billmuch / matmul_perf_testLinks
☆14Updated 3 years ago
Alternatives and similar repositories for matmul_perf_test
Users that are interested in matmul_perf_test are comparing it to the libraries listed below
Sorting:
- play gemm with tvm☆91Updated 2 years ago
- An unofficial cuda assembler, for all generations of SASS, hopefully :)☆84Updated 2 years ago
- study of cutlass☆22Updated 10 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆94Updated last week
- This is a demo how to write a high performance convolution run on apple silicon☆54Updated 3 years ago
- study of Ampere' Sparse Matmul☆18Updated 4 years ago
- ☆107Updated 5 months ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆183Updated 7 months ago
- ☆150Updated 8 months ago
- PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections☆122Updated 3 years ago
- ☆98Updated 4 years ago
- ☆114Updated last year
- Triton Compiler related materials.☆31Updated 8 months ago
- ☆39Updated 5 years ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆75Updated last year
- ☆40Updated 3 years ago
- DietCode Code Release☆65Updated 3 years ago
- ☆18Updated last year
- Benchmark scripts for TVM☆74Updated 3 years ago
- ☆98Updated last year
- Standalone Flash Attention v2 kernel without libtorch dependency☆110Updated last year
- examples for tvm schedule API☆101Updated 2 years ago
- ☆153Updated 8 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆109Updated 4 months ago
- An extension library of WMMA API (Tensor Core API)☆104Updated last year
- Triton adapter for Ascend. Mirror of https://gitee.com/ascend/triton-ascend☆70Updated this week
- Assembler and Decompiler for NVIDIA (Maxwell Pascal Volta Turing Ampere) GPUs.☆84Updated 2 years ago
- ☆101Updated 3 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.