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Fast and generic implementation using OpenMP and CUDA #45
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221b32b
First commit
shikishima-TasakiLab c32b399
CPU-only support
shikishima-TasakiLab 41a5fb0
Update README.md
shikishima-TasakiLab 1140e73
Remove "AutoNonVariableTypeMode".
shikishima-TasakiLab 684769a
Merge branch 'main' of https://github.com/shikishima-TasakiLab/Involu…
shikishima-TasakiLab 4b036d3
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab ce9100f
Compatible with PyTorch 1.7.0 or later
shikishima-TasakiLab 4dc6c8f
Update README.md
shikishima-TasakiLab 57a6d6a
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab 0c190bc
Fixed the value of CUDA_MAX_THREADS
shikishima-TasakiLab 353c22a
Fixed auto-casting to float32 when using AMP.
shikishima-TasakiLab 265c309
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab 2319dea
Support fatbin.
shikishima-TasakiLab 7734b83
Merge remote-tracking branch 'upstream/main' into main
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@d-li14
I changed the arguments of NVCC to be optimized for different architectures.
Hopefully this will increase the speed.
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@shikishima-TasakiLab
Thanks for your efforts. However, the new code still does not lead to an expected speedup from my side.