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A hitchhiker’s guide to CUDA programming

seanzhang.me · published 5 May 2024

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Why GEMM is at the heart of deep learning Read at the source ↗

petewarden.com

https://www.jmlr.org/papers/volume24/23-0069/23-0069.pdf

jmlr.org

https://tvm.apache.org/docs/how_to/tune_with_autotvm/index.html

tvm.apache.org

https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

docs.nvidia.com

https://docs.nvidia.com/cuda/parallel-thread-execution/index.html

docs.nvidia.com

https://en.wikipedia.org/wiki/Single_instruction,_multiple_threads

en.wikipedia.org

https://en.wikipedia.org/wiki/Single_instruction,_multiple_data

en.wikipedia.org

https://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf

nvidia.com

https://developer.nvidia.com/nsight-compute

developer.nvidia.com

https://xmartlabs.github.io/cuda-calculator/

xmartlabs.github.io

https://crd.lbl.gov/divisions/amcr/computer-science-amcr/par/research/roofline/introduction/

crd.lbl.gov

CUDA Pro Tip: Increase Performance with Vectorized Memory Access Read at the source ↗

developer.nvidia.com

https://en.wikipedia.org/wiki/Group_theory

en.wikipedia.org

https://arxiv.org/abs/2301.03598

arxiv.org

https://github.com/wangzyon

github.com

https://siboehm.com/

siboehm.com

https://github.com/seanxwzhang/CUDA_GEMM

github.com

https://www.olcf.ornl.gov/cuda-training-series/

olcf.ornl.gov

https://on-demand.gputechconf.com/gtc/2017/presentation/s7122-stephen-jones-cuda-optimization-tips-tricks-and-techniques.pdf

on-demand.gputechconf.com

https://github.com/wangzyon/NVIDIA_SGEMM_PRACTICE

github.com

How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog Read at the source ↗

siboehm.com

https://doi.org/10.1109/RTAS48715.2020.000-5

doi.org