" delay_comp=int(delay)\n",
|
|
" delay_send=flit_delay\n",
|
|
" delay_mem=mem_delay\n",
|
|
"\n",
|
|
"\n",
|
|
" \n",
|
|
" G.add_node(int(Task_num),delay_comp=delay_comp,delay_mem=delay_mem,delay_send=delay_send,src=first,dst=last_true)\n",
|
|
" first=False\n",
|
|
" src_task_list_filtered = [x for x in src_task_list if x is not None]\n",
|
|
"\n",
|
|
" # task constraints for first repetition\n",
|
|
" for idx,i in enumerate(src_task_list_filtered):\n",
|
|
" if int(i)<int(Task_num):\n",
|
|
" G.add_edge(int(i),int(Task_num),flits=math.ceil(int(req_flits_list[idx])*(ps_link_width/cs_link_width)))\n",
|
|
"\n",
|
|
" # for node in G.nodes:\n",
|
|
" # print(f\"Node {node} -> Edges: {list(G.predecessors(node))}, node delay_comp ={G.nodes[node][\"delay_comp\"]}\")\n",
|
|
"\n",
|
|
" # print(G.nodes[1][\"delay_comp\"])\n",
|
|
"\n",
|
|
" if Figure==True:\n",
|
|
" plt.figure(figsize=(5, 5))\n",
|
|
" pos = nx.spring_layout(G) # Layout for positioning\n",
|
|
" nx.draw(G, pos, with_labels=True, node_color=\"lightblue\", edge_color=\"black\", arrows=True, node_size=800, font_size=12)\n",
|
|
"\n",
|
|
" # Draw edge labels (flits)\n",
|
|
" edge_labels = {(u, v): f\"{d['flits']} flits\" for u, v, d in G.edges(data=True)}\n",
|
|
" nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=10)\n",
|
|
"\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" \n",
|
|
" return(G)\n",
|
|
"\n",
|
|
"# print(CreateNetworkXGraph())\n",
|
|
"# draw_networkx_graph(CreateNetworkXGraph())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 75,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# get path for the connections based on mapping\n",
|
|
"def getPaths(task_to_node):\n",
|
|
" cs_path_list=[]\n",
|
|
" for i in task_to_node:\n",
|
|
" if i%2==1 and task_to_node.get(i+1)!=None:\n",
|
|
" cs_path_list.append((task_to_node.get(i),task_to_node.get(i+1)))\n",
|
|
"\n",
|
|
" cs_data_xml,cs_task_xml,cs_map_xml,dest_address_list_int=cs_tasks(source_destination = cs_path_list, start_task=30, start_PE=16)\n",
|
|
" return(dest_address_list_int)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Node Attributes:\n",
|
|
"Node 0: {'delay_comp': 4, 'delay_mem': 1078, 'delay_send': 285, 'src': True, 'dst': False}\n",
|
|
"Node 1: {'delay_comp': 34, 'delay_mem': 38, 'delay_send': 277, 'src': False, 'dst': False}\n",
|
|
"Node 2: {'delay_comp': 67, 'delay_mem': 75, 'delay_send': 554, 'src': False, 'dst': False}\n",
|
|
"Node 3: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 4: {'delay_comp': 50, 'delay_mem': 223, 'delay_send': 205, 'src': False, 'dst': False}\n",
|
|
"Node 5: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 342, 'src': False, 'dst': False}\n",
|
|
"Node 6: {'delay_comp': 10, 'delay_mem': 42, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 7: {'delay_comp': 47, 'delay_mem': 223, 'delay_send': 194, 'src': False, 'dst': False}\n",
|
|
"Node 8: {'delay_comp': 10, 'delay_mem': 42, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 9: {'delay_comp': 26, 'delay_mem': 116, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 10: {'delay_comp': 26, 'delay_mem': 116, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 11: {'delay_comp': 47, 'delay_mem': 223, 'delay_send': 194, 'src': False, 'dst': False}\n",
|
|
"Node 12: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 388, 'src': False, 'dst': False}\n",
|
|
"Node 13: {'delay_comp': 69, 'delay_mem': 1329, 'delay_send': 95, 'src': False, 'dst': False}\n",
|
|
"Node 14: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 95, 'src': False, 'dst': False}\n",
|
|
"Node 15: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 16: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 17: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 18: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 19: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 20: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 21: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 22: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 23: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 24: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 25: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 26: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 27: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 28: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 29: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 30: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 31: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 32: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 33: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 34: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 35: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 36: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 37: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 38: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 39: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 40: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 41: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 42: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 43: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 44: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 45: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 46: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 47: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 48: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 49: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 50: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 51: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 52: {'delay_comp': 65, 'delay_mem': 5311, 'delay_send': 45, 'src': False, 'dst': False}\n",
|
|
"Node 53: {'delay_comp': 16, 'delay_mem': 296, 'delay_send': 95, 'src': False, 'dst': False}\n",
|
|
"Node 54: {'delay_comp': 80, 'delay_mem': 1550, 'delay_send': 110, 'src': False, 'dst': False}\n",
|
|
"Node 55: {'delay_comp': 25, 'delay_mem': 2067, 'delay_send': 30, 'src': False, 'dst': False}\n",
|
|
"Node 56: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 45, 'src': False, 'dst': False}\n",
|
|
"Node 57: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 119, 'src': False, 'dst': False}\n",
|
|
"Node 58: {'delay_comp': 20, 'delay_mem': 1575, 'delay_send': 45, 'src': False, 'dst': False}\n",
|
|
"Node 59: {'delay_comp': 10, 'delay_mem': 788, 'delay_send': 23, 'src': False, 'dst': False}\n",
|
|
"Node 60: {'delay_comp': 15, 'delay_mem': 1206, 'delay_send': 26, 'src': False, 'dst': False}\n",
|
|
"Node 61: {'delay_comp': 20, 'delay_mem': 1608, 'delay_send': 30, 'src': False, 'dst': False}\n",
|
|
"Node 62: {'delay_comp': 10, 'delay_mem': 788, 'delay_send': 23, 'src': False, 'dst': False}\n",
|
|
"Node 63: {'delay_comp': 13, 'delay_mem': 1034, 'delay_send': 23, 'src': False, 'dst': False}\n",
|
|
"Node 64: {'delay_comp': 15, 'delay_mem': 1206, 'delay_send': 26, 'src': False, 'dst': False}\n",
|
|
"Node 65: {'delay_comp': 18, 'delay_mem': 1406, 'delay_send': 26, 'src': False, 'dst': False}\n",
|
|
"Node 66: {'delay_comp': 20, 'delay_mem': 1608, 'delay_send': 30, 'src': False, 'dst': False}\n",
|
|
"Node 67: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 119, 'src': False, 'dst': False}\n",
|
|
"Node 68: {'delay_comp': 7, 'delay_mem': 526, 'delay_send': 15, 'src': False, 'dst': False}\n",
|
|
"Node 69: {'delay_comp': 7, 'delay_mem': 0, 'delay_send': 15, 'src': False, 'dst': True}\n",
|
|
"1078\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"G=CreateNetworkXGraph(detailed=False ,Figure=False) \n",
|
|
"\n",
|
|
"print(\"Node Attributes:\")\n",
|
|
"for node, attrs in G.nodes(data=True):\n",
|
|
" print(f\"Node {node}: {attrs}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"print(task2memory_bits(0,\"/home/sfischer/Documents/projects/wk_AI2Task/ReWrite/map_addition.xml\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 77,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# create the linear programming model based on a networkX graph and mapping for the overlapping connections\n",
|
|
"def Graph2LinProgStaticMap():\n",
|
|
" from pulp import LpMaximize, LpProblem, LpStatus, lpSum, LpVariable , LpMinimize\n",
|
|
" repetitions=10\n",
|
|
"\n",
|
|
" model = LpProblem(name='small-problem', sense=LpMinimize)\n",
|
|
"\n",
|
|
" G=CreateNetworkXGraph({0: 12, 1: 12, 2: 2, 3: 2, 4: 14, 5: 14, 6: 7, 7: 7, 8: 13, 9: 13, 10: 1, 11: 1, 12: 3, 13: 3, 14: 10, 15: 10, 16: 4, 17: 4, 18: 8, 19: 8},True ,Figure=False)\n",
|
|
" dest_address_list_int=getPaths({0: 12, 1: 12, 2: 2, 3: 2, 4: 14, 5: 14, 6: 7, 7: 7, 8: 13, 9: 13, 10: 1, 11: 1, 12: 3, 13: 3, 14: 10, 15: 10, 16: 4, 17: 4, 18: 8, 19: 8})\n",
|
|
"\n",
|
|
" for task, atr in G.nodes(data=True):\n",
|
|
"\n",
|
|
" # Define Variables\n",
|
|
" exec(f\"t{task}_0 = LpVariable(name='t{task}_0', lowBound=0)\",locals())\n",
|
|
" exec(f\"t_comp_{task} = LpVariable(name='t_comp_{task}', lowBound=0)\",locals())\n",
|
|
" exec(f\"t_send_{task} = LpVariable(name='t_send_{task}', lowBound=0)\",locals())\n",
|
|
" exec(f\"t_mem_{task} = LpVariable(name='t_mem_{task}', lowBound=0)\",locals())\n",
|
|
"\n",
|
|
" # Add static time constraints\n",
|
|
" exec(f\"model += (t_comp_{task} == {atr['delay_comp']}, 'constraint_t_comp_{task}')\",locals())\n",
|
|
" exec(f\"model += (t_send_{task} == {atr['delay_send']}, 'constraint_t_send_{task}')\",locals())\n",
|
|
" exec(f\"model += (t_mem_{task} == {atr['delay_mem']}, 'constraint_t_mem_{task}')\",locals())\n",
|
|
"\n",
|
|
" predecessors = list(G.predecessors(task))\n",
|
|
" print(predecessors)\n",
|
|
"\n",
|
|
" # set predecessor constraints\n",
|
|
" for pre in predecessors:\n",
|
|
" exec(f\"model += (t{pre}_0 + t_comp_{task} + t_send_{task} + t_mem_{task} <= t{task}_0, 'constraint_T{task}_{pre}_0')\",locals())\n",
|
|
"\n",
|
|
" # set predecessor constraints for repetition tasks\n",
|
|
" for m in range(repetitions-1):\n",
|
|
" exec(f\"t{task}_{m+1} = LpVariable(name='t{task}_{m+1}', lowBound=0)\",locals())\n",
|
|
" for pre in predecessors:\n",
|
|
" if int(pre)<int(task):\n",
|
|
" exec(f\"model += (t{pre}_{m+1} + t_comp_{task} + t_send_{task} + t_mem_{task} <= t{task}_{m+1}, 'constraint_T{task}_{pre}_{m+1}')\",locals())\n",
|
|
"\n",
|
|
" # constraint: first task in accel can only start when last task is done + last task in accel can only start when first task of next accel is done. Here hardcoded as always two tasks in accel\n",
|
|
" for Task_num in range(G.number_of_nodes()-2):\n",
|
|
" for m in range(repetitions-1):\n",
|
|
" exec(f\"model += (t{int(Task_num)+1}_{m} + t_comp_{Task_num} + t_send_{Task_num} + t_mem_{Task_num} <= t{Task_num}_{m+1}, 'constraint_Task{Task_num}_{int(Task_num)+1}_{m+1}')\",locals())\n",
|
|
"\n",
|
|
"\n",
|
|
" # constraints for multi use of links\n",
|
|
" multi_pair_list=count_consecutive_pairs_no_file(dest_address_list_int)\n",
|
|
" for multi_pair in multi_pair_list:\n",
|
|
" index_list=find_pair_indices(multi_pair,dest_address_list_int)\n",
|
|
" for index in range(len(index_list)):\n",
|
|
" prev_idx=index_list[index-1]*2+1\n",
|
|
" if index+1>len(index_list)-1:\n",
|
|
" next_idx=index_list[0]*2+1\n",
|
|
" else:\n",
|
|
" next_idx=index_list[index+1]*2+1\n",
|
|
" curr_idx=index_list[index]*2+1\n",
|
|
" last_idx=index_list[-1]*2+1\n",
|
|
"\n",
|
|
" # print(f\"index_list: {index_list} curr_idx: {curr_idx} prev_idx: {prev_idx} next_idx: {next_idx} \")\n",
|
|
"\n",
|
|
" for m in range(repetitions-1):\n",
|
|
" constraint_name = f\"constraint_MultiLink{curr_idx}_{prev_idx}_{m+1}\"\n",
|
|
" if constraint_name not in model.constraints:\n",
|
|
" if last_idx!=prev_idx:\n",
|
|
" prev_rep=m+1\n",
|
|
" else:\n",
|
|
" prev_rep=m\n",
|
|
" exec(f\"model += (t{prev_idx}_{prev_rep} + t_send_{curr_idx} <= t{curr_idx}_{m+1}, '{constraint_name}')\",locals())\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # separat constrain for first task\n",
|
|
" exec(\"model += (t_comp_0 +t_mem_0<= t0_0, 'constraint_0_0_0')\",locals())\n",
|
|
"\n",
|
|
" # objective function\n",
|
|
" exec(\"model += t18_9\",locals())\n",
|
|
"\n",
|
|
" status = model.solve()\n",
|
|
" print(f\"objective: {model.objective.value()}\")\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"def Graph2LinProgStaticMapDict():\n",
|
|
" repetitions = 10\n",
|
|
"\n",
|
|
" model = LpProblem(name='small-problem', sense=LpMinimize)\n",
|
|
"\n",
|
|
" G = CreateNetworkXGraph({0: 12, 1: 12, 2: 2, 3: 2, 4: 14, 5: 14, 6: 7, 7: 7, 8: 13, 9: 13, 10: 1, 11: 1, 12: 3, 13: 3, 14: 10, 15: 10, 16: 4, 17: 4, 18: 8, 19: 8}, detailed=False, Figure=False)\n",
|
|
" dest_address_list_int = getPaths({0: 12, 1: 12, 2: 2, 3: 2, 4: 14, 5: 14, 6: 7, 7: 7, 8: 13, 9: 13, 10: 1, 11: 1, 12: 3, 13: 3, 14: 10, 15: 10, 16: 4, 17: 4, 18: 8, 19: 8})\n",
|
|
"\n",
|
|
" task_vars = {} # Dictionary to hold variables for each task\n",
|
|
" task_constraints = [] # List to collect constraints for later addition to the model\n",
|
|
"\n",
|
|
" # Define Variables and Constraints for each task\n",
|
|
" for task, atr in G.nodes(data=True):\n",
|
|
" tR0 = LpVariable(name=f't{task}_0', lowBound=0)\n",
|
|
" t_comp = LpVariable(name=f't_comp_{task}', lowBound=0)\n",
|
|
" t_send = LpVariable(name=f't_send_{task}', lowBound=0)\n",
|
|
" t_mem = LpVariable(name=f't_mem_{task}', lowBound=0)\n",
|
|
"\n",
|
|
" task_vars[task] = {'tR0': tR0, 't_comp': t_comp, 't_send': t_send, 't_mem': t_mem}\n",
|
|
"\n",
|
|
" # Static time constraints\n",
|
|
" task_constraints.append(t_comp == atr['delay_comp'])\n",
|
|
" task_constraints.append(t_send == atr['delay_send'])\n",
|
|
" task_constraints.append(t_mem == atr['delay_mem'])\n",
|
|
"\n",
|
|
" predecessors = list(G.predecessors(task))\n",
|
|
"\n",
|
|
" # Predecessor constraints\n",
|
|
" for pre in predecessors:\n",
|
|
" task_constraints.append(task_vars[pre]['tR0'] + t_comp + t_send + t_mem <= tR0)\n",
|
|
"\n",
|
|
" # Predecessor constraints for repetition tasks\n",
|
|
" for m in range(repetitions - 1):\n",
|
|
" t_m = LpVariable(name=f't{task}_{m + 1}', lowBound=0)\n",
|
|
" task_vars[task][f'tR{m + 1}'] = t_m\n",
|
|
"\n",
|
|
" for pre in predecessors:\n",
|
|
" if int(pre) < int(task):\n",
|
|
" task_constraints.append(task_vars[pre][f'tR{m + 1}'] + t_comp + t_send + t_mem <= t_m)\n",
|
|
"\n",
|
|
" # Constraint for the last task in one acceleration block and the first task in the next block\n",
|
|
" for Task_num in range(G.number_of_nodes() - 2):\n",
|
|
" for m in range(repetitions - 1):\n",
|
|
" task_constraints.append(task_vars[Task_num + 1][f'tR{m}'] + task_vars[Task_num]['t_comp'] + task_vars[Task_num]['t_send'] + task_vars[Task_num]['t_mem'] <= task_vars[Task_num][f'tR{m + 1}'])\n",
|
|
"\n",
|
|
" # Multi-use of links constraints\n",
|
|
" multi_pair_list = count_consecutive_pairs_no_file(dest_address_list_int)\n",
|
|
" for multi_pair in multi_pair_list:\n",
|
|
" index_list = find_pair_indices(multi_pair, dest_address_list_int)\n",
|
|
" for index in range(len(index_list)):\n",
|
|
" prev_idx = index_list[index - 1] * 2 + 1\n",
|
|
" if index + 1 > len(index_list) - 1:\n",
|
|
" next_idx = index_list[0] * 2 + 1\n",
|
|
" else:\n",
|
|
" next_idx = index_list[index + 1] * 2 + 1\n",
|
|
" curr_idx = index_list[index] * 2 + 1\n",
|
|
" last_idx = index_list[-1] * 2 + 1\n",
|
|
"\n",
|
|
" for m in range(repetitions - 1):\n",
|
|
" constraint_name = f\"constraint_MultiLink{curr_idx}_{prev_idx}_{m + 1}\"\n",
|
|
" if constraint_name not in model.constraints:\n",
|
|
" prev_rep = m + 1 if last_idx != prev_idx else m\n",
|
|
" task_constraints.append(task_vars[prev_idx][f'tR{prev_rep}'] + task_vars[curr_idx]['t_send'] <= task_vars[curr_idx][f'tR{m + 1}'])\n",
|
|
"\n",
|
|
" # Add the constraints to the model\n",
|
|
" for constraint in task_constraints:\n",
|
|
" model += constraint\n",
|
|
"\n",
|
|
" # Constraint for first task\n",
|
|
" model += task_vars[0]['t_comp'] + task_vars[0]['t_mem'] <= task_vars[0]['tR0']\n",
|
|
"\n",
|
|
" # Objective function\n",
|
|
" model += task_vars[18]['tR9']\n",
|
|
"\n",
|
|
" # Solve the problem\n",
|
|
" status = model.solve()\n",
|
|
" print(f\"objective: {model.objective.value()}\")\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"def CreateNetworkXGraphManuelLinear(Figure=False):\n",
|
|
"\n",
|
|
" G = nx.DiGraph()\n",
|
|
"\n",
|
|
" # number of parallel paths\n",
|
|
" # length of parallel paths\n",
|
|
"\n",
|
|
" ###########################################################################################################################\n",
|
|
" # create NetworkX Graph\n",
|
|
" ###########################################################################################################################\n",
|
|
"\n",
|
|
" time_per_flit=0.649 # for 1 hop\n",
|
|
" flit_number=0\n",
|
|
" repetitions=10\n",
|
|
" max_task_num=16\n",
|
|
"\n",
|
|
"\n",
|
|
" # simplified memory delay without taking distance to memory into account \n",
|
|
" mem_delay=100\n",
|
|
" flit_delay=10\n",
|
|
" delay_comp=100\n",
|
|
"\n",
|
|
" delay_send=flit_delay\n",
|
|
" delay_mem=mem_delay\n",
|
|
"\n",
|
|
"\n",
|
|
" for i in range(max_task_num):\n",
|
|
" G.add_node(i,delay_comp=delay_comp,delay_mem=delay_mem,delay_send=delay_send)\n",
|
|
" if i >0:\n",
|
|
" G.add_edge(i-1,i)\n",
|
|
" G.add_edge(i-1,i,flits=flit_delay)\n",
|
|
"\n",
|
|
"\n",
|
|
" # for node in G.nodes:\n",
|
|
" # print(f\"Node {node} -> Edges: {list(G.predecessors(node))}, node delay_comp ={G.nodes[node][\"delay_comp\"]}\")\n",
|
|
"\n",
|
|
" # print(G.nodes[1][\"delay_comp\"])\n",
|
|
"\n",
|
|
" if Figure==True:\n",
|
|
" plt.figure(figsize=(5, 5))\n",
|
|
" pos = nx.spring_layout(G) # Layout for positioning\n",
|
|
" nx.draw(G, pos, with_labels=True, node_color=\"lightblue\", edge_color=\"black\", arrows=True, node_size=800, font_size=12)\n",
|
|
"\n",
|
|
" # Draw edge labels (flits)\n",
|
|
" edge_labels = {(u, v): f\"{d['flits']} flits\" for u, v, d in G.edges(data=True)}\n",
|
|
" nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=10)\n",
|
|
"\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" return(G)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 80,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"\n",
|
|
"from LinProg_lib import *\n",
|
|
"# Initialize graph\n",
|
|
"G = CreateNetworkXGraphManuelParallel()\n",
|
|
"# elements = graph_to_cytoscape(G)\n",
|
|
"\n",
|
|
"# # Dash App\n",
|
|
"# app = dash.Dash(__name__)\n",
|
|
"\n",
|
|
"# app.layout = html.Div([\n",
|
|
"# html.H3(\"Drag and Move Nodes in Real Time\"),\n",
|
|
"# cyto.Cytoscape(\n",
|
|
"# id=\"cytoscape-graph\",\n",
|
|
"# elements=elements,\n",
|
|
"# layout={\"name\": \"preset\"}, # Use preset positions\n",
|
|
"# style={\"width\": \"100%\", \"height\": \"600px\"},\n",
|
|
"# stylesheet=[\n",
|
|
"# {\"selector\": \"node\", \"style\": {\n",
|
|
"# \"content\": \"data(label)\",\n",
|
|
"# \"background-color\": \"lightblue\",\n",
|
|
"# \"width\": \"75px\", # Increase node size\n",
|
|
"# \"height\": \"75px\", # Increase node size\n",
|
|
"# \"font-size\": \"16px\", # Make labels larger\n",
|
|
"# \"text-valign\": \"center\",\n",
|
|
"# }},\n",
|
|
"# {\"selector\": \"edge\", \"style\": {\n",
|
|
"# \"curve-style\": \"bezier\",\n",
|
|
"# \"target-arrow-shape\": \"triangle\",\n",
|
|
"# \"label\": \"data(label)\",\n",
|
|
"# \"font-size\": \"12px\",\n",
|
|
"# \"color\": \"white\", # Make edge labels white\n",
|
|
"# }}\n",
|
|
"# ],\n",
|
|
"# ),\n",
|
|
"# ])\n",
|
|
"\n",
|
|
"# if __name__ == \"__main__\":\n",
|
|
"# app.run(debug=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 81,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"<Figure size 500x500 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
|
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"<networkx.classes.digraph.DiGraph at 0x74f785263160>"
|
|
]
|
|
},
|
|
"execution_count": 81,
|
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"metadata": {},
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"output_type": "execute_result"
|
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}
|
|
],
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"source": [
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"\n",
|
|
"import random\n",
|
|
"def CreateNetworkXGraphRandom(Figure=False, nodes=5):\n",
|
|
" G = nx.DiGraph() # Directed graph\n",
|
|
" G.add_nodes_from(range(nodes)) # Add nodes 0 to n-1\n",
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"\n",
|
|
" # Add random delays as node attributes\n",
|
|
" for node in range(nodes):\n",
|
|
" delay_comp = random.randint(1, 100) # Random integer for delay_comp\n",
|
|
" delay_mem = random.randint(1, 10) # Random integer for delay_mem\n",
|
|
" delay_send = random.randint(1, 10) # Random integer for delay_send\n",
|
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" \n",
|
|
" # Add node with attributes\n",
|
|
" G.nodes[node]['delay_comp'] = delay_comp\n",
|
|
" G.nodes[node]['delay_mem'] = delay_mem\n",
|
|
" G.nodes[node]['delay_send'] = delay_send\n",
|
|
"\n",
|
|
" # Ensure source node (node 0) has no incoming edges\n",
|
|
" # and destination node (node nodes-1) has no outgoing edges\n",
|
|
" # Source connects to at least one other node\n",
|
|
" first_hop = random.randint(1, nodes-2)\n",
|
|
" G.add_edge(0, first_hop, flits=G.nodes[0]['delay_send'])\n",
|
|
"\n",
|
|
" # Intermediate random edges, avoiding self-loops, bidirectional edges, and cycles\n",
|
|
" for node in range(1, nodes-1): # Exclude source and destination\n",
|
|
" target = random.randint(1, nodes-1)\n",
|
|
" while target == node or G.has_edge(target, node) or G.has_edge(node, target): # Avoid self-loops and bidirectional edges\n",
|
|
" target = random.randint(1, nodes-1)\n",
|
|
" \n",
|
|
" # Temporarily add the edge to check for cycles\n",
|
|
" G.add_edge(node, target, flits=G.nodes[node]['delay_send'])\n",
|
|
" \n",
|
|
" # Check if adding this edge creates a cycle\n",
|
|
" if nx.is_directed_acyclic_graph(G) == False: # Checks if the graph has a cycle\n",
|
|
" G.remove_edge(node, target) # Remove the edge if it creates a cycle\n",
|
|
" else:\n",
|
|
" # Keep the edge if no cycle is detected\n",
|
|
" pass\n",
|
|
"\n",
|
|
" # Ensure at least one path to the destination\n",
|
|
" prev_node = random.randint(1, nodes-2)\n",
|
|
" G.add_edge(prev_node, nodes-1, flits=G.nodes[prev_node]['delay_send'])\n",
|
|
"\n",
|
|
" # Ensure destination node (nodes-1) has no outgoing edges\n",
|
|
" G.remove_edges_from(list(G.out_edges(nodes-1)))\n",
|
|
"\n",
|
|
"\n",
|
|
" if Figure==True:\n",
|
|
" plt.figure(figsize=(5, 5))\n",
|
|
" pos = nx.spring_layout(G) # Layout for positioning\n",
|
|
" nx.draw(G, pos, with_labels=True, node_color=\"lightblue\", edge_color=\"black\", arrows=True, node_size=800, font_size=12)\n",
|
|
"\n",
|
|
" # Draw edge labels (flits)\n",
|
|
" edge_labels = {(u, v): f\"{d['flits']}\" for u, v, d in G.edges(data=True)}\n",
|
|
" nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=10)\n",
|
|
"\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" return(G)\n",
|
|
"\n",
|
|
"CreateNetworkXGraphRandom(True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 82,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Node Attributes:\n",
|
|
"Node 0: {'delay_comp': 4, 'delay_mem': 1078, 'delay_send': 285, 'src': True, 'dst': False}\n",
|
|
"Node 1: {'delay_comp': 34, 'delay_mem': 38, 'delay_send': 277, 'src': False, 'dst': False}\n",
|
|
"Node 2: {'delay_comp': 67, 'delay_mem': 75, 'delay_send': 554, 'src': False, 'dst': False}\n",
|
|
"Node 3: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 4: {'delay_comp': 50, 'delay_mem': 223, 'delay_send': 205, 'src': False, 'dst': False}\n",
|
|
"Node 5: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 342, 'src': False, 'dst': False}\n",
|
|
"Node 6: {'delay_comp': 10, 'delay_mem': 42, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 7: {'delay_comp': 47, 'delay_mem': 223, 'delay_send': 194, 'src': False, 'dst': False}\n",
|
|
"Node 8: {'delay_comp': 10, 'delay_mem': 42, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 9: {'delay_comp': 26, 'delay_mem': 116, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 10: {'delay_comp': 26, 'delay_mem': 116, 'delay_send': 137, 'src': False, 'dst': False}\n",
|
|
"Node 11: {'delay_comp': 47, 'delay_mem': 223, 'delay_send': 194, 'src': False, 'dst': False}\n",
|
|
"Node 12: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 388, 'src': False, 'dst': False}\n",
|
|
"Node 13: {'delay_comp': 69, 'delay_mem': 1329, 'delay_send': 95, 'src': False, 'dst': False}\n",
|
|
"Node 14: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 95, 'src': False, 'dst': False}\n",
|
|
"Node 15: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 16: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 17: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 18: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 19: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 20: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 21: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 22: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 23: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 24: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 25: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 26: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 27: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 28: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 29: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 30: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 31: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 32: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 33: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 34: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 35: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 36: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 37: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 38: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 39: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 40: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 41: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 42: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 43: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 44: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 45: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 46: {'delay_comp': 6, 'delay_mem': 100, 'delay_send': 32, 'src': False, 'dst': False}\n",
|
|
"Node 47: {'delay_comp': 12, 'delay_mem': 223, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 48: {'delay_comp': 18, 'delay_mem': 333, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 49: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 50: {'delay_comp': 8, 'delay_mem': 149, 'delay_send': 48, 'src': False, 'dst': False}\n",
|
|
"Node 51: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 189, 'src': False, 'dst': False}\n",
|
|
"Node 52: {'delay_comp': 65, 'delay_mem': 5311, 'delay_send': 45, 'src': False, 'dst': False}\n",
|
|
"Node 53: {'delay_comp': 16, 'delay_mem': 296, 'delay_send': 95, 'src': False, 'dst': False}\n",
|
|
"Node 54: {'delay_comp': 80, 'delay_mem': 1550, 'delay_send': 110, 'src': False, 'dst': False}\n",
|
|
"Node 55: {'delay_comp': 25, 'delay_mem': 2067, 'delay_send': 30, 'src': False, 'dst': False}\n",
|
|
"Node 56: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 45, 'src': False, 'dst': False}\n",
|
|
"Node 57: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 119, 'src': False, 'dst': False}\n",
|
|
"Node 58: {'delay_comp': 20, 'delay_mem': 1575, 'delay_send': 45, 'src': False, 'dst': False}\n",
|
|
"Node 59: {'delay_comp': 10, 'delay_mem': 788, 'delay_send': 23, 'src': False, 'dst': False}\n",
|
|
"Node 60: {'delay_comp': 15, 'delay_mem': 1206, 'delay_send': 26, 'src': False, 'dst': False}\n",
|
|
"Node 61: {'delay_comp': 20, 'delay_mem': 1608, 'delay_send': 30, 'src': False, 'dst': False}\n",
|
|
"Node 62: {'delay_comp': 10, 'delay_mem': 788, 'delay_send': 23, 'src': False, 'dst': False}\n",
|
|
"Node 63: {'delay_comp': 13, 'delay_mem': 1034, 'delay_send': 23, 'src': False, 'dst': False}\n",
|
|
"Node 64: {'delay_comp': 15, 'delay_mem': 1206, 'delay_send': 26, 'src': False, 'dst': False}\n",
|
|
"Node 65: {'delay_comp': 18, 'delay_mem': 1406, 'delay_send': 26, 'src': False, 'dst': False}\n",
|
|
"Node 66: {'delay_comp': 20, 'delay_mem': 1608, 'delay_send': 30, 'src': False, 'dst': False}\n",
|
|
"Node 67: {'delay_comp': 1, 'delay_mem': 0, 'delay_send': 119, 'src': False, 'dst': False}\n",
|
|
"Node 68: {'delay_comp': 7, 'delay_mem': 526, 'delay_send': 15, 'src': False, 'dst': False}\n",
|
|
"Node 69: {'delay_comp': 7, 'delay_mem': 0, 'delay_send': 15, 'src': False, 'dst': True}\n",
|
|
"\n",
|
|
"Edge Attributes:\n",
|
|
"Edge from 0 to 1: {'flits': 285}\n",
|
|
"Edge from 1 to 2: {'flits': 277}\n",
|
|
"Edge from 2 to 3: {'flits': 554}\n",
|
|
"Edge from 2 to 4: {'flits': 554}\n",
|
|
"Edge from 3 to 5: {'flits': 137}\n",
|
|
"Edge from 4 to 5: {'flits': 205}\n",
|
|
"Edge from 5 to 6: {'flits': 342}\n",
|
|
"Edge from 5 to 8: {'flits': 342}\n",
|
|
"Edge from 6 to 7: {'flits': 137}\n",
|
|
"Edge from 7 to 12: {'flits': 194}\n",
|
|
"Edge from 8 to 9: {'flits': 137}\n",
|
|
"Edge from 9 to 10: {'flits': 137}\n",
|
|
"Edge from 10 to 11: {'flits': 137}\n",
|
|
"Edge from 11 to 12: {'flits': 194}\n",
|
|
"Edge from 12 to 13: {'flits': 388}\n",
|
|
"Edge from 12 to 14: {'flits': 388}\n",
|
|
"Edge from 13 to 15: {'flits': 95}\n",
|
|
"Edge from 14 to 15: {'flits': 95}\n",
|
|
"Edge from 15 to 16: {'flits': 189}\n",
|
|
"Edge from 15 to 17: {'flits': 189}\n",
|
|
"Edge from 15 to 19: {'flits': 189}\n",
|
|
"Edge from 15 to 22: {'flits': 189}\n",
|
|
"Edge from 16 to 24: {'flits': 48}\n",
|
|
"Edge from 17 to 18: {'flits': 32}\n",
|
|
"Edge from 18 to 24: {'flits': 48}\n",
|
|
"Edge from 19 to 20: {'flits': 32}\n",
|
|
"Edge from 20 to 21: {'flits': 48}\n",
|
|
"Edge from 21 to 24: {'flits': 48}\n",
|
|
"Edge from 22 to 23: {'flits': 189}\n",
|
|
"Edge from 23 to 24: {'flits': 48}\n",
|
|
"Edge from 24 to 25: {'flits': 189}\n",
|
|
"Edge from 24 to 26: {'flits': 189}\n",
|
|
"Edge from 24 to 28: {'flits': 189}\n",
|
|
"Edge from 24 to 31: {'flits': 189}\n",
|
|
"Edge from 25 to 33: {'flits': 48}\n",
|
|
"Edge from 26 to 27: {'flits': 32}\n",
|
|
"Edge from 27 to 33: {'flits': 48}\n",
|
|
"Edge from 28 to 29: {'flits': 32}\n",
|
|
"Edge from 29 to 30: {'flits': 48}\n",
|
|
"Edge from 30 to 33: {'flits': 48}\n",
|
|
"Edge from 31 to 32: {'flits': 189}\n",
|
|
"Edge from 32 to 33: {'flits': 48}\n",
|
|
"Edge from 33 to 34: {'flits': 189}\n",
|
|
"Edge from 33 to 35: {'flits': 189}\n",
|
|
"Edge from 33 to 37: {'flits': 189}\n",
|
|
"Edge from 33 to 40: {'flits': 189}\n",
|
|
"Edge from 34 to 42: {'flits': 48}\n",
|
|
"Edge from 35 to 36: {'flits': 32}\n",
|
|
"Edge from 36 to 42: {'flits': 48}\n",
|
|
"Edge from 37 to 38: {'flits': 32}\n",
|
|
"Edge from 38 to 39: {'flits': 48}\n",
|
|
"Edge from 39 to 42: {'flits': 48}\n",
|
|
"Edge from 40 to 41: {'flits': 189}\n",
|
|
"Edge from 41 to 42: {'flits': 48}\n",
|
|
"Edge from 42 to 43: {'flits': 189}\n",
|
|
"Edge from 42 to 44: {'flits': 189}\n",
|
|
"Edge from 42 to 46: {'flits': 189}\n",
|
|
"Edge from 42 to 49: {'flits': 189}\n",
|
|
"Edge from 43 to 51: {'flits': 48}\n",
|
|
"Edge from 44 to 45: {'flits': 32}\n",
|
|
"Edge from 45 to 51: {'flits': 48}\n",
|
|
"Edge from 46 to 47: {'flits': 32}\n",
|
|
"Edge from 47 to 48: {'flits': 48}\n",
|
|
"Edge from 48 to 51: {'flits': 48}\n",
|
|
"Edge from 49 to 50: {'flits': 189}\n",
|
|
"Edge from 50 to 51: {'flits': 48}\n",
|
|
"Edge from 51 to 52: {'flits': 189}\n",
|
|
"Edge from 51 to 53: {'flits': 189}\n",
|
|
"Edge from 51 to 56: {'flits': 189}\n",
|
|
"Edge from 52 to 57: {'flits': 45}\n",
|
|
"Edge from 53 to 54: {'flits': 95}\n",
|
|
"Edge from 54 to 55: {'flits': 110}\n",
|
|
"Edge from 55 to 57: {'flits': 30}\n",
|
|
"Edge from 56 to 57: {'flits': 45}\n",
|
|
"Edge from 57 to 58: {'flits': 119}\n",
|
|
"Edge from 57 to 59: {'flits': 119}\n",
|
|
"Edge from 57 to 62: {'flits': 119}\n",
|
|
"Edge from 57 to 67: {'flits': 119}\n",
|
|
"Edge from 58 to 69: {'flits': 45}\n",
|
|
"Edge from 59 to 60: {'flits': 23}\n",
|
|
"Edge from 60 to 61: {'flits': 26}\n",
|
|
"Edge from 61 to 69: {'flits': 30}\n",
|
|
"Edge from 62 to 63: {'flits': 23}\n",
|
|
"Edge from 63 to 64: {'flits': 23}\n",
|
|
"Edge from 64 to 65: {'flits': 26}\n",
|
|
"Edge from 65 to 66: {'flits': 26}\n",
|
|
"Edge from 66 to 69: {'flits': 30}\n",
|
|
"Edge from 67 to 68: {'flits': 119}\n",
|
|
"Edge from 68 to 69: {'flits': 15}\n",
|
|
"Node Attributes:\n",
|
|
"Node 0: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': True, 'dst': False}\n",
|
|
"Node 1: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 2: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 3: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 4: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 5: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 6: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 7: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 8: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 9: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 10: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 11: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 12: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 13: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 14: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': False}\n",
|
|
"Node 15: {'delay_comp': 100, 'delay_mem': 10, 'delay_send': 10, 'src': False, 'dst': True}\n",
|
|
"\n",
|
|
"Edge Attributes:\n",
|
|
"Edge from 0 to 1: {'flits': 10}\n",
|
|
"Edge from 1 to 2: {'flits': 10}\n",
|
|
"Edge from 1 to 5: {'flits': 10}\n",
|
|
"Edge from 1 to 8: {'flits': 10}\n",
|
|
"Edge from 1 to 11: {'flits': 10}\n",
|
|
"Edge from 2 to 3: {'flits': 10}\n",
|
|
"Edge from 3 to 4: {'flits': 10}\n",
|
|
"Edge from 4 to 14: {'flits': 10}\n",
|
|
"Edge from 5 to 6: {'flits': 10}\n",
|
|
"Edge from 6 to 7: {'flits': 10}\n",
|
|
"Edge from 7 to 14: {'flits': 10}\n",
|
|
"Edge from 8 to 9: {'flits': 10}\n",
|
|
"Edge from 9 to 10: {'flits': 10}\n",
|
|
"Edge from 10 to 14: {'flits': 10}\n",
|
|
"Edge from 11 to 12: {'flits': 10}\n",
|
|
"Edge from 12 to 13: {'flits': 10}\n",
|
|
"Edge from 13 to 14: {'flits': 10}\n",
|
|
"Edge from 14 to 15: {'flits': 10}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"G=CreateNetworkXGraph()\n",
|
|
"# Print all attributes of the nodes\n",
|
|
"print(\"Node Attributes:\")\n",
|
|
"for node, attrs in G.nodes(data=True):\n",
|
|
" print(f\"Node {node}: {attrs}\")\n",
|
|
"\n",
|
|
"# Print all attributes of the edges\n",
|
|
"print(\"\\nEdge Attributes:\")\n",
|
|
"for u, v, attrs in G.edges(data=True):\n",
|
|
" print(f\"Edge from {u} to {v}: {attrs}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"G=CreateNetworkXGraphManuelParallel()\n",
|
|
"# Print all attributes of the nodes\n",
|
|
"print(\"Node Attributes:\")\n",
|
|
"for node, attrs in G.nodes(data=True):\n",
|
|
" print(f\"Node {node}: {attrs}\")\n",
|
|
"\n",
|
|
"# Print all attributes of the edges\n",
|
|
"print(\"\\nEdge Attributes:\")\n",
|
|
"for u, v, attrs in G.edges(data=True):\n",
|
|
" print(f\"Edge from {u} to {v}: {attrs}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 83,
|
|
"metadata": {
|
|
"tags": [
|
|
"parameters"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"param1 = 1\n",
|
|
"param2 = 0.1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Edge from 0 to 1: {'flits': 285}\n",
|
|
"Edge from 1 to 2: {'flits': 277}\n",
|
|
"Edge from 2 to 3: {'flits': 554}\n",
|
|
"Edge from 2 to 4: {'flits': 554}\n",
|
|
"Edge from 3 to 5: {'flits': 137}\n",
|
|
"Edge from 4 to 5: {'flits': 205}\n",
|
|
"Edge from 5 to 6: {'flits': 342}\n",
|
|
"Edge from 5 to 8: {'flits': 342}\n",
|
|
"Edge from 6 to 7: {'flits': 137}\n",
|
|
"Edge from 7 to 12: {'flits': 194}\n",
|
|
"Edge from 8 to 9: {'flits': 137}\n",
|
|
"Edge from 9 to 10: {'flits': 137}\n",
|
|
"Edge from 10 to 11: {'flits': 137}\n",
|
|
"Edge from 11 to 12: {'flits': 194}\n",
|
|
"Edge from 12 to 13: {'flits': 388}\n",
|
|
"Edge from 12 to 14: {'flits': 388}\n",
|
|
"Edge from 13 to 15: {'flits': 95}\n",
|
|
"Edge from 14 to 15: {'flits': 95}\n",
|
|
"Edge from 15 to 16: {'flits': 189}\n",
|
|
"Edge from 15 to 17: {'flits': 189}\n",
|
|
"Edge from 15 to 19: {'flits': 189}\n",
|
|
"Edge from 15 to 22: {'flits': 189}\n",
|
|
"Edge from 16 to 24: {'flits': 48}\n",
|
|
"Edge from 17 to 18: {'flits': 32}\n",
|
|
"Edge from 18 to 24: {'flits': 48}\n",
|
|
"Edge from 19 to 20: {'flits': 32}\n",
|
|
"Edge from 20 to 21: {'flits': 48}\n",
|
|
"Edge from 21 to 24: {'flits': 48}\n",
|
|
"Edge from 22 to 23: {'flits': 189}\n",
|
|
"Edge from 23 to 24: {'flits': 48}\n",
|
|
"Edge from 24 to 25: {'flits': 189}\n",
|
|
"Edge from 24 to 26: {'flits': 189}\n",
|
|
"Edge from 24 to 28: {'flits': 189}\n",
|
|
"Edge from 24 to 31: {'flits': 189}\n",
|
|
"Edge from 25 to 33: {'flits': 48}\n",
|
|
"Edge from 26 to 27: {'flits': 32}\n",
|
|
"Edge from 27 to 33: {'flits': 48}\n",
|
|
"Edge from 28 to 29: {'flits': 32}\n",
|
|
"Edge from 29 to 30: {'flits': 48}\n",
|
|
"Edge from 30 to 33: {'flits': 48}\n",
|
|
"Edge from 31 to 32: {'flits': 189}\n",
|
|
"Edge from 32 to 33: {'flits': 48}\n",
|
|
"Edge from 33 to 34: {'flits': 189}\n",
|
|
"Edge from 33 to 35: {'flits': 189}\n",
|
|
"Edge from 33 to 37: {'flits': 189}\n",
|
|
"Edge from 33 to 40: {'flits': 189}\n",
|
|
"Edge from 34 to 42: {'flits': 48}\n",
|
|
"Edge from 35 to 36: {'flits': 32}\n",
|
|
"Edge from 36 to 42: {'flits': 48}\n",
|
|
"Edge from 37 to 38: {'flits': 32}\n",
|
|
"Edge from 38 to 39: {'flits': 48}\n",
|
|
"Edge from 39 to 42: {'flits': 48}\n",
|
|
"Edge from 40 to 41: {'flits': 189}\n",
|
|
"Edge from 41 to 42: {'flits': 48}\n",
|
|
"Edge from 42 to 43: {'flits': 189}\n",
|
|
"Edge from 42 to 44: {'flits': 189}\n",
|
|
"Edge from 42 to 46: {'flits': 189}\n",
|
|
"Edge from 42 to 49: {'flits': 189}\n",
|
|
"Edge from 43 to 51: {'flits': 48}\n",
|
|
"Edge from 44 to 45: {'flits': 32}\n",
|
|
"Edge from 45 to 51: {'flits': 48}\n",
|
|
"Edge from 46 to 47: {'flits': 32}\n",
|
|
"Edge from 47 to 48: {'flits': 48}\n",
|
|
"Edge from 48 to 51: {'flits': 48}\n",
|
|
"Edge from 49 to 50: {'flits': 189}\n",
|
|
"Edge from 50 to 51: {'flits': 48}\n",
|
|
"Edge from 51 to 52: {'flits': 189}\n",
|
|
"Edge from 51 to 53: {'flits': 189}\n",
|
|
"Edge from 51 to 56: {'flits': 189}\n",
|
|
"Edge from 52 to 57: {'flits': 45}\n",
|
|
"Edge from 53 to 54: {'flits': 95}\n",
|
|
"Edge from 54 to 55: {'flits': 110}\n",
|
|
"Edge from 55 to 57: {'flits': 30}\n",
|
|
"Edge from 56 to 57: {'flits': 45}\n",
|
|
"Edge from 57 to 58: {'flits': 119}\n",
|
|
"Edge from 57 to 59: {'flits': 119}\n",
|
|
"Edge from 57 to 62: {'flits': 119}\n",
|
|
"Edge from 57 to 67: {'flits': 119}\n",
|
|
"Edge from 58 to 69: {'flits': 45}\n",
|
|
"Edge from 59 to 60: {'flits': 23}\n",
|
|
"Edge from 60 to 61: {'flits': 26}\n",
|
|
"Edge from 61 to 69: {'flits': 30}\n",
|
|
"Edge from 62 to 63: {'flits': 23}\n",
|
|
"Edge from 63 to 64: {'flits': 23}\n",
|
|
"Edge from 64 to 65: {'flits': 26}\n",
|
|
"Edge from 65 to 66: {'flits': 26}\n",
|
|
"Edge from 66 to 69: {'flits': 30}\n",
|
|
"Edge from 67 to 68: {'flits': 119}\n",
|
|
"Edge from 68 to 69: {'flits': 15}\n",
|
|
"Edge from 0 to 1: {'flits': 285}\n",
|
|
"Edge from 1 to 2: {'flits': 277}\n",
|
|
"Edge from 2 to 3: {'flits': 554}\n",
|
|
"Edge from 2 to 4: {'flits': 554}\n",
|
|
"Edge from 3 to 5: {'flits': 137}\n",
|
|
"Edge from 4 to 5: {'flits': 205}\n",
|
|
"Edge from 5 to 6: {'flits': 342}\n",
|
|
"Edge from 5 to 8: {'flits': 342}\n",
|
|
"Edge from 6 to 7: {'flits': 137}\n",
|
|
"Edge from 7 to 12: {'flits': 194}\n",
|
|
"Edge from 8 to 9: {'flits': 137}\n",
|
|
"Edge from 9 to 10: {'flits': 137}\n",
|
|
"Edge from 10 to 11: {'flits': 137}\n",
|
|
"Edge from 11 to 12: {'flits': 194}\n",
|
|
"Edge from 12 to 13: {'flits': 388}\n",
|
|
"Edge from 12 to 14: {'flits': 388}\n",
|
|
"Edge from 13 to 15: {'flits': 95}\n",
|
|
"Edge from 14 to 15: {'flits': 95}\n",
|
|
"Edge from 15 to 16: {'flits': 189}\n",
|
|
"Edge from 15 to 17: {'flits': 189}\n",
|
|
"Edge from 15 to 19: {'flits': 189}\n",
|
|
"Edge from 15 to 22: {'flits': 189}\n",
|
|
"Edge from 16 to 24: {'flits': 48}\n",
|
|
"Edge from 17 to 18: {'flits': 32}\n",
|
|
"Edge from 18 to 24: {'flits': 48}\n",
|
|
"Edge from 19 to 20: {'flits': 32}\n",
|
|
"Edge from 20 to 21: {'flits': 48}\n",
|
|
"Edge from 21 to 24: {'flits': 48}\n",
|
|
"Edge from 22 to 23: {'flits': 189}\n",
|
|
"Edge from 23 to 24: {'flits': 48}\n",
|
|
"Edge from 24 to 25: {'flits': 189}\n",
|
|
"Edge from 24 to 26: {'flits': 189}\n",
|
|
"Edge from 24 to 28: {'flits': 189}\n",
|
|
"Edge from 24 to 31: {'flits': 189}\n",
|
|
"Edge from 25 to 33: {'flits': 48}\n",
|
|
"Edge from 26 to 27: {'flits': 32}\n",
|
|
"Edge from 27 to 33: {'flits': 48}\n",
|
|
"Edge from 28 to 29: {'flits': 32}\n",
|
|
"Edge from 29 to 30: {'flits': 48}\n",
|
|
"Edge from 30 to 33: {'flits': 48}\n",
|
|
"Edge from 31 to 32: {'flits': 189}\n",
|
|
"Edge from 32 to 33: {'flits': 48}\n",
|
|
"Edge from 33 to 34: {'flits': 189}\n",
|
|
"Edge from 33 to 35: {'flits': 189}\n",
|
|
"Edge from 33 to 37: {'flits': 189}\n",
|
|
"Edge from 33 to 40: {'flits': 189}\n",
|
|
"Edge from 34 to 42: {'flits': 48}\n",
|
|
"Edge from 35 to 36: {'flits': 32}\n",
|
|
"Edge from 36 to 42: {'flits': 48}\n",
|
|
"Edge from 37 to 38: {'flits': 32}\n",
|
|
"Edge from 38 to 39: {'flits': 48}\n",
|
|
"Edge from 39 to 42: {'flits': 48}\n",
|
|
"Edge from 40 to 41: {'flits': 189}\n",
|
|
"Edge from 41 to 42: {'flits': 48}\n",
|
|
"Edge from 42 to 43: {'flits': 189}\n",
|
|
"Edge from 42 to 44: {'flits': 189}\n",
|
|
"Edge from 42 to 46: {'flits': 189}\n",
|
|
"Edge from 42 to 49: {'flits': 189}\n",
|
|
"Edge from 43 to 51: {'flits': 48}\n",
|
|
"Edge from 44 to 45: {'flits': 32}\n",
|
|
"Edge from 45 to 51: {'flits': 48}\n",
|
|
"Edge from 46 to 47: {'flits': 32}\n",
|
|
"Edge from 47 to 48: {'flits': 48}\n",
|
|
"Edge from 48 to 51: {'flits': 48}\n",
|
|
"Edge from 49 to 50: {'flits': 189}\n",
|
|
"Edge from 50 to 51: {'flits': 48}\n",
|
|
"Edge from 51 to 52: {'flits': 189}\n",
|
|
"Edge from 51 to 53: {'flits': 189}\n",
|
|
"Edge from 51 to 56: {'flits': 189}\n",
|
|
"Edge from 52 to 57: {'flits': 45}\n",
|
|
"Edge from 53 to 54: {'flits': 95}\n",
|
|
"Edge from 54 to 55: {'flits': 110}\n",
|
|
"Edge from 55 to 57: {'flits': 30}\n",
|
|
"Edge from 56 to 57: {'flits': 45}\n",
|
|
"Edge from 57 to 58: {'flits': 119}\n",
|
|
"Edge from 57 to 59: {'flits': 119}\n",
|
|
"Edge from 57 to 62: {'flits': 119}\n",
|
|
"Edge from 57 to 67: {'flits': 119}\n",
|
|
"Edge from 58 to 69: {'flits': 45}\n",
|
|
"Edge from 59 to 60: {'flits': 23}\n",
|
|
"Edge from 60 to 61: {'flits': 26}\n",
|
|
"Edge from 61 to 69: {'flits': 30}\n",
|
|
"Edge from 62 to 63: {'flits': 23}\n",
|
|
"Edge from 63 to 64: {'flits': 23}\n",
|
|
"Edge from 64 to 65: {'flits': 26}\n",
|
|
"Edge from 65 to 66: {'flits': 26}\n",
|
|
"Edge from 66 to 69: {'flits': 30}\n",
|
|
"Edge from 67 to 68: {'flits': 119}\n",
|
|
"Edge from 68 to 69: {'flits': 15}\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
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"<Figure size 3200x1400 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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|
"data": {
|
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"image/png": 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2m9UhhxyiAw88UJL05z//WW+88YbC4bBeeOEFHXHEETrssMNq9m0YhuLxuOLx+Db303EcWZbVMCxcLxjc17p0Ot1wm8qAc3+GOkxcWk6gGAAAAABQj+E0e1ssAAAAAACA3ns8cE9PT1X13UQiUTWfSqWqtgkEAorFYgqFQvL5fOWAbbFYVC6XUyqVUnd3d1X4d/Lkybrgggvq9uHaa6/Vt771LV1wwQWaNm2aTjnlFH3pS1/SM888I9M0NW3aNP3nf/6n5s6dq1Qqpfvvv1+5XE477rijdtppJ4VCoaF7gwAADSWTSXm93nLY9q677tJvf/tbrV27Vtttt50uvvjicnX2ZjiOo1Qq1ednUk9PT1V1SJ/PVxX8rTd1u6mRAWD8yWQy5bF3Op3W3Llzlc1m9fTTT5dvxDv00EPV3t4uSbrnnnt0zz33KJlMatasWbrooovG1Y1y/QWKtyZsXG9Z5Q0p/ekrBDwYYeLSusoKzwAAAACA0Y+gLwAAAAAAKHMcR5lMpqr6bu/QVDKZrKp+5fF4qh6Z7vF4ZJqmbNtWoVBQJpNRIpFQV1eXCoVCeTu3212u/FWqtlg5H4lEaipaWZalM888Uy+++KJmzpyptrY27b///vr85z9fDhh3dHTwxTUAoMyyLPX09PRZZT6dTldtEwqFagLAla/D4TCfNQCApti23TA0PFhh4tLyZrlcrqbCwf0FhvsLG4+nYDgAAAAAjCSCvgAAAAAwAVmOo40ZS+vTRa1PF9VTtGXZjlymobDbVEfQrY6gW+0Bl1w8OnRcyefzdUO8ldPKL4hN0yyHmiKRiAKBQPnLWsuylM1mq8JTldWqfD5fObRbbxoMBnk0LQBgVCgUCjWfh70/I/P5fLm9YRiKRqMNqwLHYjEFAgE+58YhxtEARivHcQYUDN6aMHHpT7NM09ym0HCzlYtdLhefuQAwijGGBgBg2xH0BQAAAIAJpDtvadmmrJ7blFXWeu/XQVOSXdGmct7vMrSgza+92vyKeanEM9pZltUwpFSaz2azVduEw+FyICkcDsvr9co0TTmOo3w+X67uW/pTeRkhGAzWhHcrX/v9/uF+CwAAGBKO4yiXy/VZFTiRSFTd8OJ2u2uqAfee9/l8I3hWGAjG0QDwHsdxZFnWkIeJC4WCmv0a2zCMrQ4UNxsmLr0mUAwAzWMMDQDA4CHoCwAAAAATQNay9cDalJZ35mRIGsgvgqX2e07yaeH2IflcPKZ6JNi2rVQq1WfAqKenp2qbQCBQVWEwHA7L7XZL+mc13mQyqa6uLnV3dyuZTFZtH4lEGlbjjcVi8nq9w3b+AACMdo7jlD+rG91w0/uz1ufz9RkEjkaj5c9ujAzG0QAwcioDxc0Eg3u3aTaAXHmjTn8GGg7emvYej4dAMYAxjTE0AACDj6AvAAAAAIxzKxN53bEqqXTRGdBF1d4MSSG3ocUzI9ohSsBzMDmOo0wm0zAUVAoG2fY/6114PJ6aUFAwGKyqxtvT01PevqurS+l0urx96ZHjlVV4K6vyEiwCAGDwWZalZDJZ85lfOc1kMlXbhEKh8md+vWk4HJZp8uX3UGAcDQATg23bW12ZuL/tKtcVi8Wm++RyuYY8TOzxeBhDABh0jKEBABgaBH0BAAAAYBxbujGj+9akBlw5oZHSfo6eFtI+7YFB2OPEkM/nG1bhLU0LhUK5vWmaVQHe0mu/3y/DMGRZllKpVHn7rq4udXV1KZfLlffhcrkaVuONx+OKRCJ8oQcAwChUKBT6rArc3d1dM24oVeGvVxU4FospEAhQGXCAGEcDAAab4zhbHSbua329Zc0yTXPIw8SlQDFjEWD8YwwNAMDQIegLAAAAAONU6cLqUOEC63ssy6oJ3/QO8maz2aptwuFwTRgnGo3K4/HIcRzlcjklEgl1dXWVg7y9Qz0ej6dhNd5SdT++RAMAYPxxHEfZbLbPIHAikah6EoDb7e6zKnA0GpXP5xvBsxpdGEcDAMYyx3FkWdagBoobtW82amAYxpCHiT0ej1wuF9dCgBHCGBoAgKFF0BcAAAAAxqGVibz+8EZiyI9zxo7Rfh+d5jiO1q1bp46OjjFXQdZxHPX09PRZVa+np6dqG7/fX1VFrxSgKVXQLRaLSiaTVQHerq4uJRIJWZZVtZ9GId54PE5lPgAA0JDjOEqlUg1vQkokEkomk1Xb+P3+hkHg0ljG7XaP0BltvQ0bNqilpUVeb3OP+x1N42gAAEYzx3Fk2/aQh4kLhULVDUz9aSYc3FdguL8wcakN12SAf2IMDQDA0CPoCwAAAADjTNaydc1LW5QuOoPyiLRGDEkht6Hzd22Rz1U/wNvd3a077rhDK1as0Kc//Wl1dHQMYY8GplQNr68ATKNqePUCMKFQSI7jKJ1O14R4S/ur/BU8FAqVQ7u9p/F4nKp6AABgSFmWVb75qNFYKJPJVG1TGr80qgocDodH1Y1djuPo0ksvldvt1oknnqgddtihz/ajaRwNAAD+qVGgeDDDxIVCoeoG7P5UBoIHGhxuNkzs8XhG1dgKqIcxNAAAw4OgLwAAAACMM0tWJ/V8Z25IL6yWGJL2mOTTcTMiVcsdx9Ezzzyjv/71r/L5fDrhhBO00047DUOP/imfz9dU4e09LRQK5famaSoSidStxhuLxeT3+5XL5ZRIJNTV1VUT5O1d2be0bb0gbywWk8fjGdb3AwAAYKAqx1ONnm7QaDxVrypwNBod9qcSbN68WbfddptWrVqlBQsW6JhjjpHf76/bdjSMowEAwMixbXvA4eCtbd8sl8vVVDi49/qBhIk9Ho9cLtcQvrMYa95++21ZlqVZs2b125YxNAAAw4OgLwAAAACMI105S1e9tGXYj3vB/BbFvO99IdDZ2anbb79dq1at0j777KOjjjqqYZhia1mWVRUwaaYCXTgcbvg46mg0KpfLVd6+d4i3q6uran+maSoajZar75bCK6X50v4AAADGs8onJDS6waq/JyT0DgLHYjF5vYP7OF7HcbR06VLdd9998vl8Wrx4sXbeeeeqNqNhHA0AACYGx3GqAsEDDRP3t03lumbjIKZpDnmYuBQoHs6bvrB17rrrLj399NOaM2eOjjrqKE2ZMqVuO8bQAAAMH4K+AAAAADCOPPROSk9uyNStoLBy6eN6+DdXau3Ly5XctEEfu+TXmn/E8eX1X9+7ve4+j/u3/9ChH/9cw2Makg6YEtAhHQE98cQTevDBBxWJRJp6PHI9juOop6enYdW4etVz/X5/w6pxsVhM4XBYuVyuHNotBXgr5/P5fHl/pQBKvRBvLBZTJBLh0YkAAABNKI3tGt2c1Whs11cQeGtvquru7tadd96p119/XbvttpsWLVqkUCgkadvG0cnOd3X3Zd/T6088qGxPQrMWHKAPfO3/qW3Gjn32pzSOPmxqaMDnAgAA0B/HcWRZ1laHiQeyvPLGrv4MdZi4tIxA8dZzHEcvv/yy/va3v2nz5s3aa6+9dMQRRygajVa162sMLUkPXv8LvXj/ndr41uvy+AKaued+WvSv31b7rDmSpHT3Fv31qh/r9ScfVNf6tQq1TNKuhx+nYy74uvyRaN19MoYGAExUBH0BAAAAYJywHEeXv7BZWav+r3mvPvZXrVr2lLbfZU/99sJzagMKmzb0av83/e/3vqALb31KrdNm9Xlsr+Eo/MytWv/OO9p///21cOFCeTyemnZ9VX2rDHzUq/rWqBpvLBaT2+1WT09PVYi397RYLP6zv15vObRbOS29DoVCfBkAAAAwTHo/raHejV7ZbLZqm76e1lC60aveeM5xHL3wwgu6++67ZRiGFi1apF3mz9cVL27ZqnG04zj6r3OOl8vt1vFf+p78oYge/e1/6bXH79cXb35U3kDfAQS/y9Dnd2+Vi7EnAAAYwyzL2uow8UCqFluW1XSfGoWEBytMXFo+nosBWJalpUuX6qGHHlI+n9cBBxyg97///fL5fP1ei5ak6//ldO157CmaNn+BbKuoe674oTaseKU8Tl6/4mX99aofa58TP6zJs3dS17o1+suPLtR2c3fVR396Q8P9MoYGAExEBH0BAAAAYJxYny7qxle7mmr79b3ba4K+vd30pbOVS/XovKv/t6l9hp6/VycedpDC4XCfFdsKhUJ5G8MwqoIZ9Sq2BQIB2batRCJRN8Tb1dVVEw4OBAINq/HG43H5/X6CvAAAAGNIPp+vCQL3nlaOM03TrBpf9p66XC49+OCDeumllzRz9320focDmupH73H0xlVv6OenHKAv/OkRTdlxniTJtm396Oj5OvZz39B+p5zV7z7P2TmujqB7K94VAACAicW27QEHhvta31fF4ma5XK6mqwxvTZi49GckA8W5XE6PPfaYnnjiCXm9Xh122GHafpc9ddOK5ID207Nlk3545C761DW3aod9Dqrb5oX7btUfvvVZffexVXK5G4+RGUMDACYaPvUAAAAAYJxYn27+AnR/kp3v6pVH79OHvntFcxs4jlKuoH79619XLQ6FQuVAxY477lgT5A2HwzJNU4VCoSrAu2bNmqr5RCJRtd9wOFwO7U6dOrWmKq/X6x2stwIAAACjgNfrVVtbm9ra2uqudxxHmUym4c1mq1evVjKZrLo5zOPxKBwOa20yJ9NxtupGMCufkyS5vb7yMtM05fZ69dayvzcV9F2fLhJSAAAAaIJpmvJ6vUN+7c9xnD5DwAMJE+dyOfX09DRs3yzTNJuuMry1YWK32y2Xy1UzLvb5fFq4cKH23XdfPfDAA1qyZIkib22S5u4nqfkxdDb53jXeQKylcZuehPyhSJ8hX4kxNABg4uFTDwAAAADGifXpokxJdr8t+/fs7X+QLxjW/IWLm9vAsRXefpaO3Hl7tbe3l6unuf/vgmwul6uqwrt27dpyNbauri6lUqnyrgzDUCQSUTweV0tLi2bNmlUV5I3FYuX9AgAAANJ7Y8hgMKhgMKiOjo66bWzbViqVUnd3t7Zs2aKVK1dq1apVykbbJMeWDNeAj9s+a67iHdN0zxU/0CnfvESeQFCP/e4qdW94R8mNG/rd3tTg3rAHAACAbWcYRjn8OpQcx5FlWYNWmTidTjfcptmHfVeee6Ng8Ny5c7UmFJdj2zLM5sbQtm3rjp99SzP3ep865uxSt01qS6fuv+bn2u+Dfd8sxxgaADAR8c0oAAAAAIwTPUV7UEK+krT0tv/WXsedKo/P39wGhqlENq+7H71H7e3tisVikqREIqGuri5ls9lyU9M0y6Hd9vZ2zZ07t6oabyQSkcs18JAFAAAA0BfDMNTT06MXX3xRL774olKplCZPnqz2aTO0ucmAQm8uj0cf+9mNuvl7/6bvHT5XpsulHd93qHY6+EipiTCFLSlVHKxRPAAAAMYSwzDkdrvldrsVCASG9Fi9A8VbGyYuFArKZDJy2vySYTZ9/Nsu/po2vPGKPnP9HXXXZ3uSuvHfPqLJs3fSUZ/+ap/7YgwNAJiICPoCAAAAwDhh2c1VZejPymef0Ma3VujDF1/T/EaGIZku2batDRs2aMOGDTJNU5MmTdJuu+2m6dOnq6WlRbFYTJFIZKseiwwAAABsje7ubr3wwgt6/vnntXHjRoVCIe2+++7ac889NWXKFP3xjYQ2J5t/bHJv2++6p/719w8qm0yoWMwr3NKmK88+VtN22bOp7YuDNI4HAAAAGnG5XHK5XPL7myzsUMe6dev0xBNPaMWKFfLO2Kfpa7y3Xvw1vfLIvfrUtbcpNmVqzfpcqkc3fO4M+YJhfeySX8vVRCVlxtAAgImGoC8AAAAAjBMuc3DCs8/c+jttv8ue2m6n3QawlaMZ06bplAsvVDAY1Lvvvqvly5frhRde0DPPPKOVK1dqjz320H777UfIFwAAAEPOcRw9//zzWr58uVauXCm326158+bpmGOO0ezZs2Wa/6w+NljjaH8kKknatPoNrX1pmY6+4KKmtnMP0vEBAACAweY4jlasWKEnnnhCK1euVDwe17HHHqvVk2bpjWSx321v+/FFeumBu3T+NbeodfuZNW2yPUld/y+ny+316uxLb2r6CXOMoQEAEw1BXwAAAAAYJ8JuU6bee3RZPbl0jzrfXlme37J2td559QUFoy2KbzdN0nsXVl+473Yt/tJ3B3RsU4baIiGFQiFJ0pQpU3TMMcfoqKOO0sqVK/X888/r0Ucfldvt1kEHHbQ1pwcAAAA0bePGjbr11ls1c+ZMfeADH9Cuu+4qn89Xt+22jqNfuO9WhVraFO/YXutXvKzbf/pN7Xr4cdrpwCP67acpKeRu/pHHAAAAwHAoFot64YUX9MQTT2jjxo2aOnWqTjvtNO2yyy4yTVNbVvfIVLHhGFp6r5Lv8iU366xLfyNfMKzkpg2SJH84Ko8/8F7I97MfUiGb0Rk/+KVyqaRyqaQkKdTSJtPlqrtfxtAAgInIcByHevYAAAAAMIYVCgV1dXXp2Y1pPZcNSg0q5r75zGO65lMn1yzf+8Qz9KHvXiFJeurm3+iOS76lb9zzYrkiWbMWTQ9rr7bGFRcsy5KrwcVZAAAAYLA1O/5ctimru9/uabi+v3H0Y//zKz3ymyvV07lRkbYpWnDC6Vp4/pfl9nib6md/42gAAABguDiOoyeeeEJPPPGEenp6tNNOO+mggw7SjBkzqp7U1t8YWpK+vnd73eWnfecy7fOBDzccZ0vSV+9YqpapMxrumzE0AGCiIegLAAAAAKNcNptVV1eXuru71dXVVX5dmk+n05IkI9Yu72Gnj1g/z9k5ro4gD44BAADA2LI+XdSNr3aN2PEZRwMAAGC0SKVSuvrqqzV37lwdeOCBamtrq9uOMTQAAMOLTz0AAAAAGEGO4yidTteEeCunuVyu3N7lcikWiykej2vKlCnaeeedy/ORWEy/WW0paw3//Zx+l6H2ANV6AQAAMPa0B1zyuwzG0QAAAJjwQqGQvvSlL/XbjjE0AADDi6AvAAAAAAwhx3GUTCZrwruV00KhUG7v8XgUj8cVj8c1ffp07bbbbuX5WCymcDhc9Yi03hakU3pyQ0bDeXnVkLSgzS9XH/0CAAAARiuXYWhBm59xNAAAANAkxtAAAAwvgr4AAAAAsA1s21YikWhYjTeRSMiyrHJ7v99fDu3Onj27KsQbj8cVCAT6DPL2Z682v57YkBmMU2ua83/HBQAAAMYqxtEAAADAwDCGBgBg+BD0BQAAAIA+FIvFPqvxJhIJOc4/axaEQqFyaLejo6MmyOvz+Ya0vzGvS3tO8un5ztywVFIwJO0xyaeYl0elAQAAYOxiHA0AAAAMDGNoAACGD0FfAAAAABNaPp+vCu/2DvL29PRUtY9Go+XQ7owZM6pCvLFYTB6PZ4TO5J8Wbh/SG915pYrOkF5gNSSF3IYWbh8awqMAAAAAw4NxNAAAADAwjKEBABgehlNZegoAAAAAxhHHcZTNZusGeUvzmcw/Hy1mmqai0WhVFd5SiDcejysajcrlGhvVAlYm8vrDG4khP84ZO0a1Q9Q75McBAAAAhgPjaAAAAGBgGEMDADD0CPoCAAAAGLMcx1EqlWoY4u3q6lI+nxGY+D0AAQAASURBVC+3d7vdVdV3e08jkYhM0xzBMxpcSzdmdN+a1JDt/5hpIe3dHhiy/QMAAAAjgXE0AAAAMDCMoQEAGFoEfQEAAACMWrZtq6enpyrE23taLBbL7b1eb90Qb+l1KBSSYRgjeEbDr3SB1ZAG6dFpjiRD03ve1kcPWTAoewQAAABGm8EeR5f2Q0ABAAAA4xVjaAAAhg5BXwAAAAAjxrIsJRKJuiHerq4uJRIJ2bZdbh8IBMqh3d4h3ng8Lr/fP+GCvM1YmcjrzlVJpYrONl1gNSSF3IZmp9fo6btv0Yknnqi99957sLoJAAAAjCqDPY5ePDPCo4YBAAAwrjGGBgBgaBD0BQAAADBkCoVC3Sq8pWkikahqHw6Ha6rxVgZ6vV4u6G2trGXrgbUpLe/MDbiiQqn9npN8Wrh9SF7T0F133aVnn31WH/vYx7TDDjsMTacBAACAETaY42ifyxyaTgIAAACjyLaMoUut95zkZwwNAEAFgr4AAADAGLd582ZFo1G53W5J0qpVq/SVr3xFq1evVjgc1ne/+10dfPDBQ3LsXC5XU4W3MsibSqXKbQ3DUCQSqanCWzktnQOGTnfe0rJNWT23Kaus9d6vg6Yku6JN5bzfZWhBm197tfkV87rKbWzb1n//939r7dq1Ovfcc9XW1jZcpwAAAAAMu3rjaEOOHP3ziSLNjKMBAACAiWKwrkUDAACCvgAAAMCot2bNGq1fv17z589XIBCQJD311FP613/9V+VyOblcLp122mm66KKLVCwW9fGPf1x77723Tj31VG3atEknn3yy/vGPfygWiw3ouI7jKJPJNKzG29XVpWw2W25vmmaf1XgjkYhcLi7OjRaW42hjxtL6dFHr00WliraKtiO3aSjkNtURdKsj6FZ7wCWXYdTdRzab1fXXX69isajzzjtPwWBwmM8CAAAAGF6V4+j/+t0fdejRi+T2+qrG0REnq385+yO6e8ldI91dAAAAYMQ1uhZtFfKyMj2aP22Kpoa9fV6LBgBgoiPoCwAAAIxin/3sZ3XPPfdo5cqVWrp0qRYsWCBJOvDAA/Uf//EfOvroo5XL5RQOh7V+/XpNnjxZHo9HnZ2dikajkqT9999fZ511li644IKaoK1lWVq3bl1VNd7K6ryFQqHc1u12V1XjrQzxxmIxRSIRGVyEm3C6urp07bXXatKkSTrrrLOoygwAAIAJY88999Ty5cvL8x//+Mf161//WpK01157admyZSPUMwAAAGB0+/3vf69Pf/rT2m677bTddtvpW9/6ls4880zZtq2bbrpJxx9//Eh3EQCAUYVvYAEAAIBR7Oyzz9aVV16pffbZR1u2bJHjODIMQ08//bQkyeVyaeXKlTrssMNkWZYkadKkSbr99tv10Y9+VMlkUuvWrdPy5cuVSqXK4d+SfD6v6667TpLk8/nKod0ddtihJsgbDAYJ8qJGPB7XGWecoV//+te6/fbbdfLJJ/PvBAAAABNC73HvAw880HAdAAAAgH/60Y9+pL///e+aN2+eHnvsMZ188sn6y1/+opaWFp199tkEfQEA6IWgLwAAADCKve9975NhGAoEAnr77bfLXxb/7W9/00UXXaRzzz1Xmzdv1te+9jWZpilJuvzyy3XLLbfoiiuu0LRp07THHnsoFAopnU7XBH0DgYA+85nPKBaLye/3D/v5YXyYPn26Tj75ZN18882aNGmSDj300JHuEgAAADDkJk+erFtuuUUnn3yybrrpJhmGoRtvvFHxeLzmdy8AAAAA/+RyuTRv3jxJ0sEHH6xQKKT3v//9kiTbtkeyawAAjEoEfQEAAIAxYPvtt9fq1avL8ytXrtRBBx2kJUuWKB6P6/jjj1c6ndZPfvITfehDH9Lee++t7u5u7b333vrmN7+pDRs2qKOjo+6+p0yZMlyngXFst9120+bNm/XAAw+otbVVu+2220h3CQAAABhSl1xyiRYvXqyPfOQjOuKII3TXXXfprLPOkm3b+vWvfz3S3QMAAABGLdu2tWLFCs2ZM0fPPPOMurq69PzzzysWi5WfXggAAP6JoC8AAAAwQrLZrLq6utTd3a2uri4ZhqH3ve99ddvOnDlTq1atUqFQkMfj0eOPP66ZM2cqHo9LkhYvXqwHHnhAmUxGfr9fO+64oySpp6dHr776qg477DBJ7108K1X+BQbbIYccos7OTt1yyy2KxWKaPn36SHcJAAAAGDKtra1avXq1tmzZopaWFknSs88+q3w+L8dxRrh3AAAAwOj1jW98QwcccIBmz56tNWvW6H//93+1ePFibdq0SVdfffVIdw8AgFGHoC8AAAAwBBzHUTqdLod4KwO9pWkulyu3d7lcmjZtWk3Q1zAMSe9VS7311lvl8XgkSZFIRH/961/1zW9+U8ViUStXrlQ8HlcgENCaNWt06aWX6qGHHlI2m9UHP/hBff7znyfkiyFnGIZOPPFEdXV16fe//73OO++8cuABAAAAGG+mT5+un/3sZ/rSl75Utfz+++/X1Vdfrb/85S8j1DMAAABgdDvjjDN08MEH69VXX9WCBQvU2tqqlStXKpfLKRQKjXT3AAAYdQyH28oBAACAAXMcR8lksia8WzktFArl9h6PR/F4XPF4XLFYTLFYrGo+HA6XQ731XHnllfr+97+vT3ziE5o5c6Y+/OEP6ytf+Yoefvhh+Xw+7bzzzrrkkks0ffp0ZTIZPf/88/J4PJo1a5ZaW1uH4y0BytLptK699lq53W598pOflN/vH+kuAQAAAINu7ty5isViOvPMM3XhhRdWrZs3b55eeeWVEeoZAAAAMPZ0dnZq0qRJI90NAABGJYK+AAAAQB22bSuRSDSsxptIJGRZVrm93+9vGOItVdrtK8jbSLFY1JlnnqmXX35Zs2fP1uTJk3XQQQfp3HPPVSqVkm3bikQig3nqwKDYtGmTrrvuOm2//fb6yEc+QjVpAAAAjDt77723HnjgAR199NE6+eST9Y1vfKO8bsGCBXruuedGsHcAAADA6PW9731Pn/rUp9TR0aEXXnhBJ510ktatW6e2tjb97//+r/bbb7+R7iIAAKMKQV8AAABMSMVisWE13q6uLiWTSVUOlUOhUDm0Wy/I6/P5RvBsgNFp5cqV+u1vf6sFCxZo8eLFWxV2BwAAAEYTy3G0MWNpfbqoK37zey06+YPK5Qt6+u9PyitLB++2s1a9sFR3/eG3unvJXSPdXQAAAGDEVY6h16eL6inaevDhh3X4oYcq7Db1x2t/qYX77qEPn3isHn7wQf37v/+7Hn300ZHuNgAAowpBXwAAAIxL+Xy+JrxbGejt6empah+NRmuCvJVTj8czQmcCjG3PPvusbr/9dh177LE64IADRro7AAAAwFbpzltatimr5zZllbXe+1rFLhZlut3lNo5tSYYpwzDkka19p4S0V5tfMa9rpLoNAAAAjJh6Y2hTkl3RxtR7QWDDMOR3GVrQ5tfXzzpFj//t3pHoMgAAoxZBXwAAAIw5juMom83WDfKW5jOZTLm9aZqKRqNVVXgrq/JGo1G5XHzxCgyV++67T48//rjOPPNM7bzzziPdHQAAAKBpWcvWA2tTWt6ZkyFpIF+olNrvOcmnhduH5HOZQ9NJAAAAYBTZtjG0I9t2tFd7gDE0AAAVCPoCAABg1HEcR6lUqmGIt6urS/l8vtze7XY3rMYbj8cVDodlmlwMAkaK4zj64x//qDfeeEOf/OQn1dHRMdJdAgAAAPq1MpHXHauSShedAYUTejMkhdyGFs+MaIeod7C6BwAAAIw6jKEBABgaBH0BAAAw7GzbVk9PT1WIt/e0WCyW23u93oYh3lgsplAoJMMwRvCMAPQnn8/rxhtvVE9Pj84//3xFIpGR7hIAAADQ0NKNGd23JjXgCmSNlPZz9LSQ9mkPDMIeAQAAgNGFMTQAAEOHoC8AAAAGnWVZSiQSDavxJhIJ2bZdbh8IBMqh3d4h3ng8Lr/fT5AXGAeSyaSuvfZahUIhnXPOOfJ6qcQAAACA0acUUBgqBBUAAAAw3jCGBgBgaBH0BQAAwIAVCoW6VXhL00QiUdU+HA7XVOOtDPQS9gMmjvXr1+v666/XjjvuqNNPP50QPwAAAEaVlYm8/vBGov+G2+iMHaM8ghgAAADjAmNoAACGHkFfAAAA1MjlclXh3d5B3lTqn3dlG4ahSCRSU4W3cup2u0fwbACMNq+99pr+8Y9/6KSTTpJpmiPdHQAAAECSlLVsXfPSFqWLzqA8argRQ1LIbej8XVvkczEeBgAAwNjFGBoAgOFB0BcAAGCCcRxHmUymYTXerq4uZbPZcnvTNPusxhuJRORyuUbwjACMVY7j9FnRt1gscqMAAAAAhs2S1Uk935kb0oBCiSFpj0k+HTcjMgxHAwAAAIYGY2gAAIYH35gCAACMM47jqKenp6Yab+V8oVAot3e73eXQ7vbbb69dd921qjpvJBLpM4gHAFurr58tGzdu1Oc//3ldfvnlam9vH8ZeAQAAYCLqylla3pkbtuM5kpZ35nRQR1AxLzfPAgAAYOxhDA0AwPAh6AsAADDG2LatZDJZFeLtPbUsq9ze5/OVQ7s77LBDVTXeWCymYDBIkBfAqFGq8tve3q5ddtlFZ599tpYsWTLS3QIAAMA4t7wzK0OqW4nswet/oRfvv1Mb33pdHl9AM/fcT4v+9dtqnzWn3KaQy+qun39by++9RVY+p7kHHqGTvv4TRSZNbnhMQ9KyTVkdNjU06OcDAAAADLW+xtBSc+Pov/zgy1rx1MNKbFwvXyCkGf/XZvIOc+vukzE0AGCiMhzHGY4K+gAAAGhSsVhUIpGoG+Lt6upSIpFQ5RAuGAyWQ7u9Q7zxeFx+v38EzwYABq67u1uxWEyS9JnPfEaFQkHXXXfdCPcKAAAA45XlOLr8hc3KWvW/Lrn+X07XnseeomnzF8i2irrnih9qw4pX9MWbH5U38F7A4JYffUWvPHqfPvSdy+UPR3Xrjy+SaRr6zA139Xlsv8vQ53dvlYsbcAEAADCG9DeGlpobRz9182/UPmuO4ttNU7p7i/529U/1zmsv6qu3L5Xpql+1lzE0AGAiIugLAAAwzAqFQp/VeJPJZFX7SCRSDu3Wm3q93hE6EwAYPLlcTj6fT5L005/+VKtWrdIVV1yhp59+WjfeeKOuvPLKEe4hAAAAxqv16aJufLWr6fY9Wzbph0fuok9dc6t22OcgZZMJ/eDIeTrjR1dp96M+IEl6d+XruvTUg3TBjUs0Y499+9zfOTvH1RHkAYwAAAAYOwY6hpZqx9H1rHvtH7rszMN14a1PadL0HRruizE0AGCi4VMPAABgkGWz2ZoqvN3d3eX5dDpdbmsYhqLRqOLxuCZNmqTZs2dXBXmj0ajcboZsAMa3l156SW+//baOPfZYSdJXvvIV7bjjjjrjjDP08ssva99995VlWXr55Ze12267jXBvAQAAMN6sTxcH1D6bTEiSArEWSdLal5fLKhY0Z//Dym0m7zBX8Y5pWv38M/0Gfdeni4QUAAAAMKYMdAwt1Y6je8tnUlp62/+oZfuZinVs3+/xGUMDACYSPvUAAAAGwHEcpdPpmhBv5TSXy5Xbu1yucmh3ypQp2nnnncvz8XhckUhEpmmO4BkBwMjr7OzU5z73OT3wwAOaNm2aJOnjH/+45s6dq1NPPVWnn3667r//fn3pS1/SxRdfrEWLFo1wjwEAADCerE8XZUqym2hr27bu+Nm3NHOv96ljzi6SpGTnu3J5vApEYlVtw5Palex8t8/9mdq6kAQAAAAwkgYyhpbqj6NLnvjj9br7P7+rfCat9llzdO4v/yS3p/HTLBlDAwAmIoK+AAAAFRzHUTKZrAnvVk4LhUK5vcfjKYd2p0+frt122608H4vFFA6HZRjGCJ4RAIx+hxxyiL785S/rrLPO0nXXXad169bphhtu0A033KDDDz9cknTAAQfosssu07/+679q9uzZ2mmnnUa20wAAABg3eop20wGF2y7+mja88Yo+c/0dg3JsW1Kq2OzRAQAAgNFhIGNoqe9x9ILjTtPcAw5TcuMGPXLTL/XfXztPn7nhTnl8/rr7YgwNAJiICPoCAIAJxbZtJRKJhtV4E4mELMsqt/f7/eXQ7uzZs6tCvPF4XIFAgCAvAAyCz3zmM1q3bp2++c1v6r777tMXvvAFHX744XIcR5ZlKRAI6NBDD9VJJ52kyy+/XJdffvlIdxkAAADjhGU7TbW79eKv6ZVH7tWnrr1NsSlTy8sjkybLKuSVSXZXVfXt6dyoyKTJ/e632OTxAQAAgJFQLBaVTqeVSqWUTqeVTqfVmY9KZqip7RuNo0v8kaj8kajaZuyo6Xvsq+8dNlf/eOAu7bXog437xBgaADDBEPQFAADjSrFYbFiNt6urS8lkUo7zz1/+Q6FQObTb0dFRE+T1+XwjeDYAMLF897vfVaFQUE9Pj1paWlQsFmWaptzu9351zWQysixL06ZNG+GeAgAAYDxxmX3fwOs4jm778UV66YG7dP41t6h1+5lV67ffZU+53B698dTD2u3IEyVJG99aoa71azRjj337Pb67n+MDAAAAg8VxHBUKhXJot/e09KdyeT6fr9mPd//FMiYHpT6K4fQ3jm6wkSRHVj7XZzPG0ACAiYagLwAAGFPy+XxNeLcy0NvT01PVPhqNlkO7M2bMqArxxmIxeTyeEToTAEA9Ho+nXLH34IMPlmVZam1t1ebNmxUKhfTkk0/qhhtuGOluAgAAYBwJu02ZUsNHD9968de0fMnNOuvS38gXDCu5aYMkyR+OyuMPyB+Jat+TP6o7L/m2AtEW+UMR3faTr2vGHvv1G/Q1JYXc5qCeDwAAACYOx3GUzWargrr1wruVr4vFYs1+fD6fgsGgQqGQgsGg2tvby69L08rX96/P6/nObMMxtNT/OHrzmrf0/L23aO4BRyjUMknd776jh264TG6fXzu//6iG+2UMDQCYiAynsqQdAADACCpdjKgX5C3NZzKZcnvTNBWNRquq8JZCvPF4XNFoVC6XawTPCACwNd555x0tXrxYCxcu1Pnnn69Vq1Zp1apV6ujo0Pz587XjjjuOdBcBAAAwjizblNXdb/c0XP/1vdvrLj/tO5dpnw98WJJUyGV118+/reX3/EXFfF47HXiETvr6jxVpm9Lv8RdND2uvNv/WdR4AAADjim3bDQO7qVRKmUymZnm92E/vYG7v+d7TgX6f1t8YWup/HJ3YuF43f+8Leufl55VJdCk8qV2z9j5QR55/odpnzelz34yhAQATDUFfAAAwbBzHUSqVahji7erqqnr8j9vtrqq+WzmNx+MKh8MyTe7YBYDxaNmyZTrvvPN01VVXad99q6ug2bbNz38AAAAMmvXpom58tWvEjn/OznF1BHkAIwAAwHhULBb7rbBbuT6bzdbswzTNulV16wV2g8GgAoHAkF8/ZQwNAMDw4lMPAAAMGtu21dPTUxXi7T2tfByQ1+sth3dnzpypPfbYo6o6bygUkmEYI3hGAICRstdee+nrX/+6Pvaxj+mpp55SNBotryPkCwAAgMHUHnDJ7zKUtYa/LorfZag9wNOIAAAAxgLHcZTP5xtW2y0FdyvnKwvclHg8nqpwbmtrq6ZNm9YwvOvz+Ubd92WMoQEAGF4EfQEAQNMsy1IikWhYjTeRSMi27XL7QCBQDu3OmTOnKsQbj8fl9/tH3YUJAMDoceqpp2r77bevCvkCAAAAg81lGFrQ5teTGzIazpiCIWlBm18uro0AAACMCMdxlMlkmgrslqaWZdXsx+/3VwVzJ0+e3LDabigUksfjGYGzHVyMoQEAGF4EfQEAQFmhUKhbhbc0TSQSVe3D4XA5tDt16tSqEG88HpfX6x2hMwEAjBcHHHBA1XzpQrrLRcUGAAAADJ692vx6YkNmWI/p/N9xAQAAMDgsy+ozpFsvyOs41TFVwzAUCASqQrqtra11A7ul1xP1WiVjaAAAhg9BXwAAJpBcLlcV3u0d5E2lUuW2hmEoEokoHo+rpaVFs2bNqgryxmIxud0MJQAAw8dxHHV1denmm2/WmWeeSaVfAAAADJqY16U9J/n0fGdueCqSObacNa/qxbSpAw44YMKGQwAAAPpSKBSaCuyWXmez2Zp9uFyuqmBuJBLRlClTaqrtlqaBQICnUTZpuMfQhqQ9JvkU8zJ2BgBMPIbT+/YkAAAwJpUeL9SoGm9XV1fVBQ7TNKtCu72r8UYiEb5kAgCMOslkUtdee61CoZDOOeccqscDAABg0OQsW9e8tEWpojOkQQVDUtBlaId3ntGzT/1dra2tWrRokebMmTOERwUAABhZjuMol8s1FdgtTQuFQs1+PB5PTTi30etQKCSv10twdwgN5xg65DZ0/q4t8rnMITwSAACjE0FfAADGCMdx1NPTU1ONt3K+8oKH2+0uh3ZjsVhViDcWiykSiXBhAwAwJq1fv17XX3+9dtxxR51++ul8ngEAAGDQrEzk9Yc3EkN+nDN2jGqHqFcbNmzQkiVLtGrVKu2888469thj1dLSMuTHBwAA2FalAjS9w7mVwd3e85Zl1ezH7/dXBXMDgUDDarvBYFAej2cEzhZ9Ge4xNAAAExFBXwAARgnbtpVMJqtCvL2nlRdAfD5fw2q8sVhMwWCQ4BMAYNx67bXX9Pvf/14HHnigjj766JHuDgAAAMaRpRszum9Nasj2f8y0kPZuD5TnHcfRSy+9pHvvvVepVEoHHXSQ3v/+9/P0CgAAMKwsy+q3wm7l60wmo95xE8Mw+q2wW7ksEAjwdMlxYrjH0AAATDQEfQEAGCbFYlGJRKJuiLerq0uJRKLqgkgwGGxYjTcej8vv94/g2QAAMPKefPJJ3XPPPTrhhBO0zz77jHR3AAAAMI6UggqGNCiPIC7tp6+AQj6f16OPPqrHH39coVBIxxxzjHbddVdu5AYAAFulUCg0rLZbr+puLper2YfL5aob0q0X3g2FQvL7/YxdJrCRGEMDADBREPQFAGCQFAqFPqvxJpPJqvaRSKSmGm/llKotAAD0zXEc3XXXXXr22Wf10Y9+VLNnzx7pLgEAAGAcWZnI685VSaWKzjYFFQxJIbehxTMjTT1qeMuWLbrnnnv06quvatasWVq0aJGmTJmyDT0AAABjneM4yuVyfVbY7R3kLRaLNfvxer11K+02qrrr9XoJ7mJARmoMDQDAeEfQFwCAJmWz2ZoqvN3d3eX5dDpdbmsYhqLRaE0V3tI0Go3K7XaP4NkAADA+2Lat//7v/9aaNWt07rnnqr29faS7BAAAgHEka9l6YG1KyztzA65MVmq/5ySfFm4fks9lDujYK1as0N13363NmzfrM5/5jNrb2wnaAAAwTti2rUwm03S13XQ6Ldu2a/YTCAQaBnbrhXf5bgrDYSTH0AAAjFcEfQEA0Ht3QqfT6ZoQb+W08pFFLperYTXeeDyuSCQi0+QXTwAAhkM2m9X111+vYrGo8847T8FgcKS7BAAAgHGmO29p2aasntuUVdZ672sVU1Jl3KZy3u8ytKDNr73a/Ip5XVt9XMuy9Nprr2mXXXapu9627fI1qNdff12///3vFQgEdOGFF271MQEAwMAVi8W6VXXrBXZTqZQymUzNPkzTrBvWbVRtNxgM8l0URrWRGkMDADAeEfQFAEwIjuMomUzWhHcrp4VCodze4/FUVeOtDPHGYjGFw2EqqAAAMIp0dXXp2muvVWtrq84++2yqkwAAAGBIWI6jjRlL69NFrU8XlSraKtqO3KahkNtUR9CtjqBb7QGXXIN47chxnLrXonp6evTaa6/pJz/5iWKxmP7yl79owYIF+t3vfqe2trZBOz4AABOJ4zgqFApVQd164d3K15XFYkrcbnfDkG69136/n++eMC6N1BgaAIDxhKAvAGBcsG1biUSiYTXeRCIhy7LK7f1+f8MQbzweVyAQ4GIKAABjzNtvv61f//rX2nXXXXXKKafwWQ4AAIBx7eWXX9ZPfvITTZs2TXvttZdaW1v1s5/9TF/84hd11FFHadmyZXriiSd0wgknaPr06SPdXQAARozjOMpms3UDu6Xqur2XF4vFmv34fL6mqu2Wph6Ph+tTAAAAGBQEfQEAY0KxWGxYjberq0vJZFKVH2mhUKgc2q0X5PX5fCN4NgAAYKi8+OKLuvnmm3X44YfrsMMOq1n/+uuva8WKFTruuONGoHcAAADA4Ein0/riF7+oP//5z+rs7JQknXnmmZo1a5Yuvvhi/fKXv9QNN9ygfffdV3//+991wgkn6Hvf+94I9xoAgMFh23bdqrqNXqfTadm2XbOfQCDQdLXdYDDIE6QAAAAwYhiJAgBGhXw+XxPerQz09vT0VLWPRqPl0O6MGTOqQryxWEwej2eEzgQAAIyk3XbbTZs3b9YDDzyg1tZW7b777lXrE4mEnn76aS1cuJAbfwAAADBmBYNBXXbZZZKk+fPna/78+cpkMvr0pz8tSdq0aZNOOeUUfeMb31Amk9Epp5yiBx98UIcffvgI9hoAgPqKxWLDaruVYd3SfCaTqdmHaZpVwdxwOKz29vaG1XYDgYBM0xyBswUAAAAGjqAvAGDIlR6JVC/IW5qvvChjmqai0aji8bja29s1Z86cqqq80WhULpdrBM8IAACMZocccog6Ozt16623Kh6PVz2meObMmXIcR6tXr9bcuXNHsJcAAADA1rNtWz6fT1dffbUefPBBnXbaafJ4PDJNUw8//LAKhYL+53/+R0ceeaT2339/Pf300+ru7pb03rU6HiMOABgqjuMon883VW23NM3n8zX78Xg8VVV14/G4pk6d2rACr8/n4/MNAAAA45bhVD7nHACAreA4jlKpVMMQb1dXV9VFGrfbXVV9t3Iaj8cVDoe5ixoAAGyTYrGom266SZs2bdJ5552nlpYWSe+NWy655BLttddeOuqoo0a4lwAAAMDWs21bpmnqy1/+st5991198IMf1P3336877rhDv/jFL7R69WpddNFFWrx4sUKhkK677jquuQEABqxUzKXZarupVEqWZdXsx+fz1Q3p1qu2GwqFeHIjAAAAUIGgLwCgX7Ztq6enpyrE23taLBbL7b1eb8MQbywWUygU4q5qAAAw5NLptK699lq5XC6de+658vv9kqQ///nP6u7u1rnnnjvCPQQAAAC23W9+8xu1tLToxBNP1KGHHqojjzxS//Ef/yFJuv322/XQQw/pJz/5CSFfAICk977zaRTYbfS6d6TAMAwFAoGmAruldTypEQAAANh67pHuAABg5FmWpUQi0bAabyKRkG3b5faBQKAc2p0zZ05ViDcej8vv9xPkBQAAIy4YDOojH/mIrrvuOv3pT3/SRz7yEblcLs2cOVN333238vm8vF7vSHcTAAAA2CZnn312+drdMcccUxXG+vvf/66NGzc2FfLdsGGDli5dqkWLFhEKBoAxpFAo1K2q26jqbjabrdmHaZpV4dxwOKzJkydXBXUr1/v9fj4rAAAAgGFE0BcAJoBCoVC3Cm9pmkgkqtqHw+FyaHfq1Kk1VXkJxAAAgLGira1Np59+un77299qyZIlWrx4sWbOnCnbtrVmzRrNnj17pLsIAAAAbLNS2Oqkk07S5z73OT333HPad9999Ze//EUXXXSRbNvuN5Bl27aefvppxeNxHXTQQcPRbQBAL47jKJ/PNxXYLU0LhULNfjweT1VIt7W1VdOmTWtYbdfn81HABQAAABjFCPoC45TlONqYsbQ+XdT6dFE9RVuW7chlGgq7TXUE3eoIutUecMnFL+5jXi6Xqwrv9g7yplKpclvDMBSJRBSPx9XS0qJZs2ZVBXljsZjcbj4eAADA2PToo48qHA5rt912K49pdthhBy1evFi33367Jk2apAMOOECBQEBvvfVWTdCXcTQAAADGst13310PPfSQrrzySnV2duryyy/XYYcdVg752ratZDKpSCRSE/zdbrvttP/+++uBBx7QvHnz1Nra2vRxGUcDQH2O4yiTyVQFdeuFdytfW5ZVsx+/318Vzp0yZUrDarvBYFAej2cEzhYAAADAUDGcymc4ARjzuvOWlm3K6rlNWWWt9/57m5LsijaV836XoQVtfu3V5lfM6xrm3qIZpYtAjarxdnV1VT1myTTNqtBu72q8kUhELhd/1wAAYHy65ZZbtHz5cgWDQe29997ad999FYvFJEn33XefHn/8cZ155platmyZ0um0PvGJT0hiHA0AAICJwbZtXXnllfL5fDruuOM0ffr0qvX5fF7/9V//pXg8rrPPPrvf6o6MowFMNJZlNQzsplKpqlBvab731/GGYdQEdBsFdkOhkAKBAN/rAAAAABMcQV9gnMhath5Ym9LyzpwMSQP5j11qv+cknxZuH5LP1fcj3DC4HMdRT09PTTXeyvnKxy653e5yaDcWi1WFeGOxmCKRCI9XAgAAE1pnZ6eefvppLVu2TPl8XvPmzdP73vc+zZgxQ3/605/0xhtvaN9999VTTz2lL37la3p4Q5ZxNAAAACaMt99+W0uWLNG6deu0xx576KijjlIkEimvf/PNN3XTTTfphBNO0D777FN3H1yPBjBeFAqFfivsVr6uLLxS4nK56oZ0G70OBAJ8jwMAAABgQAj6AuPAykRed6xKKl10BnRBtTdDUshtaPHMiHaIegerexNe6XF4lSHe3tPKxzD5fL6G1XhjsZiCwSAXgAAAAJqQz+e1fPlyPfXUU9q0aZMmT56sffbZR88995wSiYQyoUmKHnSCco7JOBoAAAATim3bWrZsmf72t7+pWCzq0EMP1QEHHFCuGHnrrbfq5Zdf1mc/+1lFo9GqbbkeDWC0chxHuVyuYbXdUli3cr6y0EqJ1+utW1W3XrXdYDAor9fL9zYAAAAAhhRBX2CMW7oxo/vWpAZcNaGR0n6OnhbSPu2BQdjj+FcsFpVIJOqGeLu6upRIJKoeyxQMBhtW443H4/L7/SN4NgAAAOOP4zhauXKlnnrqKb366qvy+Xxypu8iY9eDJceRBuHLOMbRAAAAGIsymYwefPBBPf3002ptbdWiRYs0Z84cZTIZ/fKXv9TUqVN15plnlgNsXI8GMJxs21Ymk2mq2m5patt2zX4CgUCfFXZ7L3O73SNwtgAAAADQGEFfYAwrXVQdKlxcfU+hUOizGm8ymaxqH4lEaqrxVk69XqpTAAAAjJQtW7borhdW6u3IjCE7BuNoAAAAjDUbNmzQ3Xffrbfeeks777yzjj32WK1fv15//OMfdeqpp2q33XbjejSAbWZZVp8h3XqVd3szDKNhSLfRctM0R+BsAQAAAGDwEPQFxqiVibz+8EZiyI9zxo7Rcf/YtGw2W1OFt7u7uzxfeSHJMAxFo9GaKrylaTQa5U5vAACAUYxxNAAAAFCf4zh66aWXdO+99yqVSumggw7Sxo0btXr1ap1wzmd065rckPeBcTQwtuTz+aYCu6XXuVztzxG3291UYLc09fv95SrjAAAAADBREPQFxqCsZeual7YoXXQG5fFojRiSQm5D5+/aIp+r8d3O7777rpYsWaJ58+Zp//33H8IeDZzjOEqn0zUh3spp5YUll8vVsBpvPB5XJBLhzm8AAIAxarSNowEAAICRkkqlVCgUFAgE5PV6q0Jz+Xxejz32mB577DEFAgHlLFueIz4i2+1jHA2MY47jKJvNNl1tN5VKqVgs1uzH6/XWrapb73UoFJLH4yG4CwAAAAD9oOwkMAY9sDY15OEESXIkpYqO7l+b0nEzIjXrLcvSY489poceekiTJk3S7Nmzh7hHdfroOEomkzXh3cppoVAot/d4POXQ7vTp07XbbrtVVecNh8NcUAIAABinRss4GgAAABhpV1xxhbLZrKT3nmIWCATk9/sVCATKf+bPn6+1a9cqu/3uKpoeDfVVU8bRwOCybVuZTKYmsFsZ1u09b9t2zX4CgUBVSHe77bZrWG03GAzy1EMAAAAAGAJU9AXGmK6cpate2jLsx71gfotiXld5fsOGDbrlllu0YcMGHXzwwTrssMOG5OKNbdtKJBINq/EmEglZllVu7/f7y6Hdykq8pdeBQIAgLwAAwAQ0WsbRAAAAwGiwZcsWbd68WZlMRplMRtlstuZ1NptV2jGV2/+UYb+myjgaqFUsFhtW2K23LJPJ1OzDNM0+K+z2Du8GAgGecggAAAAAowC3VAJjzPLOrAxpyKuQVTIkLduU1WFTQ7IsS4888ogeeeQRtbW16bzzztPUqVO3et/FYrFhNd6uri4lk0lV3o8QCoXKod2Ojo6aIK/P5xuEMwYAAMB4M9LjaAAAAGA0aWlpUUtLS7/tHnonpSc3ZBhHA4PMcRwVCoWmq+2mUinl8/ma/bjd7qpgbjwe19SpU+tW2w2FQvL5fBRDAQAAAIAxiKAvMIZYjqPnNmX7vKi6cunjevg3V2rty8uV3LRBH7vk15p/xPFVbd598zXdfdn39Oazj8suWpo8eyd97Kc3KL7dtLr7dCQ9tymruUZSt916i9599129//3v16GHHtpvFd98Pl8T3q0M9Pb09FS1j0aj5dDujBkzqkK8sVhMHo+nmbcKAAAAKBuMcXQu3aO7L/u+XnpwidLdW9Q6dYYO+vD52v+0cxruszSOfv92Qbn4IhUAAABjTH/j6Aev/4VevP9ObXzrdXl8Ac3ccz8t+tdvq33WnHKbp27+jZbdfbPeeeV55VI9+vZDKxSIxPo8LuPoiatQKOiBBx7QpEmTtGDBApmmqZ/85Cd69NFHtW7dOh111FG68MILNWnSpJHuag3HcZTNZvussNv7dbFYrNmPz+erCue2t7fXBHYrX3u93hE4WwAAAADAcCPoC4whGzOWslbftRPy2bS222m+9j3pI/rthefUrO98e6WuOvcE7XfSR3XUZ74qXyiiDW++Knc/lXCzlqPr/vgXtfsMnX/++dpuu+3kOI4ymUzdIG9pvvLRUKZpKhqNKh6Pq729XXPmzCmHeOPxuKLRqFwuHscGAACAwTUY4+g7L/m23nj6EZ3xg/9Sy9Tpev2JB3XrxV9VpL1Dux62qOF+s5ajjRlLHUF+/QYAAMDY0t84+s2lj+vA0z+pafMXyLaKuueKH+r6z35IX7z5UXkD71XjzWfT2umghdrpoIW65/IfNH1sxtETi+M4MgxD9957r0488USde+65uuaaa/Sb3/xGr7zyis4//3ztscceOv/88/WrX/1KX//614e8T7Zt162q21fV3cqnE5b0DubG4/GG1XaDwSDfkQAAAAAA6uIKCTCGrE/X3t3d284HH6WdDz6q4fp7r/yRdj74KB33hf8oL5s0fYd+9+s4jtpmz9M0I6UHH3ywHOqtfFSU2+0uB3e322477bLLLlVB3nA4LNM0+z0WAAAAMJgGYxy9+vmntfeJZ2r2vgdLkt536tn6+82/1poXn+0z6Fs6PgEFAAAAjDX9jaM/eeUfq+ZP++7l+uGRu2jtS8u1wz4HSZLe/9HPSJLefOaxrTo+4+iJwTAMbdmyRQ899JAOOeQQTZ48WZL00EMPqVgs6sQTT5QkxePxquIiA1EsFpuutptKpZTNZmv2YZpmVVXdcDisyZMn1w3sBoNBBQIBvhMBAAAAAAwKrpAAY8j6dFGmJHsrt7dtW688ep8O/fjndf1nP6R3Xn1RLdvP0OGf+LeqxxLX5djqsl3SO28rFotp5syZ2mOPPcoh3lgsplAoJINHqQEAAGCU2dZxtCTN2GM/vfzQ3dr3pI8o2t6hN595TJtWv6ETvvz9Prcz1VzQGAAAABhtBjqOziYTkqRArGWbj804euIoVfO99NJLNXv2bO200066//771d3dra9+9au65JJLtPfee6uzs1O77767zj333Lr7+cc//qGurq6G1XYri5aUeDyeqnBua2urpk2bVlOFtzT1+Xx8BwIAAAAAGBEEfYExpKdob1M4IbV5o/LplB664TId89mva9G/fVuvPX6/fnfhOTrvV3/R7H0ObryxYcr2+BUMBjVv3jztuuuu8vv929AbAAAAYHhs6zhakj7wtf+n//3Bl3Txoj1kut0yDFMf/PeflyuVNWJLShW39egAAADA8BvIONq2bd3xs29p5l7vU8ecXbb52IyjJw7DMLR06VIlk0mdddZZevzxxyVJwWBQ2223nfx+v8455xwtWrRIV111lb785S/rj3/8Y02l3CeffFKbNm2qCub2VW03FArJ4/GMxCkDAAAAADBgBH2BMcSynW3a3nHe237Xwxfp/R9775FpU3feXauXP62///nXfQZ9DcOQ2+vTunXr9NZbb+n2229XJBJRa2urWltb5fP55PF45PF45Ha7y6/r/em93uVybdN5AQAAAH3Z1nG0JD3++2v19gtLdfalv1V8u2la+ewTuvXiryna3qE5+x/W57bFQTg+AAAAMNwGMo6+7eKvacMbr+gz198xaMdnHD12WZbVsKru+9///pr29957rzZs2CDbtvXoo4/queee0y9/+Utt2rRJK1eu1GWXXSZJOuOMM/TjH/9YTzzxhA4+uPr7jHPOOYfvGgAAAAAA4xZBX2AMcZnb9kioYLxVptutybN3qlrevsNOWrXsyb43dhxFQkFN23VXZTIZbd68WV1dXVq1apVWr14tn89XvohWKBRUKBTKweL+GIbRZzC4Xjh4oGHiUqCYx2oBAABMPNs6ji5kM7r3ih/qY5fcqHmHHCNJ2m6n+Vr32ot6+DdX9h30dRxt3rRRT256WdFoVLFYTLFYTKFQiLEpAAAARoVisahEIqFEIqHu7m51d3crkUhofXS2FG6X+hm33nrx1/TKI/fqU9feptiUqYPWL/c2juMxeAqFQlVgt/e0d6A3m83W7MPlcikUCunggw+u+l2oUCho48aNeuaZZ3T00UfL5XKps7NT69evVzwe19KlS6vavvbaa5o7d27d/QMAAAAAMF4R9AXGkLDblClt9WOH3R6vpu26QBvfeqNq+abVbyi+3fQ+tzUNQ7O3306LDqy+gLZx40a98MILeuGFF9TV1aUZM2boE5/4hBzHkWVZKhaL5eBvoz+N2lQuT6fTDdvbdvPvyFCHiUvLCW0AAACMHts6jraKRVnFgoxej4Y1TVf/N7c5jlJbNulvz/5NxWKxYluzKvgbjUZr5v1+P+NKAAAAbBPbttXT01MO79abplKpqm0CgYBisZhcoWmSHEn1x6SO4+i2H1+klx64S+dfc4tat585aP02JYXcZr/tMHCO4yiXy9UEdeu9Lk0LhULNfjwej0KhkILBoEKhkFpbWzVt2rTystLy0tTr9db9/cbj8ejnP/+5fv7zn0uSenp6dOSRR+pTn/qUpk2bpjfffFOHHXaYPB6PEomEvvzlL2vy5MlD/j4BAAAAADCaEPQFxpCOoFvLOvtuk0v3qPPtleX5LWtX651XX1Aw2qL4dtN06Nn/ov+56HztsPeBmr3vwXrt8fv1ysP36Pxf3dLnfu3/O35v7e3tWrhwoY444gitWbOmfKe+YRhyu91yu93y+/0DPdUBaSZQ3Nf6ynU9PT0N21uW1XSf+goBD0aYuLTONLnYDQAA0J/BGEfvsM9BWvKL78rjCyi+3TStXPq4nr3zj1r8pe/1vWPT1FH77aU9F+2vTCZTN1hRelJGMpmsuonN4/H0GQSOxWLyeDzb8tYAAABgDHMcp2aM2Xu82XuM6fV6y2PJKVOmaKeddqoZZ5bGmMs2ZXX32z0Nj3/rxV/T8iU366xLfyNfMKzkpg2SJH84Ko8/IElKbtqgZOe76nz7TUnS+tdfki8UVrxjmoKxlob7bnQ9GrVK/w6arbabTqfrXuv2+/1VwdwpU6ZUzVdOg8HgoP8uYtu2TNNUsVjU4sWLVSgU5PF49OMf/1jPPfecWltbNXnyZHV0dAzqcQEAAAAAGAsMp9/yQwBGi/Xpom58tavPNm8+85iu+dTJNcv3PvEMfei7V0iSnrnld3rwhv9U97vr1D5zRx31ma9p18OP6/f45+wcn9AXV23bbjow3Mz6vioZN8vlcjVdZXhrwsSl9Tz2DAAAjGWDMY5Obtqgey7/gV5/8kGlE11q2W6a9vvg2Xr/Rz/Tb9XdZsfRpWprvUMalfONqq2Vwhm9p5FIhLEcAADAGJXL5RpW4S1Nez81oq+xYSwWk8/na/qpEf2No7++d3vd5ad95zLt84EPS5L+etVP9Ldf/bTPNo1M1OvRlmX1W2G38nUmk6l50ohhGDVVdRsFdkOhkAKBAL83AAAAAAAwihH0BcYQy3F0+QublbWG/7+t32Xo87u3ysWjg4ec4zh9hoAHI0xc+tMs0zS3KTTcbOVil8vF46kBAMCgG0/j6GKxqGQyWbdaW2laespGSSQS6bMqcCgUYgwGAAAwzLZ2XFcvvDtU47rxNI4eSfl8vmFgt17V3VwuV7MPl8tVN6RbL7wbCoXk9/sZ4wMAAAAAMI5MvFuhgTHMZRha0ObXkxsyGs5Lq4akBW3+cXFRdSwwDKMcgB1KjuPIsqxBCxOn0+mG2zR7T0np3JsNBm9NmLj0mgvdAABMHONpHO12u9XS0qKWlsaPOe6r8tuGDRtqKr+5XK6qEPC2Vn4DAACY6Lb1SQ0zZsyoCfGOxJMaxtM4erA4jqNcLtdnhd3eQd56T3Dzer1Vgd22trY+q+56vV7G4wAAAAAATGAEfYExZq82v57YkBnWYzr/d1yML4ZhyO12y+12KxAIDOmxegeKBxomrlyWzWYbbmNZVtN9GuowcekPF+ABABgdJtI42ufzqb29Xe3t9R+l7DiOMplM3epxW7Zs0apVq5RIJKpu1vJ6vX0GgaPR6JDfqAYAADAalMZSvUO8ldNkMinbtsvbeDye8rhpypQpmjt3bk2Qd7SOpcb7ONq2bWUymaar7abT6aq/25JAIFAVzt1uu+2qgrq9w7tuN1/PAQAAAACA5hlOs2UWAYwaS1Yn9XxnbliqKBiS9pjk03EzIsNwNGDb2LY9KGHi/rapV4WjEZfL1XQwuL8gcV9tTNMcwncWAIDxgXF080pV6CoDK70r0fWuQhcMBhuGgaPR6IhUoQMAABiofD7f5xio99MRTNOsuQGq97zf7x/TN4OPpXF0sVisW1W3XmA3lUopk6kNMZumWQ7o9q6sW6/abjAY5NocAAAAAAAYUgR9gTEoZ9m65qUtShWdIb24akgKuQ2dv2uLfC4uVAIljuM0FQzuq02zy5tlmuaQh4lLgeKx/MUUAGBiYxw9uIrFYk3opfd8LpcrtzcMQ+FwuM8QTCgUYqwBAACGjGVZNdV3e49hstls1Ta9xy+9p+FweNyPX0ZqHF26BlcZ1K0X3q18XTn+LHG73X2GdHsvH+vBbAAAAAAAMP4Q9AXGqJWJvP7wRmLIj3PGjlHtEPUO+XEA1HIcR5ZlbXWYeCCVi5sdDhiG0VQwuK+wcDNhY5fLxRcqAIAhwTh6eOVyubqPs66cVlbEc7lcVcGZ3kHgWCwmn8/HOAEAANRwHKf8RIJG446enp6qbQKBQMMnEsRiMZ5IUGG4xtFtbz0te+Pb5fBuvSdr+Xy+pgK7panH42H8CAAAAAAAxjSCvsAYtnRjRvetSfXfcCsdMy2kvdsDQ7Z/AKOD4ziybXurw8T11jdqb9t20/1qJhw80OX12vBFDwBMPIyjRw/HcZROp/usCpxMJqtuSvJ6vQ0r6pWmHo9nBM8KAAAMNsdxlM1m+wzxJhKJqusOHo+nz5uHotGovF5uzBqIoR5HR95+Xu3ZTTXB3d6v3W73kPUBAAAAAABgNCLoC4xxpYurhjQoj00r7YdwAoChYFlWwxDwQMPEfW1jWVbTfSoFfvsLB/deP5AwscfjkWmO30e3A8BYxDh67LBtu9/qfKlUdeAkGAz2GeyJRCJ8NgMAMIrk8/maG396TwuFQrm9aZpVn/P1Pu/9fj839w5QsVhUOp0uV9PtPU2n09oUnKzktN0lx5aMbR9PMY4GAAAAAADoH0FfYBxYmcjrzlVJpYrONoUUDEkht6HFMyM8ZhjAmGbb9oCCwVsTJi6ta5bL5Rr0MHG99TxSFACaxzh6/CgWizWBoN7VgXO5XLm9YRiKRCJ9Pqo7GAwSDgIAYBBYllVTsb/3fDabrdomHA73WcE/HA7zOd0Px3GUz+drgrr1Xpem+Xy+Zj8ej6emqm4xPkWrozuoYLilbfh7YBwNAAAAAADQHIK+wDiRtWw9sDal5Z25AVclK7Xfc5JPC7cPyeeishUANMNxnJog8GCHiUt/mmUYxpCHiUuBYr5UBTAeMI6eOHK5XN0gcOW08qkALperpjpg73m/3z+CZwQAwMhzHKdceb9eFd7u7m719PRUbeP3+/v8fI1Go9zEWofjOMpms31W2+29vN4Tj3w+XzmwW5r2DvJWTj0eT93+MI4GAAAAAAAYPgR9gXGmO29p2aasntuUVdZ677+3KcmuaFM573cZWtDm115tfsW8XEAHgNHIcRxZljWgYPDWBJDz+bwGMjQc6jBxaR2BYgDDgXE0HMdROp3usypwMpms+qz0er01jwnvPXW73SN4Vv/0yiuv6J577tGHP/xhTZ48eaS7AwDoQ6FQ0IYNG8qh15FSCpb29dmYSCRk2/8cMXk8nj4r5kejUXm9VG6V3nsaUaPAbqPXvX9nNwxDgUCgJrRbL8hbej3YIWrG0QAAAAAAAEOPoC8wTlmOo40ZS+vTRa1PF5Uq2irajtymoZDbVEfQrY6gW+0Bl1wEqAAA/6cUKN6aMHF/21Wuq1dVqJF6QeBmlw0kbGyaVBDaFitWrNDHPvYxHXfccfrGN74hj8ejn/3sZ3r++edlGIYWLVqkD33oQ6Mm8AY0wjgafbFtW8lkss9Hj6fT6aptgsFgn2GnSCQy5J9BnZ2dOvnkk/XUU0/prrvu0pFHHqlbb71V3//+9+X1etXS0qJvfetbOvDAA4e0HwCAauvWrdNvfvMbnXTSSZo3b55WrFihT3ziE0qn07JtWwceeKB+/OMfKxKJDMnx8/l8TfXd3p9tlU+YMU2zqgJvvWkgEJiwN2sWCoW6VXUbVd3NZrM1+zBNc0DVdv1+/6j5XZZxNAAAAAAAwNDhW3ZgnHIZRvniKQAAzXK5XMPyiFTbtrc6TNx7eTqd7rOScbNcLldT4eCBLu+9frw+gvbnP/+53nrrLUnvVfH63ve+p3feeUdHHnmkTNPU1VdfrdbWVh177LEj21GgH4yj0RfTNMsh3enTp9dtUywWa8JSpenKlSvV3d2tfD5fbm8YhiKRSMPHl8diMQWDwa0OTa1atUqXXnqp9t13X3V0dGj77bfXyy+/rK9//eu64YYbNHv2bP3pT3/S97//fd12223ckAEAw+jnP/+5LrnkEk2ePFnz5s3TRRddpLPPPlvnn3++JCkej+u0007TwoULB7xvy7KUTCbrfh6VpplMpmqbcDhc/uzZcccda25QCYVCoyZUOtQcx1E+n28qsFuaVoaiSzweT1Vgt7W1VdOmTWtYddfn843ZoDTjaAAAAAAAgKHDFRcAAAAMO9M05fV6h/yRrY7j9BsobnZ9NptVMpls2L5ZpmludRXiZsPEpUDxUH9BbNu2TNPUpz/9aS1cuFBdXV3y+/2SpL/97W869thj9fGPf1yS9MMf/rAcBAaA8cztdqu1tVWtra0N22Sz2YaVE9955x0lEomq6vdut7vPCoqTJk2qeyPJunXrdNVVV2n69Ok6+OCD9e///u8KhULauHGjWltbFQ6H1d7erunTp+sf//jHuL0ZBQBGo+XLlyuRSOioo45SLpeTJK1evVqdnZ3q6enRxo0byzdp1JNKpdTV1VU3xNvd3a2enp6q9n6/v/zZMW3atJqbSiKRyLi+2cNxHGUymaqgbr3wbuXrek+i8fv9VeHcKVOmVAV1e4d3PR7PCJwtAAAAAAAAxpvxe+UOAAAAE55hGOXg61ByHEeWZW1zmLhQKJSrRjXaxnGcps+9v2BwvfBwo22mTJlSU1HSNE396U9/km3bOvHEE7VkyRIZhqFCoaDLL79cX/nKV/TYY48pl8vp8MMP14knnjhUfwUAMKb4/X75/X5Nnjy57nrHcZROp+tWYezs7NTKlSuVTCbLnwkf/vCHNXfu3JobPK6++mpNmjRJX/7yl3XnnXdqu+22U0tLi6ZPn66jjjpKhx12mLxer3bffXddd911MgxDjuOM2UqCADAWlH7Ofuc739E3v/lN3XTTTVq9erVyuZxuvvlmffazn9Vll12mZDKp008/veHvMkuWLNE//vEPSe/dEFIK7ra3t5er8VYGeYf6JsvhZllWn4Hd3sszmUzN71KGYdQEdFtbW+sGdkOhkAKBADfFAAAAAAAAYEQQ9AUAAAC2USlU63a7FQgEhuw4juPItu1tDhOX/mSz2YbtbduuOvbpp5+uefPmVS3bsmWLrrrqKl133XUKBoPK5/OaMmW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|
|
"text/plain": [
|
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"<Figure size 3600x2400 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
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"Set parameter Username\n",
|
|
"Set parameter LicenseID to value 2631888\n",
|
|
"Set parameter TimeLimit to value 1200\n",
|
|
"Set parameter LogFile to value \"gurobi.log\"\n",
|
|
"Set parameter Threads to value 20\n",
|
|
"Using license file /home/sfischer/Software/gurobi12.0.1_linux64/gurobi1201/gurobi.lic\n",
|
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"Academic license - for non-commercial use only - expires 2026-03-06\n",
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|
"\n",
|
|
"Gurobi Optimizer version 12.0.1 build v12.0.1rc0 (linux64 - \"Ubuntu 24.04.2 LTS\")\n",
|
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"Copyright (c) 2025, Gurobi Optimization, LLC\n",
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"\n",
|
|
"Read LP format model from file /tmp/30b17f419b0f4284b398e37052664ac5-pulp.lp\n",
|
|
"Reading time = 0.07 seconds\n",
|
|
"OBJ: 123791 rows, 45084 columns, 334004 nonzeros\n",
|
|
"\n",
|
|
"Using Gurobi shared library /home/sfischer/Software/gurobi12.0.1_linux64/gurobi1201/linux64/lib/libgurobi.so.12.0.1\n",
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|
"\n",
|
|
"CPU model: Intel(R) Core(TM) i7-14700KF, instruction set [SSE2|AVX|AVX2]\n",
|
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"Thread count: 28 physical cores, 28 logical processors, using up to 20 threads\n",
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|
"\n",
|
|
"Non-default parameters:\n",
|
|
"TimeLimit 1200\n",
|
|
"Threads 20\n",
|
|
"\n",
|
|
"Optimize a model with 123791 rows, 45084 columns and 334004 nonzeros\n",
|
|
"Model fingerprint: 0x217dfde5\n",
|
|
"Variable types: 2689 continuous, 42395 integer (41835 binary)\n",
|
|
"Coefficient statistics:\n",
|
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" Matrix range [1e+00, 2e+05]\n",
|
|
" Objective range [1e+00, 1e+00]\n",
|
|
" Bounds range [1e+00, 1e+00]\n",
|
|
" RHS range [1e+00, 5e+05]\n",
|
|
"Presolve removed 121060 rows and 44745 columns\n",
|
|
"Presolve time: 0.02s\n",
|
|
"Presolved: 2731 rows, 339 columns, 5778 nonzeros\n",
|
|
"Variable types: 189 continuous, 150 integer (150 binary)\n",
|
|
"Found heuristic solution: objective 102.0000000\n",
|
|
"Performing another presolve...\n",
|
|
"Presolve removed 1064 rows and 91 columns\n",
|
|
"Presolve time: 0.00s\n",
|
|
"\n",
|
|
"Root relaxation: cutoff, 147 iterations, 0.00 seconds (0.00 work units)\n",
|
|
"\n",
|
|
" Nodes | Current Node | Objective Bounds | Work\n",
|
|
" Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n",
|
|
"\n",
|
|
" 0 0 cutoff 0 102.00000 102.00000 0.00% - 0s\n",
|
|
"\n",
|
|
"Explored 1 nodes (147 simplex iterations) in 0.05 seconds (0.13 work units)\n",
|
|
"Thread count was 20 (of 28 available processors)\n",
|
|
"\n",
|
|
"Solution count 1: 102 \n",
|
|
"\n",
|
|
"Optimal solution found (tolerance 1.00e-04)\n",
|
|
"Best objective 1.020000000000e+02, best bound 1.020000000000e+02, gap 0.0000%\n",
|
|
"\n",
|
|
"Wrote result file '/tmp/30b17f419b0f4284b398e37052664ac5-pulp.sol'\n",
|
|
"\n",
|
|
"objective: 102.0\n",
|
|
"[(0, 4), (4, 10), (4, 11), (11, 17), (10, 11), (17, 35), (35, 53), (53, 62), (62, 53)]\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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|
|
"text/plain": [
|
|
"<Figure size 3600x2400 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"def Graph2LinProgDynamicMap():\n",
|
|
" import pulp\n",
|
|
" import importlib\n",
|
|
" import LinProg_lib\n",
|
|
" importlib.reload(LinProg_lib)\n",
|
|
" from LinProg_lib import draw_networkx_graph,combine_nodes\n",
|
|
" # ======================================================================\n",
|
|
" # Parameters of the NoC (how many PEs are available)\n",
|
|
" # ======================================================================\n",
|
|
"\n",
|
|
" mesh_x=4\n",
|
|
" mesh_y=4\n",
|
|
" router_num=mesh_x*mesh_y\n",
|
|
"\n",
|
|
" noc_factor=param1\n",
|
|
" comp_factor=param2\n",
|
|
" # noc_factor=0.5\n",
|
|
" # comp_factor=0.1\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Creating/importing the input Task graph \n",
|
|
" # ======================================================================\n",
|
|
" \n",
|
|
" # G = CreateNetworkXGraph(Figure=False,flit_delay_consistent=True,detailed=False)\n",
|
|
" # G =CreateNetworkXGraphManuelParallel()\n",
|
|
"\n",
|
|
" G=CreateNetworkXGraph()\n",
|
|
"\n",
|
|
"\n",
|
|
" for node, atr in G.nodes(data=True):\n",
|
|
" G.nodes[node]['delay_send'] = math.ceil(atr['delay_send'] * noc_factor)\n",
|
|
" G.nodes[node]['delay_comp'] = math.ceil(atr['delay_comp'] * comp_factor)\n",
|
|
"\n",
|
|
"\n",
|
|
" for u, v, attrs in G.edges(data=True):\n",
|
|
" print(f\"Edge from {u} to {v}: {attrs}\")\n",
|
|
"\n",
|
|
"\n",
|
|
" for u, v, attrs in G.edges(data=True):\n",
|
|
" G[u][v]['flits'] = math.ceil(attrs['flits'] * noc_factor)\n",
|
|
"\n",
|
|
" for u, v, attrs in G.edges(data=True):\n",
|
|
" print(f\"Edge from {u} to {v}: {attrs}\")\n",
|
|
"\n",
|
|
" # Draw the graph\n",
|
|
" plt.figure(figsize=(32, 14))\n",
|
|
" pos = nx.spring_layout(G) # Layout for positioning\n",
|
|
" nx.draw(G, pos, with_labels=True, node_color=\"lightblue\", edge_color=\"black\", arrows=True, node_size=800, font_size=12)\n",
|
|
"\n",
|
|
" # Draw edge labels (if any)\n",
|
|
" edge_labels = {(u, v): f\"{d.get('flits', '')}\" for u, v, d in G.edges(data=True)}\n",
|
|
" nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=10)\n",
|
|
"\n",
|
|
" plt.title(\"NetworkX Graph Visualization\")\n",
|
|
" plt.show()\n",
|
|
" # G.add_edge(7, 3, flits=10)\n",
|
|
" # G.add_edge(7, 3, flits=10)\n",
|
|
" draw_networkx_graph(G)\n",
|
|
" \n",
|
|
" # ======================================================================\n",
|
|
" # Setup of LP model and initialization of variables\n",
|
|
" # ======================================================================\n",
|
|
"\n",
|
|
" model = LpProblem(name='small-problem', sense=LpMinimize)\n",
|
|
" \n",
|
|
" task_vars = [{} for _ in G.nodes]\n",
|
|
" task_total_delay_router = [{},{},{},{},{},{},{},{}] \n",
|
|
" task_total_delay_router = [{} for _ in range(router_num)]\n",
|
|
" task_total_memory_router = [{},{},{},{},{},{},{},{}] \n",
|
|
" task_total_memory_router = [{} for _ in range(router_num)]\n",
|
|
"\n",
|
|
" PE_var = {}\n",
|
|
" PE_delay = {}\n",
|
|
" PE_delay_min = {}\n",
|
|
" PE_delay_max = {}\n",
|
|
" PE_mem = {}\n",
|
|
" PE_partial = [{} for _ in range(router_num)]\n",
|
|
" PE_mem_overflow = {}\n",
|
|
" task_constraints = [] \n",
|
|
"\n",
|
|
" task_map={}\n",
|
|
"\n",
|
|
" same_map={}\n",
|
|
" same_map_and={}\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Mapping variables of tasks (is this task mapped to this PE/router) \n",
|
|
" # ======================================================================\n",
|
|
" for router in range(router_num):\n",
|
|
" for task in G.nodes:\n",
|
|
" if task not in task_map:\n",
|
|
" task_map[task] = {}\n",
|
|
" task_map[task][f\"t_map_{router}\"]=LpVariable(name=f't_{task}_map_{router}', cat=\"Binary\")\n",
|
|
"\n",
|
|
"\n",
|
|
" # for task in G.nodes:\n",
|
|
" # assigned_router = task // 5 # Integer division: 0-4→0, 5-9→1, etc.\n",
|
|
" # for router in range(router_num):\n",
|
|
" # if router == assigned_router:\n",
|
|
" # task_map[task][f\"t_map_{router}\"].setInitialValue(1)\n",
|
|
" # task_map[task][f\"t_map_{router}\"].fixValue()\n",
|
|
" # else:\n",
|
|
" # task_map[task][f\"t_map_{router}\"].setInitialValue(0)\n",
|
|
" # task_map[task][f\"t_map_{router}\"].fixValue() \n",
|
|
"\n",
|
|
" # 5x optimal mapping\n",
|
|
" manual_task_groups = [\n",
|
|
" [0, 1, 2, 3],\n",
|
|
" [10, 8, 9],\n",
|
|
" [11, 12, 13, 14, 15, 16, 7],\n",
|
|
" [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],\n",
|
|
" [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52],\n",
|
|
" [4, 5, 6],\n",
|
|
" [53, 54, 55, 56, 57, 58, 59, 60, 61, 65, 66, 67, 68, 69],\n",
|
|
" [62, 63, 64]\n",
|
|
" ]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # manual_task_groups = [\n",
|
|
" # list(range(0, 16)), # 16 elements: 0-15\n",
|
|
" # list(range(16, 20)), # 4 elements: 16-19\n",
|
|
" # list(range(20, 24)), # 4 elements: 20-23\n",
|
|
" # list(range(24, 28)), # 4 elements: 24-27\n",
|
|
" # list(range(28, 32)), # 4 elements: 28-31\n",
|
|
" # list(range(32, 36)), # 4 elements: 32-35\n",
|
|
" # list(range(36, 40)), # 4 elements: 36-39\n",
|
|
" # list(range(40, 44)), # 4 elements: 40-43\n",
|
|
" # list(range(44, 48)), # 4 elements: 44-47\n",
|
|
" # list(range(48, 52)), # 4 elements: 48-51\n",
|
|
" # list(range(52, 56)), # 4 elements: 52-55\n",
|
|
" # list(range(56, 60)), # 4 elements: 56-59\n",
|
|
" # list(range(60, 64)), # 4 elements: 60-63\n",
|
|
" # list(range(64, 68)), # 4 elements: 64-67\n",
|
|
" # list(range(68, 70)), # 2-4 elements: 68-71 (adjust if you only want up to 70: use range(68, 71))\n",
|
|
" # ]\n",
|
|
"\n",
|
|
" # Assign each group to a router index\n",
|
|
" for router, task_group in enumerate(manual_task_groups):\n",
|
|
" for task in G.nodes:\n",
|
|
" if task in task_group:\n",
|
|
" task_map[task][f\"t_map_{router}\"].setInitialValue(1)\n",
|
|
" task_map[task][f\"t_map_{router}\"].fixValue()\n",
|
|
" else:\n",
|
|
" task_map[task][f\"t_map_{router}\"].setInitialValue(0)\n",
|
|
" task_map[task][f\"t_map_{router}\"].fixValue()\n",
|
|
" # # ======================================================================\n",
|
|
" # Number of tasks per router\n",
|
|
" # ======================================================================\n",
|
|
" # for router in range(router_num):\n",
|
|
" # task_router_count = LpVariable(name=f'task_router_count_{router}', lowBound=0, cat=\"Integer\")\n",
|
|
"\n",
|
|
"\n",
|
|
" # for router in range(router_num):\n",
|
|
" # task_router_count = LpVariable(name=f'task_router_count_{router}', lowBound=0, cat=\"Integer\")\n",
|
|
" # task_constraints.append(task_router_count >= lpSum(task_map[task][f\"t_map_{router}\"] for task in G.nodes))\n",
|
|
"\n",
|
|
" # num_tasks = len(G.nodes)\n",
|
|
" # task_constraints.append(task_router_count <= math.ceil(num_tasks/16))\n",
|
|
"\n",
|
|
" # print(f\"max tasks per router: {math.ceil(num_tasks/16)}\")\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Are two tasks mapped to the same PE/Router?\n",
|
|
" # ======================================================================\n",
|
|
" for task1 in G.nodes:\n",
|
|
" for task2 in G.nodes:\n",
|
|
" if task2>=task1:\n",
|
|
" if task1 not in same_map:\n",
|
|
" same_map[task1] = {}\n",
|
|
" \n",
|
|
" if task1 not in same_map_and:\n",
|
|
" same_map_and[task1] = {}\n",
|
|
"\n",
|
|
" # bool: are task 1 and task 2 in the same creating variable\n",
|
|
" same_map[task1][f\"same_{task2}\"]=LpVariable(name=f'same_{task1}_{task2}', cat=\"Binary\")\n",
|
|
" for same_and in range(router_num):\n",
|
|
" same_map_and[task1][f\"same_{task2}_and_{same_and}\"]=LpVariable(name=f'same_{task1}_{task2}_and_{same_and}', cat=\"Binary\")\n",
|
|
"\n",
|
|
" # AND of task1_in_routerX and task2_in_routerX \n",
|
|
" for task1 in G.nodes:\n",
|
|
" for task2 in G.nodes:\n",
|
|
" if task2>task1:\n",
|
|
" for same_and in range(router_num):\n",
|
|
" task_constraints.append(same_map_and[task1][f\"same_{task2}_and_{same_and}\"] <= task_map[task1][f\"t_map_{same_and}\"])\n",
|
|
" task_constraints.append(same_map_and[task1][f\"same_{task2}_and_{same_and}\"] <= task_map[task2][f\"t_map_{same_and}\"])\n",
|
|
" task_constraints.append(same_map_and[task1][f\"same_{task2}_and_{same_and}\"] >= task_map[task2][f\"t_map_{same_and}\"]+task_map[task1][f\"t_map_{same_and}\"]-1)\n",
|
|
"\n",
|
|
" # Combine all ANDs to check if two tasks are in the same PE/Router\n",
|
|
" for task1 in G.nodes:\n",
|
|
" for task2 in G.nodes:\n",
|
|
" if task2 > task1:\n",
|
|
" # OR condition: If any same_map_and[task1][task2]_X is 1, then same_map[task1][task2] must be 1 There should only be one and being one\n",
|
|
" task_constraints.append(same_map[task1][f\"same_{task2}\"] == lpSum(same_map_and[task1][f\"same_{task2}_and_{same_and}\"] for same_and in range(router_num)))\n",
|
|
"\n",
|
|
" if task2==task1:\n",
|
|
" task_constraints.append(same_map[task1][f\"same_{task2}\"] == 1)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Constraints + dependencies of the task graph\n",
|
|
" # ======================================================================\n",
|
|
"\n",
|
|
" t_send_list=[]\n",
|
|
" # Define Variables and Constraints for each task\n",
|
|
" for task in G.nodes:\n",
|
|
" t_comp = LpVariable(name=f't_comp_{task}', lowBound=0)\n",
|
|
" t_send = LpVariable(name=f't_send_{task}', lowBound=0)\n",
|
|
" t_mem = LpVariable(name=f't_mem_{task}', lowBound=0)\n",
|
|
"\n",
|
|
" t_start = LpVariable(name=f't{task}_start', lowBound=0)\n",
|
|
" t_end = LpVariable(name=f't{task}_end ', lowBound=0)\n",
|
|
" t_send_list.append(t_send)\n",
|
|
" task_vars[task] = {'t_start': t_start, 't_end': t_end, 't_comp': t_comp, 't_send': t_send, 't_mem': t_mem}\n",
|
|
"\n",
|
|
" for task, atr in G.nodes(data=True):\n",
|
|
"\n",
|
|
" # Every task can only be mapped to one router\n",
|
|
" constraint = lpSum(task_map[task][f\"t_map_{router}\"] for router in range(router_num)) == 1\n",
|
|
" task_constraints.append(constraint)\n",
|
|
"\n",
|
|
" predecessors = list(G.predecessors(task))\n",
|
|
"\n",
|
|
"\n",
|
|
" # remove memory delay as it is saved in PE\n",
|
|
" # task_constraints.append(task_vars[task][\"t_start\"] + task_vars[task][\"t_comp\"] <= task_vars[task][\"t_end\"])\n",
|
|
" task_constraints.append(task_vars[task][\"t_start\"] + task_vars[task][\"t_comp\"] +task_vars[task]['t_send'] <= task_vars[task][\"t_end\"])\n",
|
|
"\n",
|
|
" # Predecessor constraints\n",
|
|
" for pre in predecessors:\n",
|
|
" # task_constraints.append(task_vars[pre]['t_end'] + task_vars[pre]['t_send'] <= task_vars[task][\"t_start\"])\n",
|
|
" task_constraints.append(task_vars[pre]['t_end']<= task_vars[task][\"t_start\"])\n",
|
|
" # Constraint for first task\n",
|
|
" if task==0:\n",
|
|
" # remove memory delay as it is saved in PE\n",
|
|
" # model += task_vars[rep][0]['t_comp'] + task_vars[rep][0]['t_mem'] <= task_vars[rep][0]['tR0_end']\n",
|
|
" model += task_vars[0]['t_comp'] <= task_vars[0]['t_end']\n",
|
|
" model += 0 <= task_vars[0]['t_start']\n",
|
|
"\n",
|
|
" # Static time constraints\n",
|
|
" task_constraints.append(task_vars[task][\"t_comp\"] == atr['delay_comp'])\n",
|
|
" task_constraints.append(task_vars[task][\"t_mem\"] == atr['delay_mem'])\n",
|
|
"\n",
|
|
" #total t_send\n",
|
|
" t_send_total = LpVariable(name=f't_send_total', lowBound=0)\n",
|
|
" # task_constraints.append(t_send_total<=110)\n",
|
|
" task_constraints.append(t_send_total >= lpSum(t_send_list))\n",
|
|
"\n",
|
|
" M = 25e4\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # If the successor of a task is in the same PE the sending delay is 0/the normal sending delay condition does not apply\n",
|
|
" # ======================================================================\n",
|
|
" time_per_flit=0.649\n",
|
|
" for task, atr in G.nodes(data=True):\n",
|
|
" successors = list(G.successors(task))\n",
|
|
" if successors:\n",
|
|
" task_constraints.append(task_vars[task]['t_send'] >= 0)\n",
|
|
" task_constraints.append(task_vars[task]['t_send'] +M*(same_map[task][f\"same_{successors[0]}\"])>= math.ceil(atr['delay_send']*time_per_flit))\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Only one task can execute in one PE at the same time. If tasks are dependent on each other this is automatically satisfied\n",
|
|
" # ======================================================================\n",
|
|
" nodes = list(G.nodes)\n",
|
|
" for i, task1 in enumerate(nodes):\n",
|
|
" for task2 in nodes[i+1:]:\n",
|
|
" if (not(nx.has_path(G, task1, task2) or nx.has_path(G, task2,task1))):\n",
|
|
"\n",
|
|
" \n",
|
|
" # task1_idx, task1_rep = map(int, task1.split('_'))\n",
|
|
" # task2_idx, task2_rep = map(int, task2.split('_'))\n",
|
|
"\n",
|
|
" if f\"same_{task1}\" in same_map[task2]:\n",
|
|
" temp=same_map[task2][f\"same_{task1}\"]\n",
|
|
" elif f\"same_{task2}\" in same_map[task1]:\n",
|
|
" temp=same_map[task1][f\"same_{task2}\"]\n",
|
|
" else:\n",
|
|
" print(task1,task2)\n",
|
|
" print(\"NO combinatnion exists\")\n",
|
|
"\n",
|
|
" order_choose_var=LpVariable(name=f'order_choose_{task1}_{task2}', cat=\"Binary\")\n",
|
|
" task_constraints.append(task_vars[task1]['t_end'] <= task_vars[task2]['t_start']+ M*(1-order_choose_var) + M*(1-temp))\n",
|
|
" task_constraints.append(task_vars[task2]['t_end'] <= task_vars[task1]['t_start']+ M*(order_choose_var) + M*(1-temp))\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Add memory constraints: if memory exceeds the PE capacity, add delay because memory must be fetched from main mem\n",
|
|
" # ======================================================================\n",
|
|
"\n",
|
|
" # Get the usage time of each PE\n",
|
|
" max_router_delay=LpVariable(name=f'max_router_delay', lowBound=0)\n",
|
|
" max_memory_overflow=LpVariable(name=f'max_memory_overflow', lowBound=0)\n",
|
|
"\n",
|
|
" # max_memory=200 #bytes\n",
|
|
" max_memory=0 #bytes\n",
|
|
" delay_per_flit_mem=0\n",
|
|
" for router in range(router_num):\n",
|
|
" PE_var[router]=LpVariable(name=f'PE{router}_total_delay', lowBound=0)\n",
|
|
" PE_mem_overflow[router]=LpVariable(name=f'PE_mem_overflow{router}', lowBound=0)\n",
|
|
" \n",
|
|
" PE_mem[router]=LpVariable(name=f'PE{router}_total_memory', lowBound=0)\n",
|
|
"\n",
|
|
" sum_list=[]\n",
|
|
" sum_list_mem=[]\n",
|
|
" for task in G.nodes:\n",
|
|
" task= int(task)\n",
|
|
" # task_idx, task_rep = map(int, task.split('_'))\n",
|
|
" task_total_delay_router[router][task]=LpVariable(name=f'task_total_delay_router_router{router}_task{task}', lowBound=0)\n",
|
|
" task_constraints.append(task_total_delay_router[router][task]>=task_vars[task]['t_end']-task_vars[task]['t_start']+task_vars[task]['t_send'] - M*(1-task_map[task][f\"t_map_{router}\"]))\n",
|
|
" sum_list.append(task_total_delay_router[router][task])\n",
|
|
"\n",
|
|
" task_total_memory_router[router][task]=LpVariable(name=f'task_total_memory_router_router{router}_task{task}', lowBound=0)\n",
|
|
" task_constraints.append(task_total_memory_router[router][task]>=task_vars[task]['t_mem'] - M*(1-task_map[task][f\"t_map_{router}\"]))\n",
|
|
" sum_list_mem.append(task_total_memory_router[router][task])\n",
|
|
" PE_partial[router][task]=list(sum_list_mem)\n",
|
|
" task_constraints.append(PE_mem_overflow[router]>=lpSum(sum_list_mem) -max_memory)\n",
|
|
"\n",
|
|
" # task_constraints.append(PE_mem_overflow[router]>=lpSum(sum_list_mem))\n",
|
|
"\n",
|
|
"\n",
|
|
" task_constraints.append(PE_var[router] >= lpSum(sum_list) + PE_mem_overflow[router]*delay_per_flit_mem) #total delay per router\n",
|
|
" # task_constraints.append(PE_var[router] >= lpSum(sum_list)) #total delay per router no memory\n",
|
|
" task_constraints.append(PE_mem[router] >= lpSum(sum_list_mem)) #total memory per router\n",
|
|
"\n",
|
|
"\n",
|
|
" task_constraints.append(max_memory_overflow>=PE_mem_overflow[router]) #max memory overflow per router\n",
|
|
"\n",
|
|
" # task_constraints.append(PE_mem_overflow[router]<=5311.0) # calculated as max memory overflow\n",
|
|
" \n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # task_constraints.append(max_router_delay>=PE_var[router])\n",
|
|
" \n",
|
|
" # enforce optimal PE delay\n",
|
|
" # task_constraints.append(PE_var[router] <= 1010) # Slightly stricter bound\n",
|
|
"\n",
|
|
" # PE_var[router].bounds(0, 3248) # Set the max delay per router to the optimal one\n",
|
|
" # PE_var[router].bounds(0, 3250) # Set the max delay per router to the optimal one\n",
|
|
" # PE_mem[router].bounds(0, 20) # Set the max memory per router to a random number\n",
|
|
"\n",
|
|
" \n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # optimization goal for max throughput is to minimize the max delay(start-end time of all tasks) of the router/PE\n",
|
|
" # ======================================================================\n",
|
|
" # make throughput max-min not sum(delays)\n",
|
|
"\n",
|
|
" PE_delay[router]=LpVariable(name=f'PE{router}_total_time_delay', lowBound=0)\n",
|
|
" PE_delay_min[router]=LpVariable(name=f'PE{router}_time_delay_min', lowBound=0)\n",
|
|
" PE_delay_max[router]=LpVariable(name=f'PE{router}_time_delay_max', lowBound=0)\n",
|
|
"\n",
|
|
" for i, task1 in enumerate(nodes):\n",
|
|
" # task_vars[task1]['t_end'] <= task_vars[task2]['t_start']\n",
|
|
" # task_vars[task]['t_mem'] - M*(1-task_map[task][f\"t_map_{router}\"])\n",
|
|
" \n",
|
|
"\n",
|
|
" ## ADDED THE OVEFRFLOW HERE\n",
|
|
" task_constraints.append(PE_delay_max[router]>=task_vars[task1]['t_end'] - M*(1-task_map[task1][f\"t_map_{router}\"])+PE_mem_overflow[router]*delay_per_flit_mem)\n",
|
|
" task_constraints.append(PE_delay_min[router]<=task_vars[task1]['t_start'] + M*(1-task_map[task1][f\"t_map_{router}\"]))\n",
|
|
"\n",
|
|
" task_constraints.append(PE_delay[router]==PE_delay_max[router]-PE_delay_min[router])\n",
|
|
"\n",
|
|
"\n",
|
|
" # max router delay is now dependent on the actual time (max-min) of the tasks in the PE\n",
|
|
" task_constraints.append(max_router_delay>=PE_delay[router])\n",
|
|
"\n",
|
|
" # task_constraints.append(PE_delay[router] <= 1943*1) #strictly enforce some/optimal delay/throughput 2096\n",
|
|
" # task_constraints.append(PE_delay[router] <= 2096*1.2) #strictly enforce some/optimal delay/throughput\n",
|
|
"\n",
|
|
" # task_constraints.append(PE_delay[router] <= 4098*1) #34 layers inception \n",
|
|
" # task_constraints.append(PE_delay[router] <= 7725*1) #94 layers inception \n",
|
|
" # task_constraints.append(PE_delay[router] <= 4683*1) #94 layers inception reduced add\n",
|
|
"\n",
|
|
" # task_constraints.append(PE_delay[router] <= 4016*1) #6 layers inception \n",
|
|
"\n",
|
|
" # task_constraints.append(PE_delay[router] <= 2247*1.001) #52 layers inception \n",
|
|
" # task_constraints.append(PE_delay[router] <= 2697*1.01) #70 layers inception \n",
|
|
" # task_constraints.append(PE_delay[router] <= 862*1) #70 layers inception fast gemmini \n",
|
|
" # task_constraints.append(PE_delay[router] <= 6009*1) #70 layers inception fast gemmini and memory with zero size \n",
|
|
" # task_constraints.append(PE_delay[router] <= 5633*1) #70 layers inception fast gemmini and memory with zero size \n",
|
|
" # task_constraints.append(PE_delay[router] <= 613*1) #70 layers inception fast gemmini and no memory\n",
|
|
" \n",
|
|
" # task_constraints.append(PE_delay[router] <= 10590) #all layers inception \n",
|
|
"\n",
|
|
" # task_constraints.append(PE_delay[router] <= 20615) #all layers inception \n",
|
|
"\n",
|
|
" # task_constraints.append(PE_delay[router] <= 243) \n",
|
|
" \n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Add constraints to the model\n",
|
|
" # ======================================================================\n",
|
|
"\n",
|
|
" for constraint in task_constraints:\n",
|
|
" model += constraint\n",
|
|
"\n",
|
|
" # ======================================================================\n",
|
|
" # Set the objective function and solve the model\n",
|
|
" # ======================================================================\n",
|
|
"\n",
|
|
" # Objective function\n",
|
|
" # model += task_vars[18]['t_end']\n",
|
|
" model += max_router_delay\n",
|
|
"\n",
|
|
" #optimize: minimize the traffic I have by minimizing t_send_total while having optimal time per PE\n",
|
|
" # model+=t_send_total\n",
|
|
" # exit()\n",
|
|
" # # Solve the problem\n",
|
|
" # status = model.solve(PULP_CBC_CMD(threads=20))\n",
|
|
"\n",
|
|
"\n",
|
|
" # model += max_memory_overflow # Minimize the memory per router\n",
|
|
"\n",
|
|
" status = model.solve(pulp.GUROBI_CMD(threads=20, timeLimit=1200))\n",
|
|
" \n",
|
|
" # ======================================================================\n",
|
|
" # print debug information\n",
|
|
" # ======================================================================\n",
|
|
" # G=combine_nodes(G, 8, 14,14)\n",
|
|
" \n",
|
|
"\n",
|
|
" print(f\"objective: {model.objective.value()}\")\n",
|
|
"\n",
|
|
" import os\n",
|
|
" import pickle\n",
|
|
" import csv\n",
|
|
" \n",
|
|
" # Set your subfolder and filename here\n",
|
|
" subfolder = \"inception70/Opt_noChange_5x_fasterLink\"\n",
|
|
" filename = f\"No_mem_opt_5x_NoC{noc_factor}_Comp{comp_factor}.pkl\"\n",
|
|
"\n",
|
|
" # Create the full path\n",
|
|
" results_dir = f\"/home/sfischer/Documents/projects/wk_LinProg/optimization_results/{subfolder}\"\n",
|
|
" os.makedirs(results_dir, exist_ok=True) # Create subfolder if it doesn't exist\n",
|
|
"\n",
|
|
" # Save the model\n",
|
|
" with open(os.path.join(results_dir, filename), \"wb\") as f:\n",
|
|
" pickle.dump(model, f)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" with open(\"Task_graph.pkl\", \"wb\") as f:\n",
|
|
" pickle.dump(G, f)\n",
|
|
"\n",
|
|
" nx.write_graphml(G, \"Task_graph.graphml\")\n",
|
|
"\n",
|
|
"\n",
|
|
" csv_path = os.path.join(results_dir, \"resultsLinProg.csv\")\n",
|
|
" row = [noc_factor, comp_factor, model.objective.value()]\n",
|
|
" write_header = not os.path.exists(csv_path)\n",
|
|
" with open(csv_path, mode='a', newline='') as csvfile:\n",
|
|
" writer = csv.writer(csvfile)\n",
|
|
" if write_header:\n",
|
|
" writer.writerow([\"noc_factor\", \"comp_factor\", \"objective_value\"])\n",
|
|
" writer.writerow(row)\n",
|
|
"\n",
|
|
"\n",
|
|
" # Combine nodes and create a new graph + graph figure\n",
|
|
" import re\n",
|
|
" mappings = {}\n",
|
|
"\n",
|
|
" for v in model.variables():\n",
|
|
"\n",
|
|
" match = re.match(r't_(\\d+)_map_(\\d+)\\s*=\\s*(\\d+\\.\\d+)', f\"{v.name} = {v.varValue}\\n\")\n",
|
|
" if match:\n",
|
|
" task, pe, value = match.groups()\n",
|
|
" if float(value) > 0.9 and float(value) < 1.1:\n",
|
|
" pe = int(pe)\n",
|
|
" if pe not in mappings:\n",
|
|
" mappings[pe] = []\n",
|
|
" mappings[pe].append(int(task))\n",
|
|
"\n",
|
|
"\n",
|
|
" with open(\"CombinedMapping.txt\", \"w\") as f:\n",
|
|
" for router, tasks in mappings.items():\n",
|
|
" f.write(f\"Router {tasks[0]}: Tasks {tasks}\\n\")\n",
|
|
" for router, tasks in mappings.items():\n",
|
|
" for task in tasks:\n",
|
|
" if task != tasks[0]:\n",
|
|
" G=combine_nodes(G, tasks[0], task,tasks[0])\n",
|
|
"\n",
|
|
" print(G.edges)\n",
|
|
" draw_networkx_graph(G)\n",
|
|
"\n",
|
|
" \n",
|
|
"\n",
|
|
" with open(\"PE_graph.pkl\", \"wb\") as f:\n",
|
|
" pickle.dump(G, f)\n",
|
|
"\n",
|
|
" nx.write_graphml(G, \"PE_graph.graphml\")\n",
|
|
"\n",
|
|
" \n",
|
|
"\n",
|
|
" # for task in G.nodes:\n",
|
|
" # for router in range(router_num):\n",
|
|
" # var_name = f't_{task}_map_{router}'\n",
|
|
" # var_value = task_map[task][f\"t_map_{router}\"].varValue\n",
|
|
" # if float(var_value) > 0.7 and float(var_value) < 1.3: # Check if the task is mapped to this router\n",
|
|
" # mapped_tasks_per_router[router].append(task)\n",
|
|
"\n",
|
|
" # # Print the results\n",
|
|
" # for router, tasks in mapped_tasks_per_router.items():\n",
|
|
" # print(f\"Router {router}: Tasks {tasks}\")\n",
|
|
" # # # Open a file to write the variable values\n",
|
|
" # with open(\"/home/sfischer/Documents/projects/wk_hybridNoC_VHDL/Python/gen_vhdl_from_task/temp.txt\", \"w\") as f:\n",
|
|
" # for v in model.variables():\n",
|
|
" # f.write(f\"{v.name} = {v.varValue}\\n\")\n",
|
|
"\n",
|
|
" # f.write(\"\\nConstraints:\\n\")\n",
|
|
" \n",
|
|
" # # Print all constraints in their final form\n",
|
|
" # for name, constraint in model.constraints.items():\n",
|
|
" # f.write(f\"{name}: {constraint}\\n\")\n",
|
|
" # print(f\"Solver Status: {LpStatus[model.status]}\")\n",
|
|
"\n",
|
|
" # # # Print the start and end times of all tasks\n",
|
|
" # # for rep in range(repetitions):\n",
|
|
" # # print(f\"Repetition {rep}:\")\n",
|
|
" # for task in G.nodes:\n",
|
|
" # start_time = task_vars[task]['t_start'].varValue\n",
|
|
" # end_time = task_vars[task]['t_end'].varValue\n",
|
|
" # print(f\"Task {task}: Start Time = {start_time}, End Time = {end_time}\")\n",
|
|
" # print(f\"Max Router Delay: {max_router_delay.varValue}\")\n",
|
|
"\n",
|
|
" # # Print the total delay for each PE\n",
|
|
" # for router in range(len(PE_delay)):\n",
|
|
" # print(f\"PE {router} Total Delay: {PE_delay[router].varValue}\")\n",
|
|
" # print(f\"PE {router} Max Delay: {PE_delay_max[router].varValue}\")\n",
|
|
" # print(f\"PE {router} Min Delay: {PE_delay_min[router].varValue}\")\n",
|
|
" # # for i, task1 in enumerate(nodes):\n",
|
|
" # print(f\"Total t_send: {t_send_total.varValue}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" Graph2LinProgDynamicMap()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"anaconda-cloud": {},
|
|
"kernelspec": {
|
|
"display_name": "env",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|