FlagTree / libtriton_jitLinks
A Triton JIT runtime and ffi provider in C++
☆22Updated this week
Alternatives and similar repositories for libtriton_jit
Users that are interested in libtriton_jit are comparing it to the libraries listed below
Sorting:
- ☆64Updated 4 months ago
- Tile-based language built for AI computation across all scales☆57Updated last week
- NVSHMEM‑Tutorial: Build a DeepEP‑like GPU Buffer☆100Updated this week
- Triton adapter for Ascend. Mirror of https://gitee.com/ascend/triton-ascend☆71Updated this week
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆75Updated last year
- ☆98Updated last year
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆94Updated last week
- TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing.☆97Updated 2 months ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆185Updated 7 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆112Updated 4 months ago
- DeeperGEMM: crazy optimized version☆70Updated 4 months ago
- Implement Flash Attention using Cute.☆95Updated 9 months ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆110Updated last year
- A practical way of learning Swizzle☆27Updated 7 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆40Updated 6 months ago
- Multiple GEMM operators are constructed with cutlass to support LLM inference.☆19Updated last month
- ☆32Updated 7 months ago
- An experimental communicating attention kernel based on DeepEP.☆34Updated last month
- Framework to reduce autotune overhead to zero for well known deployments.☆82Updated this week
- ☆104Updated 4 months ago
- A lightweight design for computation-communication overlap.☆167Updated last week
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆43Updated 3 months ago
- GPTQ inference TVM kernel☆40Updated last year
- ☆108Updated 5 months ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆65Updated last year
- ☆94Updated 5 months ago
- ☆55Updated 2 months ago
- triton for dsa☆40Updated this week
- ☆13Updated 6 months ago
- ☆18Updated this week