zeroine / cutlass-cute-sample
☆29Updated 11 months ago
Alternatives and similar repositories for cutlass-cute-sample:
Users that are interested in cutlass-cute-sample are comparing it to the libraries listed below
- ☆42Updated 2 months ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆60Updated 7 months ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆57Updated 6 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆59Updated 2 weeks ago
- Optimize GEMM with tensorcore step by step☆24Updated last year
- Standalone Flash Attention v2 kernel without libtorch dependency☆106Updated 6 months ago
- Implement Flash Attention using Cute.☆71Updated 3 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆89Updated 3 weeks ago
- Benchmark code for the "Online normalizer calculation for softmax" paper☆85Updated 6 years ago
- llama INT4 cuda inference with AWQ☆53Updated 2 months ago
- play gemm with tvm☆89Updated last year
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆35Updated 3 weeks ago
- Tutorials of Extending and importing TVM with CMAKE Include dependency.☆13Updated 5 months ago
- 使用 CUDA C++ 实现的 llama 模型推理框架☆48Updated 4 months ago
- ☆82Updated last year
- Examples of CUDA implementations by Cutlass CuTe☆145Updated last month
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆35Updated last week
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆100Updated 8 months ago
- This project is about convolution operator optimization on GPU, include GEMM based (Implicit GEMM) convolution.☆26Updated 2 months ago
- ☆105Updated 3 months ago
- ☆87Updated 6 months ago
- ☆87Updated last week
- CUDA 8-bit Tensor Core Matrix Multiplication based on m16n16k16 WMMA API☆28Updated last year
- Multiple GEMM operators are constructed with cutlass to support LLM inference.☆17Updated 5 months ago
- ☆64Updated last month
- ☆145Updated 2 months ago
- ☆35Updated 5 months ago
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆38Updated 7 months ago