[PTX] ldmatrix builtin to accelerate copying data from shared memory to warp memory
#10855
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vinx13
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Apr 1, 2022
Hzfengsy
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Apr 2, 2022
yzh119
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Apr 2, 2022
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It turns out that SplitHostDevice would split device and host function via the position of launch_thread, and launch_thread was always placed under block allocated buffers, thus the allocated buffer would not be recognized as device buffer.
I created a boundary block as a workaround.
pfk-beta
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Apr 11, 2022
…y to warp memory (apache#10855) We already have PTX mma and mma.sp builtin support in apache#9909 and apache#10339 . However, we have not supported corresponding data movement builtins for these mma instructions, so the data movement would not be as fast as wmma. This PR brings the `ldmatrix` builtin, which is a native PTX warp-level instruction (https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-ldmatrix), and we can use it to load several (1/2/4) 8x8 matrices from shared memory to warp memory.
mehrdadh
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Apr 11, 2022
…y to warp memory (apache#10855) We already have PTX mma and mma.sp builtin support in apache#9909 and apache#10339 . However, we have not supported corresponding data movement builtins for these mma instructions, so the data movement would not be as fast as wmma. This PR brings the `ldmatrix` builtin, which is a native PTX warp-level instruction (https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-ldmatrix), and we can use it to load several (1/2/4) 8x8 matrices from shared memory to warp memory.
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We already have PTX mma and mma.sp builtin support in #9909 and #10339 . However, we have not supported corresponding data movement builtins for these mma instructions, so the data movement would not be as fast as wmma.
This PR brings the
ldmatrixbuiltin, which is a native PTX warp-level instruction (https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-ldmatrix), and we can use it to load several (1/2/4) 8x8 matrices from shared memory to warp memory.@vinx13 @Hzfengsy