Comments (1)
Performance of mkl-dnn is not yet on par with MKL, For 3x3 forward/inference I expect the performance to be within 5-10% of AVX2. Other cases may be hit and miss: some cases that are optimized in MKL would fall back to reference implementation in mkl-dnn.
You can use https://github.com/baidu-research/DeepBench to compare the two libraries on your hardware on convolution cases you are interested in.
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Related Issues (20)
- dnnl_use_shift in dnnl_batch_normalization_backward_primitive_desc_create causes illegal argument error HOT 1
- Conservative reshaping HOT 6
- Test Failure with threadpool enabled on Arm. HOT 2
- Problem with creating descriptor for pooling primitive HOT 6
- [nvidia] batch normalization primitive fails correctness check HOT 2
- how to dispatch "avx2_vnni_2" HOT 8
- [nvidia] pooling primitive fails correctness check HOT 2
- [nvidia] resampling primitive fails correctness check
- gemm_api and Reorder in HuggingFace OPT model HOT 6
- [nvidia|amd] Add missing synchronization HOT 2
- oneDNN does not build with Intel oneMKL as BLAS Vendor HOT 5
- Falling to ref code in matmul HOT 2
- Understand jit_brgemm_kernel_t and its internals HOT 2
- Help need: use graph API to construct a subgraph of multi-head attention HOT 8
- CPU usage is not as high as expected when thread number >30 HOT 4
- Meet a erro in building process about dnnl HOT 2
- benchdnn matmul failing tests on aarch64 HOT 1
- Expected Multi-Threaded CPU Performance HOT 4
- Wrongly handling of inf when the post-op operation is mul. HOT 2
- Matmul - tensor size effect on performance HOT 3
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