Matrix Multiply-Accumulate with CUDA and WMMA( Tensor Core)
☆147Aug 18, 2020Updated 5 years ago
Alternatives and similar repositories for wmma_tensorcore_sample
Users that are interested in wmma_tensorcore_sample are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- An extension library of WMMA API (Tensor Core API)☆115Jul 12, 2024Updated last year
- A simple high performance CUDA GEMM implementation.☆436Jan 4, 2024Updated 2 years ago
- Multiple GEMM operators are constructed with cutlass to support LLM inference.☆20Aug 3, 2025Updated 11 months ago
- 方便扩展的Cuda算子理解和优化框架，仅用在学习使用☆18Jun 13, 2024Updated 2 years ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆113Sep 10, 2024Updated last year
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruct…☆551Sep 8, 2024Updated last year
- ☆160Dec 26, 2024Updated last year
- ☆20Nov 7, 2019Updated 6 years ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆194Jan 28, 2025Updated last year
- A Easy-to-understand TensorOp Matmul Tutorial☆441Mar 5, 2026Updated 3 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆45Feb 27, 2025Updated last year
- CUDA 8-bit Tensor Core Matrix Multiplication based on m16n16k16 WMMA API☆37Sep 15, 2023Updated 2 years ago
- ☆32Aug 24, 2022Updated 3 years ago
- ☆73Jan 6, 2025Updated last year
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- ☆150Jan 9, 2025Updated last year
- ☆121Apr 11, 2024Updated 2 years ago
- Magicube is a high-performance library for quantized sparse matrix operations (SpMM and SDDMM) of deep learning on Tensor Cores.☆92Nov 23, 2022Updated 3 years ago
- High Performance FP8 GEMM Kernels for SM89 and later GPUs.☆21Jan 24, 2025Updated last year
- 使用 cutlass 实现 flash-attention 精简版，具有教学意义☆59Aug 12, 2024Updated last year
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆47Jun 11, 2025Updated last year
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆96Jun 21, 2026Updated last week
- ☆33Feb 3, 2025Updated last year
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆82Aug 12, 2024Updated last year
- Proton VPN Special Offer - Get 70% off • AdSpecial partner offer. Trusted by over 100 million users worldwide. Tested, Approved and Recommended by Experts.
- Examples of CUDA implementations by Cutlass CuTe☆279Jul 1, 2025Updated last year
- Optimizing SGEMM kernel functions on NVIDIA GPUs to a close-to-cuBLAS performance.☆419Jan 2, 2025Updated last year
- ☆40Feb 28, 2020Updated 6 years ago
- This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several…☆1,320Jul 29, 2023Updated 2 years ago
- ☆21Mar 22, 2021Updated 5 years ago
- A practical way of learning Swizzle☆42Feb 3, 2025Updated last year
- ☆14Nov 3, 2025Updated 8 months ago
- ☆50Jun 27, 2019Updated 7 years ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆75Sep 8, 2024Updated last year
- Deploy open-source AI quickly and easily - Special Bonus Offer • AdRunpod Hub is built for open source. One-click deployment and autoscaling endpoints without provisioning your own infrastructure.
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆129Jul 13, 2024Updated last year
- ☆154Mar 18, 2024Updated 2 years ago
- BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.☆770Aug 6, 2025Updated 10 months ago
- play gemm with tvm☆91Jul 22, 2023Updated 2 years ago
- ☆268Jul 11, 2024Updated last year
- Simple example of how to write an Implicit GEMM Convolution in CUDA using the tensor core WMMA API and bindings for PyTorch.☆19Jun 29, 2023Updated 3 years ago
- Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity☆246Sep 24, 2023Updated 2 years ago