Invoke AMD specific kernel reorder_batched_ad_indices_kernel_vec #4412
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…orch#4412) Summary: X-link: facebookresearch/FBGEMM#1483 For the benchmark in the codebase, the larger the profuct of length and num-ads is, the better performance. Two optimization: 1. Vector loading in a warp. 2. The product of batch-size and table-size determines the # of thread blocks (https://www.internalfb.com/code/fbsource/[cecfed562b79afad0eb9c44259141f50352da342]/fbcode/deeplearning/fbgemm/fbgemm_gpu/src/sparse_ops/sparse_reorder_batched_ad.cu?lines=361). In MRS models, we expect more thread blocks in our user cases. As such, we shrink the block size to achieve more thread blocks, thus improving compute utilization. Performance results and local test benchmarks: D77066925 Differential Revision: D77459476
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…orch#4412) Summary: X-link: facebookresearch/FBGEMM#1483 For the benchmark in the codebase, the larger the profuct of length and num-ads is, the better performance. Two optimization: 1. Vector loading in a warp. 2. The product of batch-size and table-size determines the # of thread blocks (https://www.internalfb.com/code/fbsource/[cecfed562b79afad0eb9c44259141f50352da342]/fbcode/deeplearning/fbgemm/fbgemm_gpu/src/sparse_ops/sparse_reorder_batched_ad.cu?lines=361). In MRS models, we expect more thread blocks in our user cases. As such, we shrink the block size to achieve more thread blocks, thus improving compute utilization. Performance results and local test benchmarks: D77066925 Differential Revision: D77459476
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…orch#4412) Summary: Pull Request resolved: pytorch#4412 X-link: facebookresearch/FBGEMM#1483 For the benchmark in the codebase, the larger the profuct of length and num-ads is, the better performance. Two optimization: 1. Vector loading in a warp. 2. The product of batch-size and table-size determines the # of thread blocks (https://www.internalfb.com/code/fbsource/[cecfed562b79afad0eb9c44259141f50352da342]/fbcode/deeplearning/fbgemm/fbgemm_gpu/src/sparse_ops/sparse_reorder_batched_ad.cu?lines=361). In MRS models, we expect more thread blocks in our user cases. As such, we shrink the block size to achieve more thread blocks, thus improving compute utilization. Performance results and local test benchmarks: D77066925 Differential Revision: D77459476
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This pull request was exported from Phabricator. Differential Revision: D77459476 |
…orch#4412) Summary: Pull Request resolved: pytorch#4412 X-link: facebookresearch/FBGEMM#1483 For the benchmark in the codebase, the larger the profuct of length and num-ads is, the better performance. Two optimization: 1. Vector loading in a warp. 2. The product of batch-size and table-size determines the # of thread blocks (https://www.internalfb.com/code/fbsource/[cecfed562b79afad0eb9c44259141f50352da342]/fbcode/deeplearning/fbgemm/fbgemm_gpu/src/sparse_ops/sparse_reorder_batched_ad.cu?lines=361). In MRS models, we expect more thread blocks in our user cases. As such, we shrink the block size to achieve more thread blocks, thus improving compute utilization. Performance results and local test benchmarks: D77066925 Differential Revision: D77459476
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Summary:
For the benchmark in the codebase, the larger the profuct of length and num-ads is, the better performance.
Two optimization:
Performance results and local test benchmarks: D77066925
Differential Revision: D77459476