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Tune GEMM experiment settings in Kebab using config.yaml and optimization docs. Use when asked to adjust matrix sizes, precision, layout modes, tile sizes, kernel versions, or to design reproducible performance experiments.
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| name | kebab-gemm-config-tuning |
| description | Tune GEMM experiment settings in Kebab using config.yaml and optimization docs. Use when asked to adjust matrix sizes, precision, layout modes, tile sizes, kernel versions, or to design reproducible performance experiments. |
RR, RC, CR, CC)float16, bfloat16, float32) or kernel version listsMain file: config.yaml
Important keys under operators.gemm:
implversionsmatrix_sizesmodesprecisionstile_sizesinit_methodconfig.yaml with one controlled change at a time.make buildmake bench-gemmmake tune-gemm-cute (or cuda/ref)bench_results/, profiling/, and reports/.sm_90a.| Issue | Mitigation |
|---|---|
| Unexpected perf regression | Revert to previous versions/tile_sizes baseline and isolate one variable |
| Precision mismatch | Verify selected kernel path supports requested precision |
| Layout-specific anomaly | Test all modes and compare independently |
config.yamldocs/optimization_plan.mddocs/TILE_SIZE_CONFIGURATION.mdMakefile GEMM benchmark/profile targetsRun operator benchmarks and single-run executables in Kebab for GEMM and elementwise add. Use when asked to execute performance baselines, compare implementations, or run quick correctness/perf checks through Make targets.
Build and bootstrap the Kebab project on Linux with CUDA, CMake, and Makefile workflows. Use when asked to set up dependencies, detect CUDA/GPU architecture, configure builds, or fix compilation environment issues.
Navigate and modify the Kebab CUDA codebase safely. Use when asked to locate GEMM, CuTe, CUDA baseline, benchmark, microbenchmark, profiling, or build logic in this repository, and when deciding where a change should be implemented.
Execute Kebab microbenchmarks and generate PTX/SASS dumps for kernel-level analysis. Use when asked to run mbench targets, inspect assembly output, compare copy or MMA kernels, or analyze low-level code generation.
Profile Kebab kernels with Nsight Compute and export readable reports. Use when asked to run tune/mtune targets, collect NCU reports, convert .ncu-rep to text summaries, or troubleshoot performance bottlenecks.