speedup tabulate cuda kernel by reducing shm using #830
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Codecov Report
@@ Coverage Diff @@
## devel #830 +/- ##
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- Coverage 73.99% 64.28% -9.71%
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Files 84 5 -79
Lines 6733 14 -6719
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- Hits 4982 9 -4973
+ Misses 1751 5 -1746 Continue to review full report at Codecov.
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denghuilu
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Jul 7, 2021
galeselee
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Jul 7, 2021
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Has this modification been tested on ROCm platform to increase the speed? |
ROCM has very little speedup with this shm reduce schema. I will push another PR about tabulate ROCm op speed-up by other ways. |
amcadmus
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Jul 7, 2021
gzq942560379
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Sep 2, 2021
* reduced the shm used in tabulate_fusion_fifth_order_polynomial cuda kernel * formatted `MTILE` and `KTILE` used in tabulate kernels * formatted `warp_idx` used in tabulate kernel
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Does this PR include a bug fix? Per #2303, the ROCm should apply the same modification. |
njzjz
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Feb 28, 2023
Follow deepmodeling#830 to fix deepmodeling#2303. Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
wanghan-iapcm
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Mar 1, 2023
njzjz
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Sep 21, 2023
* add entire arguments of gaussian style Resolves deepmodeling#780. * add args for relative model deviation
wanghan-iapcm
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Sep 22, 2023
Merge `source/lib/src/cuda` and `source/lib/src/rocm` into `source/lib/src/gpu`. - Define macros `gpuGetLastError`, `gpuDeviceSynchronize`, `gpuMemcpy`, `gpuMemcpyDeviceToHost`, `gpuMemcpyHostToDevice`, and `gpuMemset` to make them available for both CUDA and ROCm. - Use `<<< >>> syntax` for both CUDA and ROCm. Per ROCm/HIP@cf78d85, it has been supported in HIP since 2018. - Fix several int const numbers that should be double or float. - For tabulate: - Fix `WARP_SIZE` for ROCm. Per pytorch/pytorch#64302, WARP_SIZE can be 32 or 64, so it should not be hardcoded to 64. - Add `GpuShuffleSync`. Per ROCm/HIP#1491, `__shfl_sync` is not supported by HIP. - After merging the code, #1274 should also work for ROCm. - Use the same `ii` for #830 and #2357. Although both of them work, `ii` has different meanings in these two PRs, but now it should be the same. - However, `ii` in `tabulate_fusion_se_a_fifth_order_polynomial` (rocm) added by #2532 is wrong. After merging the codes, it should be corrected. - Optimization in #830 was not applied to ROCm. - `__syncwarp` is not supported by ROCm. - After merging the code, #2661 will be applied to ROCm. Although TF ROCm stream is still blocking (https://github.com/tensorflow/tensorflow/blob/9d1262082e761cd85d6726bcbdfdef331d6d72c6/tensorflow/compiler/xla/stream_executor/rocm/rocm_driver.cc#L566), we don't know whether it will change to non-blocking. - There are several other differences between CUDA and ROCm. --------- Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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This PR works on tabulate fusion CUDA kernel, including:
tabulate_fusion_fifth_order_polynomialkernel 1.3 times by reducing shm using (and thread syncing).KTILEandMTILEintabulate_fusion_grad_fifth_order_polynomialkernel.warp_idxvariable used intabulate_fusion_grad_fifth_order_polynomialkernel.