Comments (2)
It is up to the user to provide the memory buffers to the CLBlast API. Thus, whether USM is used or not is up to the user. Same for the question about the unneeded memory copy: that is not inside CLBlast, it is up to the user to handle the OpenCL buffers.
Regarding your questions, I would say:
Q: CLBlast on cpu will beat MKL?
A: No, I think that in most cases MKL will be faster. There could be cases where CLBlast is faster on a CPU though, but that has to be tested and depends on the system and matrix sizes.
Q: CLBlast on iGPU beat MKL?
That depends on how fast your iGPU is compared to your CPU. Should be a simple matter of testing.
Note that CLBlast includes a 'client' (use CMake with -DCLIENTS=ON
) which can be used to benchmark, and even can automatically compare to MKL or another BLAS library.
I hope this answers your questions.
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tks.
so this may depend on the llama.cpp side, I give another question to them
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Related Issues (20)
- tunner transpose fails on various specific sizes HOT 1
- SGEMM broken with 1.6.2 on Intel ARC HOT 24
- Tests don't run on Intel Xe/ARC GPU HOT 1
- Android compilation failing HOT 2
- Banded matrices required buffer size calculated incorrectly (GBMV, HBMV, SBMV & TBMV) HOT 3
- Accuracy problem on Apple M1 and Intel(R) UHD Graphics 770 HOT 12
- when i tune GEMM kernel in clblast, i encountered l2 error HOT 2
- Tuner stuck in 'dead lock' and never completes HOT 3
- ERROR IN ROCK5b HOT 3
- Routines to simply transpose a matrix HOT 1
- 'cublasSdgmm' equivalent support HOT 1
- How to use 'CLBlastSgemmBatched'? HOT 1
- Link error when call "GemmStridedBatched<cl_float2>" HOT 1
- About the arguments meaning of the matrix operation functions HOT 3
- About the performance in different matrix layouts HOT 1
- "make uninstall" support HOT 1
- Press a key to 'abort' and 'continue' to next in tuning HOT 1
- Segmentation fault for "_routine_" tuners HOT 1
- What's the meaning of argument 'imax_offset' in clblast::Max()? HOT 1
- Support for 'size_t' as index type for 'Max()' and 'Min()'
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