Open-source book with Modern CUDA Learn Notes for Beginners, includes FP16/BF16, FP8, HGEMM, FlashAttention, CuTe, etc.
Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruction.
⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.
FastCuda is a handwritten CUDA operator library featuring progressive GEMM and Reduce kernels, cuBLAS benchmarking, and C/C++/Python interfaces for learning, profiling, and performance optimization.
Use tensor core to calculate back-to-back HGEMM (half-precision general matrix multiplication) with MMA PTX instruction.
Hand-tuned NVIDIA SASS kernels for RTX 3070 Ti (GA104, sm_86): 41,721 dense-equiv 2:4 sparse HGEMM, 11,453 GFLOPS Flash Attention, no cuBLAS / cuDNN / PyTorch. Includes cuasmR, a CRAN-ready R package for cubin read/write + GPU benchmark measurement. 6-chapter tutorial + Chladni-pattern memory layout study.
Generate narrated CUDA course videos with animated slides and AI avatars using Remotion, Gemini, and ElevenLabs TTS for automated production.
To associate your repository with the hgemm topic, visit your repo's landing page and select "manage topics."