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C
Fine-tuningFreeOpen Source
CUTLASS
CUDA template library for high-performance matrix operations
Apache-2.0
ABOUT
Writing high-performance CUDA kernels for matrix operations is incredibly difficult — it requires deep knowledge of GPU memory hierarchy, warp scheduling, tensor core instructions, and tiling strategies. Most ML teams lack the expertise to hand-optimize kernels for their specific model architectures. CUTLASS abstracts away this complexity with composable C++ template building blocks that auto-tune for target GPUs. It powers the kernels behind many major ML frameworks and is the foundation for libraries like FlashAttention and cuBLAS.
INSTALL
pip install cutlassINTEGRATION GUIDE
1. Build custom CUDA kernels for novel attention mechanisms without starting from scratch
2. Auto-tune matrix multiply operations for specific GPU architectures and problem sizes
3. Implement efficient grouped GEMM for mixture-of-experts model inference
4. Speed up convolution operations in vision models with optimized warp-level primitives
5. Create fused kernels that combine matrix multiply with activation functions in one pass
TAGS
cudakernelsmatrix-multiplygputemplate-librarynvidiagemm