meta-pytorch / tritonbenchLinks
Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.
☆319Updated this week
Alternatives and similar repositories for tritonbench
Users that are interested in tritonbench are comparing it to the libraries listed below
Sorting:
- Applied AI experiments and examples for PyTorch☆314Updated 5 months ago
- ☆277Updated this week
- Cataloging released Triton kernels.☆289Updated 4 months ago
- ☆101Updated last year
- ☆258Updated last year
- Autonomous GPU Kernel Generation via Deep Agents☆223Updated this week
- Collection of kernels written in Triton language☆175Updated 9 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆141Updated 8 months ago
- Fast low-bit matmul kernels in Triton☆424Updated this week
- A Quirky Assortment of CuTe Kernels☆772Updated this week
- Accelerating MoE with IO and Tile-aware Optimizations☆563Updated last week
- extensible collectives library in triton☆93Updated 9 months ago
- ☆158Updated last year
- A collection of memory efficient attention operators implemented in the Triton language.☆287Updated last year
- A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.☆732Updated this week
- TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels☆186Updated last week
- Github mirror of trition-lang/triton repo.☆126Updated this week
- ring-attention experiments☆163Updated last year
- QuTLASS: CUTLASS-Powered Quantized BLAS for Deep Learning☆163Updated 2 months ago
- Framework to reduce autotune overhead to zero for well known deployments.☆94Updated 4 months ago
- Helpful kernel tutorials and examples for tile-based GPU programming☆592Updated last week
- Allow torch tensor memory to be released and resumed later☆207Updated 2 weeks ago
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆250Updated 8 months ago
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆276Updated 6 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆145Updated 8 months ago
- Fastest kernels written from scratch☆528Updated 4 months ago
- kernels, of the mega variety☆657Updated 4 months ago
- ☆115Updated last year
- DeeperGEMM: crazy optimized version☆73Updated 8 months ago
- Perplexity GPU Kernels☆554Updated 2 months ago