Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruction.

CUDA编程练习项目-Hands-on CUDA kernels and performance optimization, covering GEMM, FlashAttention, Tensor Cores, CUTLASS, quantization, KV cache, NCCL, and profiling.

Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.

Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.

Multiple GEMM operators are constructed with cutlass to support LLM inference.

FastCuda is a handwritten CUDA operator library featuring progressive GEMM and Reduce kernels, cuBLAS benchmarking, and C/C++/Python interfaces for learning, profiling, and performance optimization.

Use tensor core to calculate back-to-back HGEMM (half-precision general matrix multiplication) with MMA PTX instruction.

Codes for DTC-SpMM (ASPLOS'24)

DeepGEMMPerTensor: clean and efficient FP8 GEMM per tensor kernels without scales

CUDA kernel optimization lab for GEMM, FlashAttention, quantization, and GPU performance learning.

CUDA kernels for LLM inference: FlashAttention forward, Tensor Core GEMM, PyTorch bindings, and benchmarkable reference implementations.

The lab assignments from CS4302 Parallel and Distributed Programming (2022 Fall) with my solutions

A reproducible GPU benchmarking lab that compares FP16 vs FP32 training on MNIST using PyTorch, CuPy, and Nsight profiling tools. This project blends performance engineering with cinematic storytelling—featuring NVTX-tagged training loops, fused CuPy kernels, and a profiler-driven README that narrates the GPU’s inner workings frame by frame.

Systematic CUDA kernel engineering from SGEMM fundamentals to reusable kernels, advanced optimization experiments, and lightweight inference components. https://lessup.github.io/cuda-kernel-academy/

Header-only C++/CUDA AI kernel library with OpenSpec-driven workflow and readable implementations of GEMM, attention, normalization, convolution, sparse ops, and Python bindings.

🎬 Explore GPU training efficiency with FP32 vs FP16 in this modular lab, utilizing Tensor Core acceleration for deep learning insights.

CUDA-native C++ Transformer inference engine with W8A16 quantization, KV cache management, and hand-tuned kernels.