Comments (6)
feixuedudiao
commented on June 10, 2024
I am very surprised how much the performance improved. But which way for GPU can be used to greatly improve performance? opencl or dpc++?
All best.
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rupakroyintel
commented on June 10, 2024
@feixuedudiao It mostly depends on the specific hardware and the type of workload. Can you share some more details?
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feixuedudiao
commented on June 10, 2024
@rupakroyintel , thank you to answer. if i user it with gpu on i7-10700 cpu，which is great, opencl or dpc++?
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vpirogov
commented on June 10, 2024
Recommended GPU programming model for oneDNN is DPC++. However the choice between OpenCL or DPC++ is primarily defined by application needs and if you are enabling an application that runs with OpenCL you have an option to use oneDNN with OpenCL API and runtime for better interoperability. There's no material differences in performance between OpenCL and DPC++.
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feixuedudiao
commented on June 10, 2024
@vpirogov thanks.
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rupakroyintel
commented on June 10, 2024
Closing the issue for now.
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Related Issues (20)
- Bad speed for f32:s8:f32 matmul HOT 11
- How can I create a matmul primitive with A16W8 (active 16bits, weight 8bits) configuration? HOT 2
- [Proposal] Add cpu alloc/free callback to support customlize memory alloctor APIs. HOT 3
- Assertion `dynamic_cast<derived_type>(base) == base' failed HOT 3
- Why do the "reorder" operations of the same operator take very different times on the CPU and GPU platforms? 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
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