diff --git a/.gitignore b/.gitignore
index 4c1bda0..5ba6547 100644
--- a/.gitignore
+++ b/.gitignore
@@ -179,4 +179,9 @@ ipython_config.py
-temp.py
\ No newline at end of file
+temp.py
+temp.txt
+simulation/scripts/ChangeRepData.py
+simulation/scripts/MESI_Analysis_Gemmini.py
+simulation/results/log_raw
+simulation/results/LinProgTest_rep12_link576_minTrafficWithLowThroughput_scratch10000_comp1
diff --git a/LinProg_Scripts/task_graph_feedback.pkl b/LinProg_Scripts/task_graph_feedback.pkl
new file mode 100644
index 0000000..3263fb3
Binary files /dev/null and b/LinProg_Scripts/task_graph_feedback.pkl differ
diff --git a/README.md b/README.md
index 4ddf58e..4ae838c 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,6 @@
# wk_LinProg
-Linear Programming optimization of task scheduling and mapping
\ No newline at end of file
+Linear Programming optimization of task scheduling and mapping
+
+
+XML examples symbolically linked to gem5_Sysxeleratorv2 directory
\ No newline at end of file
diff --git a/XML/LinProg_test/data.xml b/XML/LinProg_test/data.xml
index 0f7a29f..6681308 100644
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diff --git a/XML/LinProg_test/map.xml b/XML/LinProg_test/map.xml
index 3586c6b..7fc5d67 100644
--- a/XML/LinProg_test/map.xml
+++ b/XML/LinProg_test/map.xml
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diff --git a/XML/LinProg_test/map_addition.xml b/XML/LinProg_test/map_addition.xml
index ba9d6ac..fff0f0e 100644
--- a/XML/LinProg_test/map_addition.xml
+++ b/XML/LinProg_test/map_addition.xml
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diff --git a/XML/LinProg_test/map_addition_old.xml b/XML/LinProg_test/map_addition_old.xml
index f672074..fff0f0e 100644
--- a/XML/LinProg_test/map_addition_old.xml
+++ b/XML/LinProg_test/map_addition_old.xml
@@ -6,75 +6,135 @@
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diff --git a/XML_Scripts/Graph2XML.ipynb b/XML_Scripts/Graph2XML.ipynb
index fc884e0..2b532e1 100644
--- a/XML_Scripts/Graph2XML.ipynb
+++ b/XML_Scripts/Graph2XML.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": 280,
"metadata": {},
"outputs": [
{
@@ -16,6 +16,7 @@
"source": [
"import sys\n",
"import os\n",
+ "import json\n",
"import xml_writers as writers\n",
"from xml.dom import minidom\n",
"import xml.etree.ElementTree as ET\n",
@@ -36,7 +37,7 @@
},
{
"cell_type": "code",
- "execution_count": 113,
+ "execution_count": 281,
"metadata": {},
"outputs": [
{
@@ -56,7 +57,155 @@
},
{
"cell_type": "code",
- "execution_count": 114,
+ "execution_count": 282,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added feedback edge from 5 to 1 with flits=1\n",
+ "Added init node 25 for 1 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 25 to 1 with flits=1\n",
+ "Added feedback edge from 8 to 1 with flits=1\n",
+ "Added init node 26 for 1 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 26 to 1 with flits=1\n",
+ "Added feedback edge from 11 to 1 with flits=1\n",
+ "Added init node 27 for 1 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 27 to 1 with flits=1\n",
+ "Added feedback edge from 14 to 1 with flits=1\n",
+ "Added init node 28 for 1 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 28 to 1 with flits=1\n",
+ "Added feedback edge from 4 to 3 with flits=1\n",
+ "Added init node 29 for 3 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 29 to 3 with flits=1\n",
+ "Added feedback edge from 17 to 7 with flits=1\n",
+ "Added init node 30 for 7 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 30 to 7 with flits=1\n",
+ "Added feedback edge from 17 to 13 with flits=1\n",
+ "Added init node 31 for 13 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 31 to 13 with flits=1\n",
+ "Added feedback edge from 17 to 16 with flits=1\n",
+ "Added init node 32 for 16 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 32 to 16 with flits=1\n",
+ "Added feedback edge from 3 to 16 with flits=1\n",
+ "Added init node 33 for 16 with delay_comp=0, delay_mem=0, delay_send=0\n",
+ "Added edge from init node 33 to 16 with flits=1\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ======================================================================\n",
+ "# Add feedback edges and init tasks\n",
+ "# ======================================================================\n",
+ "\n",
+ "for node in task_graph.nodes:\n",
+ " if \"nodetype\" not in task_graph.nodes[node]:\n",
+ " task_graph.nodes[node][\"nodetype\"] = \"default\"\n",
+ "\n",
+ "task_graph_update = task_graph.copy()\n",
+ "# Load the data\n",
+ "with open(\"/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/LinProgResults.json\", \"r\") as json_file:\n",
+ " data = json.load(json_file)\n",
+ "\n",
+ "# Access the variables\n",
+ "combined_mapping_dict = data[\"combined_mapping_dict\"]\n",
+ "mapping = data[\"mapping\"]\n",
+ "shortest_path = data[\"shortest_path\"]\n",
+ "init_node_counter=len(task_graph.nodes())\n",
+ "init_dict = {}\n",
+ "for key, value in combined_mapping_dict.items():\n",
+ "\n",
+ " # Define a subset of nodes\n",
+ " subset_nodes = value\n",
+ " # print(f\"subset_nodes: {subset_nodes}\")\n",
+ " # Find all simple paths within the subset\n",
+ " all_paths = []\n",
+ " for src in subset_nodes:\n",
+ " for dst in subset_nodes:\n",
+ " if src != dst:\n",
+ " paths = list(nx.all_simple_paths(task_graph, source=src, target=dst))\n",
+ " for path in paths:\n",
+ " if all(node in subset_nodes for node in path):\n",
+ " all_paths.append(path)\n",
+ "\n",
+ " # Filter out subpaths to get independent paths\n",
+ " independent_paths = []\n",
+ " for path in all_paths:\n",
+ " if not any(set(path).issubset(set(other)) and path != other for other in all_paths):\n",
+ " independent_paths.append(path)\n",
+ "\n",
+ " # print(f\"Independent paths in subset {subset_nodes}: {independent_paths}\")\n",
+ "\n",
+ " for edge in task_graph.edges(data=True):\n",
+ " edge[2][\"datatype\"] = None\n",
+ " \n",
+ "\n",
+ " # Print the independent paths\n",
+ " for path in independent_paths:\n",
+ " if path[0] != path[-1]:\n",
+ " task_graph_update.add_edge(path[-1], path[0],flits=1,datatype=path[-1])\n",
+ "\n",
+ " init_dict[path[0]] = path[-1]\n",
+ "\n",
+ "\n",
+ " task_graph_update.add_node(init_node_counter, delay_comp=0, delay_mem=0, delay_send=0,nodetype=\"init\", src=False, dst=False)\n",
+ " \n",
+ " task_graph_update.add_edge(init_node_counter, path[0],flits=1,datatype=path[-1])\n",
+ " init_node_counter += 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "# ======================================================================\n",
+ "# Add feedback edges and init tasks for tasks between accelerators\n",
+ "# task can only start if its destination (if it is in another accelerator) is finished\n",
+ "# ======================================================================\n",
+ "\n",
+ "for node in task_graph.nodes:\n",
+ " for successor in task_graph.successors(node):\n",
+ " same_accelerator = False\n",
+ " for key, value_list in combined_mapping_dict.items():\n",
+ " if node in value_list and successor in value_list:\n",
+ " \n",
+ " same_accelerator=True\n",
+ "\n",
+ " if not same_accelerator:\n",
+ " task_graph_update.add_edge(successor, node, flits=1,datatype=successor)\n",
+ " print(f\"Added feedback edge from {successor} to {node} with flits=1\")\n",
+ "\n",
+ " init_dict[node] = successor\n",
+ "\n",
+ " task_graph_update.add_node(init_node_counter, delay_comp=0, delay_mem=0, delay_send=0,nodetype=\"init\", src=False, dst=False)\n",
+ " print(f\"Added init node {init_node_counter} for {node} with delay_comp=0, delay_mem=0, delay_send=0\")\n",
+ " \n",
+ " task_graph_update.add_edge(init_node_counter, node,flits=1,datatype=successor)\n",
+ " print(f\"Added edge from init node {init_node_counter} to {node} with flits=1\")\n",
+ " init_node_counter += 1\n",
+ "\n",
+ "\n",
+ "\n",
+ " # if mapping[str(node)] != mapping[str(successor)]:\n",
+ " # # task_graph.add_edge(successor, node, flits=1)\n",
+ " # print(f\"Added feedback edge from {successor} to {node} with flits=1\")\n",
+ "\n",
+ "draw_networkx_graph(task_graph_update)\n",
+ "with open('/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/task_graph_feedback.pkl', 'wb') as output_file:\n",
+ " pickle.dump(task_graph_update, output_file)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 283,
"metadata": {},
"outputs": [],
"source": [
@@ -67,33 +216,87 @@
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": 284,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "node: 0\n",
+ "node: 1\n",
+ "node: 2\n",
+ "node: 3\n",
+ "node: 4\n",
+ "node: 5\n",
+ "node: 6\n",
+ "node: 7\n",
+ "node: 8\n",
+ "node: 9\n",
+ "node: 10\n",
+ "node: 11\n",
+ "node: 12\n",
+ "node: 13\n",
+ "node: 14\n",
+ "node: 15\n",
+ "node: 16\n",
+ "node: 17\n",
+ "node: 18\n",
+ "node: 19\n",
+ "node: 20\n",
+ "node: 21\n",
+ "node: 22\n",
+ "node: 23\n",
+ "node: 24\n",
+ "node: 25\n",
+ "node: 26\n",
+ "node: 27\n",
+ "node: 28\n",
+ "node: 29\n",
+ "node: 30\n",
+ "node: 31\n",
+ "node: 32\n",
+ "node: 33\n"
+ ]
+ }
+ ],
"source": [
"tasks_node = data_writer.add_tasks_node()\n",
"\n",
- "\n",
+ "task_graph=task_graph_update\n",
"\n",
"\n",
"for node in task_graph.nodes:\n",
+ " print(f\"node: {node}\")\n",
" atr=task_graph.nodes[node]\n",
" task_node = data_writer.add_task_node(tasks_node, t_id=node,duration=(1,1), repeat=(10,10))\n",
"\n",
" generates_node = data_writer.add_generates_node(task_node)\n",
" successors = list(task_graph.successors(node))\n",
- " dist_tasks = [node] * len(successors)\n",
- " count_list = [atr[\"delay_send\"]] * len(successors)\n",
+ " if atr[\"nodetype\"] == \"init\":\n",
+ " dist_tasks=[task_graph.edges[node, successors[0]][\"datatype\"]]\n",
+ " # print(dist_tasks)\n",
+ " # print(f\"successors: {successors}\")\n",
+ " # dist_tasks = [init_dict[suc] for suc in successors]\n",
+ " # print(f\"dist_tasks: {dist_tasks}\")\n",
+ " else:\n",
+ " dist_tasks = [node] * len(successors)\n",
+ " count_list = [] \n",
+ " for suc in successors:\n",
+ " count_list.append(task_graph.edges[node, suc][\"flits\"])\n",
" if len(successors) > 0:\n",
" data_writer.add_possibility(generates_node, id=0, prob=1, delay=(atr[\"delay_comp\"],atr[\"delay_comp\"]), interval=20, count=count_list, dt_ix=dist_tasks, dist_tasks=successors)\n",
"\n",
" predecessors = list(task_graph.predecessors(node))\n",
- " for idx,pre in enumerate(predecessors):\n",
- " pre_atr = task_graph.nodes[pre]\n",
+ " if len(predecessors) > 0:\n",
" requires_node = data_writer.add_requires_node(task_node)\n",
+ " for idx,pre in enumerate(predecessors):\n",
+ " pre_atr = task_graph.nodes[pre] \n",
"# for i in range (len(t.req_type)):\n",
"# data_writer.add_requirement(requires_node,id=i, type=t.req_type[i], source=t.req_src[i], count=t.req_count[i])\n",
- " data_writer.add_requirement(requires_node,id=idx, type=pre, source=pre, count=pre_atr[\"delay_send\"])\n",
+ " count_req = task_graph.edges[pre, node][\"flits\"]\n",
+ " if pre_atr[\"nodetype\"] != \"init\":\n",
+ " data_writer.add_requirement(requires_node,id=idx, type=pre, source=pre, count=count_req)\n",
"data_writer.write_file('../XML/LinProg_test/data.xml')"
]
}
diff --git a/XML_Scripts/mappingXML.ipynb b/XML_Scripts/mappingXML.ipynb
index 7fe0cd5..d5c8f3b 100644
--- a/XML_Scripts/mappingXML.ipynb
+++ b/XML_Scripts/mappingXML.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -35,21 +35,10 @@
"\n",
"print(combined_mapping_dict)\n",
"print(mapping)\n",
- "print(shortest_path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [],
- "source": [
- "map_writer = writers.MapWriter('map')\n",
- "for combined_node in combined_mapping_dict:\n",
- " for node in combined_mapping_dict[combined_node]:\n",
- " map_writer.add_bindings(tasks=[node],nodes=[int(mapping[str(combined_node)])])\n",
- "map_writer.write_file('../XML/LinProg_test/map.xml')\n",
- "\n"
+ "print(shortest_path)\n",
+ "\n",
+ "with open('/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/task_graph_feedback.pkl', 'rb') as file:\n",
+ " task_graph = pickle.load(file)"
]
},
{
@@ -61,13 +50,61 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Loaded Task_graph: DiGraph with 19 nodes and 23 edges\n"
+ "init node: 19\n",
+ "Attributes: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 0, 'nodetype': 'init', 'src': False, 'dst': False}\n",
+ "init node: 20\n",
+ "Attributes: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 0, 'nodetype': 'init', 'src': False, 'dst': False}\n",
+ "init node: 21\n",
+ "Attributes: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 0, 'nodetype': 'init', 'src': False, 'dst': False}\n",
+ "init node: 22\n",
+ "Attributes: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 0, 'nodetype': 'init', 'src': False, 'dst': False}\n",
+ "init node: 23\n",
+ "Attributes: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 0, 'nodetype': 'init', 'src': False, 'dst': False}\n",
+ "init node: 24\n",
+ "Attributes: {'delay_comp': 0, 'delay_mem': 0, 'delay_send': 0, 'nodetype': 'init', 'src': False, 'dst': False}\n"
]
}
],
"source": [
- "with open('/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/Task_graph.pkl', 'rb') as file:\n",
- " task_graph = pickle.load(file)\n",
+ "map_writer = writers.MapWriter('map')\n",
+ "for combined_node in combined_mapping_dict:\n",
+ " for node in combined_mapping_dict[combined_node]:\n",
+ " # print(combined_node,combined_mapping_dict[combined_node])\n",
+ " map_writer.add_bindings(tasks=[node],nodes=[int(mapping[str(combined_node)])])\n",
+ "\n",
+ "for node in task_graph.nodes():\n",
+ " if task_graph.nodes[node].get('nodetype') == \"init\":\n",
+ " print(\"init node: \", node)\n",
+ " \n",
+ " successors = list(task_graph.successors(node))\n",
+ " successor=successors[0]\n",
+ " for combined_node in combined_mapping_dict:\n",
+ " for node2 in combined_mapping_dict[combined_node]:\n",
+ " # print(combined_node,combined_mapping_dict[combined_node])\n",
+ " if successor in combined_mapping_dict[combined_node]:\n",
+ " mappedTo=int(mapping[str(combined_node)])\n",
+ " map_writer.add_bindings(tasks=[node],nodes=[mappedTo])\n",
+ " # print(\"init node: \", node)\n",
+ " print(\"Attributes: \", task_graph.nodes[node])\n",
+ "map_writer.write_file('../XML/LinProg_test/map.xml')\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded Task_graph: DiGraph with 25 nodes and 35 edges\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
"\n",
"print(\"Loaded Task_graph:\", task_graph)\n",
" \n",
diff --git a/simulation/scripts/run_linProg.sh b/simulation/scripts/run_linProg.sh
new file mode 100755
index 0000000..5882fc5
--- /dev/null
+++ b/simulation/scripts/run_linProg.sh
@@ -0,0 +1,90 @@
+#!/bin/bash
+
+toGem5fromStats(){
+ cd ../..
+ cd wk_gem5_SysXelerator_v2
+}
+
+toStatsfromGem5(){
+ cd ..
+ cd wk_gem5_stats/scripts
+}
+
+runSimAndAnaly(){
+ local width=$1
+ local repetition=$2
+ local workload=$3
+ local strategy=$4
+ local scratchpad_size=$5
+ local compression_factor=$6
+ local folder_path=$7
+
+ echo "Changing the repetition!"
+ python3 ChangeRepData.py --repetition $repetition --compression_factor $compression_factor --folder_path $folder_path
+
+
+ cd /home/sfischer/Documents/projects/wk_gem5_SysXelerator_v2
+
+ echo "Starting the simulation!"
+
+ build/X86_MESI_Two_Level/gem5.opt --debug-flag RubyNetworkReduced,AccelTrafficTraceReduced,TaskPEOp configs/gemmini/Accel4x4NoC.py --ruby --num-cpus 17 --network garnet --l1d_size 8MiB --l1i_size 8MiB --topology Mesh_XY --mesh-rows 3 --mem-type DDR3_1600_8x8 --link-width-bits $width --scratchpad_size $scratchpad_size --folder_path $folder_path >raw_log.txt
+
+ cd /home/sfischer/Documents/projects/wk_LinProg/simulation/scripts
+
+ echo "Starting the Analysis!"
+ python3 MESI_Analysis_Gemmini.py \
+ --link_width $width \
+ --repetition $repetition \
+ --workload $workload \
+ --strategy $strategy
+
+ # rm -rf ../Results/Accelerator/log_raw/*
+
+}
+
+
+
+link_width_bits_list=(576) #max 72B = 576bit must be devisible by 8 (in gem5 used as byte)
+repetitions_list=(12)
+
+scratchpad_size_list=(10000)
+compression_factor_list=(1) # 1 = no compression
+
+# workload="MobileNetV2_20Layer_1C10A_R100_1GHz_fast3"
+workload="LinProgTest"
+
+strategy_mode="minTrafficWithLowThroughput"
+
+
+folder_path_2gem5="/home/sfischer/Documents/projects/wk_gem5_SysXelerator_v2"
+# folder_path="/home/sfischer/gem5folder/gem5-accel/src/gemmini_dev_a/MobileNetV2_1A1C/"
+folder_path="${folder_path_2gem5}/src/gemmini_dev_a/LinProg_test/"
+DESTINATION_FILE="${folder_path}map_addition.xml"
+SOURCE_FILE="${folder_path}map_addition_old.xml"
+
+if [ -e "$SOURCE_FILE" ]; then
+ cp "$SOURCE_FILE" "$DESTINATION_FILE"
+else
+ echo "Source file does not exist. Creating an empty file."
+ touch "$SOURCE_FILE"
+ cp "$DESTINATION_FILE" "$SOURCE_FILE"
+fi
+
+# Nested loops to execute the function for all combinations of LINK_WIDTH_BITS and repetitions
+for LINK_WIDTH_BITS in "${link_width_bits_list[@]}"; do
+ for repetition in "${repetitions_list[@]}"; do
+ for scratchpad_size in "${scratchpad_size_list[@]}"; do
+ for compression_factor in "${compression_factor_list[@]}"; do
+ strategy="${strategy_mode}_scratch${scratchpad_size}_comp${compression_factor}"
+ runSimAndAnaly $LINK_WIDTH_BITS $repetition $workload $strategy $scratchpad_size $compression_factor $folder_path
+ cp "$SOURCE_FILE" "$DESTINATION_FILE"
+ done
+ done
+ done
+done
+
+
+
+
+
+