Comments (3)
Performance differences are expected on different platforms. One thing to note though is that oneDNN verbose mode has non-trivial performance overhead, in particular on GPUs and cannot be reliably used to measure performance. You can use benchdnn in performance validation mode to get accurate performance measurements.
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@vpirogov. I conducted time consumption tests based on the cpu and gpu for the reordering of convolutional src and weights respectively, the code is from example of primitime convolution.cpp. The result of cpu and gpu are respectively 986 microseconds and 999604 microseconds,gpu is many times slower than cpu. Is there a better way to improve the performance of gpu reoder?
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@feixuedudiao First of all, it doesn't make sense to compare the performance of primitives on CPU and GPU without considering the GPU hardware capabilities and configurations.
For your case, if you insist on comparing them, please try performance testing mode of benchdnn for testing. Here is an example of command line to check the 32x32x1x1 reorder:
./benchdnn --reorder --mode=P --reset --allow-enum-tags-only=0 --engine=gpu --runtime-dim-mask= --sdt=f32 --ddt=f32 --stag=abcd --dtag=Acdb16a --strides=: 32x32x1x1
I tested this command line on a new laptop with a latest Intel integrated GPU hardware and it shows that the performance on GPU is better than that on CPU:
Avg. time on CPU: 0.00714332 ms
Avg. time on GPU: 0.00160363 ms
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Related Issues (20)
- [Proposal] Add cpu alloc/free callback to support customlize memory alloctor APIs. HOT 3
- Assertion `dynamic_cast<derived_type>(base) == base' failed HOT 3
- [ACL] 3D convolution kernel `NEConv3D` is not integrated
- INT8 Performance difference between OneDNN v2.6.3 and v3.4.1 HOT 1
- Possible null pointer dereference in cpu_reorder_pd
- Assertion failure in brgemm in debug build on G3 aarch64 machine HOT 3
- question about matmul_perf example HOT 2
- Information regarding threading backend in oneDNN HOT 1
- could not create a primitive descriptor iterator HOT 5
- cpu: s390x: build fails with saturate was not declared in this scope HOT 7
- Enabling onednn Graph API from framework level HOT 1
- Conditions for Running brgemm_convolution_fwd_t and jit_avx512_common_convolution_fwd_t in oneDNN HOT 3
- oneDNN with Nvidia GPU supprt
- batchnorm requires consistent in- and output mem format_tags HOT 1
- Build fail with CPU_RUNTIME=SEQ and graph compiler backend HOT 2
- OneDNN graph APi for LLM generation HOT 7
- Understand the document on block level APIs(https://github.com/oneapi-src/oneDNN/pull/1852) HOT 1
- dnnl_sgemm occurs segmentation fault with special size HOT 2
- SSE41 kernels are broken HOT 1
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