xlite-dev / hgemm-mma
⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.
☆62Updated 3 weeks ago
Alternatives and similar repositories for hgemm-mma:
Users that are interested in hgemm-mma are comparing it to the libraries listed below
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆60Updated 7 months ago
- Implement Flash Attention using Cute.☆74Updated 3 months ago
- ☆48Updated 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 …☆179Updated 2 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆90Updated last month
- ☆88Updated 6 months ago
- DeeperGEMM: crazy optimized version☆61Updated last week
- Standalone Flash Attention v2 kernel without libtorch dependency☆106Updated 6 months ago
- play gemm with tvm☆89Updated last year
- Quantized Attention on GPU☆45Updated 4 months ago
- Examples of CUDA implementations by Cutlass CuTe☆146Updated last month
- ☆90Updated 2 weeks ago
- ☆29Updated 11 months ago
- Multiple GEMM operators are constructed with cutlass to support LLM inference.☆17Updated 6 months ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆58Updated 6 months ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆101Updated 8 months ago
- Optimize GEMM with tensorcore step by step☆24Updated last year
- ☆57Updated 3 months ago
- A GPU-optimized system for efficient long-context LLMs decoding with low-bit KV cache.☆23Updated this week
- ☆112Updated 3 months ago
- TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing. By pro…☆70Updated this week
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆35Updated 2 weeks ago
- 📚FFPA(Split-D): Yet another Faster Flash Prefill Attention with O(1) GPU SRAM complexity for headdim > 256, ~2x↑🎉vs SDPA EA.☆154Updated this week
- ☆87Updated last year
- Tutorials of Extending and importing TVM with CMAKE Include dependency.☆13Updated 5 months ago
- ☆65Updated 2 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆35Updated last month
- llama INT4 cuda inference with AWQ☆53Updated 2 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆77Updated 4 months ago
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆38Updated 7 months ago