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update out-of-date URL for Intel optimization guide #2657

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Merged
merged 3 commits into from
Nov 8, 2023
Merged

update out-of-date URL for Intel optimization guide #2657

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36a8907
update out-of-date URL for Intel optimization guide
jingxu10 Nov 7, 2023
a5eee63
Merge branch 'main' into jingxu10/2629
svekars Nov 7, 2023
1f159ad
Merge branch 'main' into jingxu10/2629
svekars Nov 8, 2023
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7 changes: 5 additions & 2 deletions recipes_source/recipes/tuning_guide.py
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Expand Up @@ -193,12 +193,15 @@ def fused_gelu(x):
#
# numactl --cpunodebind=N --membind=N python <pytorch_script>

###############################################################################
# More detailed descriptions can be found `here <https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html>`_.

###############################################################################
# Utilize OpenMP
# ~~~~~~~~~~~~~~
# OpenMP is utilized to bring better performance for parallel computation tasks.
# ``OMP_NUM_THREADS`` is the easiest switch that can be used to accelerate computations. It determines number of threads used for OpenMP computations.
# CPU affinity setting controls how workloads are distributed over multiple cores. It affects communication overhead, cache line invalidation overhead, or page thrashing, thus proper setting of CPU affinity brings performance benefits. ``GOMP_CPU_AFFINITY`` or ``KMP_AFFINITY`` determines how to bind OpenMP* threads to physical processing units.
# CPU affinity setting controls how workloads are distributed over multiple cores. It affects communication overhead, cache line invalidation overhead, or page thrashing, thus proper setting of CPU affinity brings performance benefits. ``GOMP_CPU_AFFINITY`` or ``KMP_AFFINITY`` determines how to bind OpenMP* threads to physical processing units. Detailed information can be found `here <https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html>`_.

###############################################################################
# With the following command, PyTorch run the task on N OpenMP threads.
Expand Down Expand Up @@ -283,7 +286,7 @@ def fused_gelu(x):
traced_model(*sample_input)

###############################################################################
# While the JIT fuser for oneDNN Graph also supports inference with ``BFloat16`` datatype,
# While the JIT fuser for oneDNN Graph also supports inference with ``BFloat16`` datatype,
# performance benefit with oneDNN Graph is only exhibited by machines with AVX512_BF16
# instruction set architecture (ISA).
# The following code snippets serves as an example of using ``BFloat16`` datatype for inference with oneDNN Graph:
Expand Down