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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -796,4 +796,98 @@ if __name__ == "__main__":
## Failure is an option: Debugging ## Failure is an option: Debugging
If something is too good to be true then maybe it is not true! Not only in the case of an email from a rich Nigerian prince you might want to debug the situation, also you shouldn't totally trust numba as well. As always: Check if the results are in the right region.
If Numba njit has a problem it ***usually*** gives you an error message and stops. This is the reason why we use njit instead of jit. If jit sees a problem it fixes it with slow Python code. And it does this silently. You will only notice the absence of an improvement. njit stops with an error message.
Only once we found a problem (with an earlier version of numba) where the use of parallel loops failed so beautiful that we got wrong results but a speed improvement 10000x. Looking on the speed improvement this was clearly a case of too good to be true.
But you are not helpless!
In the case of our example with prange we can [activate debugging information](https://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics)
```python
main.parallel_diagnostics(level=4)
```
and see that it did:
```python
================================================================================
Parallel Accelerator Optimizing: Function main, <ipython-input-1-dc4cb05d144d>
(30)
================================================================================
Parallel loop listing for Function main, <ipython-input-1-dc4cb05d144d> (30)
---------------------------------------------------------------------------------|loop #ID
@njit( |
numba.types.uint64[::1]( |
numba.types.uint64, |
numba.types.uint64, |
numba.types.float32[::1], |
numba.types.float32[:, ::1], |
), |
cache=True, |
fastmath=True, |
parallel=True, |
) |
def main( |
number_of_iterations: np.uint64, |
number_of_neurons: np.uint64, |
random_number_spikes: np.ndarray, |
random_number_h: np.ndarray, |
) -> np.ndarray: |
results = np.zeros((number_of_iterations), dtype=np.uint64)------------------| #0
|
for i in prange(0, number_of_iterations):------------------------------------| #2
h = random_number_h[i, :] |
h /= h.sum()-------------------------------------------------------------| #1
results[i] = get_spike(h, number_of_neurons, random_number_spikes[i]) |
|
return results |
--------------------------------- Fusing loops ---------------------------------
Attempting fusion of parallel loops (combines loops with similar properties)...
Trying to fuse loops #0 and #2:
- fusion failed: cross iteration dependency found between loops #0 and #2
----------------------------- Before Optimisation ------------------------------
Parallel region 0:
+--2 (parallel)
+--1 (parallel)
--------------------------------------------------------------------------------
------------------------------ After Optimisation ------------------------------
Parallel region 0:
+--2 (parallel)
+--1 (serial)
Parallel region 0 (loop #2) had 0 loop(s) fused and 1 loop(s) serialized as part
of the larger parallel loop (#2).
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
---------------------------Loop invariant code motion---------------------------
Allocation hoisting:
No allocation hoisting found
Instruction hoisting:
loop #0:
Has the following hoisted:
$expr_out_var.15 = const(uint64, 0)
loop #2:
Has the following hoisted:
$const120.4 = const(NoneType, None)
$const122.5 = const(NoneType, None)
$124build_slice.6 = global(slice: <class 'slice'>)
$124build_slice.7 = call $124build_slice.6($const120.4, $const122.5, func=$124build_slice.6, args=(Var($const120.4, <ipython-input-1-dc4cb05d144d>:50), Var($const122.5, <ipython-input-1-dc4cb05d144d>:50)), kws=(), vararg=None, varkwarg=None, target=None)
$186load_global.16 = global(get_spike: CPUDispatcher(<function get_spike at 0x7fedb9fc27a0>))
Failed to hoist the following:
dependency: $126build_tuple.8 = build_tuple(items=[Var($parfor__index_18.95, <string>:2), Var($124build_slice.7, <ipython-input-1-dc4cb05d144d>:50)])
dependency: h = getitem(value=random__number__h, index=$126build_tuple.8, fn=<built-in function getitem>)
dependency: $206binary_subscr.22 = getitem(value=random__number__spikes, index=$parfor__index_18.95, fn=<built-in function getitem>)
dependency: $220call.23 = call $push_global_to_block.94($h.1.22, number__of__neurons, $206binary_subscr.22, func=$push_global_to_block.94, args=[Var($h.1.22, <ipython-input-1-dc4cb05d144d>:51), Var(number__of__neurons, <ipython-input-1-dc4cb05d144d>:30), Var($206binary_subscr.22, <ipython-input-1-dc4cb05d144d>:52)], kws=(), vararg=None, varkwarg=None, target=None)
--------------------------------------------------------------------------------
```