284804ac53
Sources: siboehm/SGEMM_CUDA → upstream_ref/sgemm_siboehm/ (25 files) wangzyon/NVIDIA_SGEMM_PRACTICE → upstream_ref/nvidia_sgemm_practice/ (23 files, filled gaps) edtallison/sgemm-cuda → upstream_ref/sgemm_edtallison/ (41 files) All files cat'd one by one from git clone (no --depth). These are the 3 public SGEMM repos that can compile on CUDA 10.2 + CoreX ivcore10. Key files for BI-V100 porting: kernel 10 (warp tiling) — already proven on device with WARPSIZE=64 kernel 11/12 (double buffering) — next optimization target sgemm.cu + runner.cu — complete build+benchmark harness CMakeLists.txt — build system reference
72 lines
1.6 KiB
Plaintext
72 lines
1.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b9326784",
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"metadata": {},
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"source": [
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"This notebook installs dependencies, builds the project, and runs a selected kernel on Colab's GPU.\n",
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"\n",
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"**Note:** Ensure Colab runtime type is set to GPU (Runtime → Change runtime type → GPU)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "35ef02c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# install system dependencies\n",
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"!apt-get update && apt-get install -y cmake ninja-build"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "37a03120",
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"metadata": {},
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"outputs": [],
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"source": [
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"# clone\n",
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"!git clone https://github.com/edtallison/sgemm-cuda.git\n",
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"%cd sgemm-cuda"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cd1855de",
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"metadata": {},
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"outputs": [],
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"source": [
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"# build\n",
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"!mkdir -p build && cd build && cmake -G Ninja .. && ninja"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "093a0b6d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# run for kernel 1\n",
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"!cd build && ./sgemm 1"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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