🤖FFPA: Extends FlashAttention-2 via Split-D for large headdims, 1.5x~3×↑🎉 vs SDPA, up to 430T🎉 on H200.
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Jun 29, 2026 - Python
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tensor-cores
Here are 38 public repositories matching this topic...
⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.
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May 10, 2025 - Cuda
General Matrix Multiplication using NVIDIA Tensor Cores
matrix-multiplication cuda-kernels gpu-computing nvidia-cuda nvidia-gpu gpu-programming sgemm cuda-programming tensor-cores nvidia-tensor-cores
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Jan 25, 2025 - Cuda
Minimal FlashAttention in CUDA C++/CuTe: readable WMMA/CuTe kernels, no NxN workspace, up to 4.5x faster than naive PyTorch
cuda attention cutlass cute gpu-kernels pytorch-extension tensor-cores llm flash-attention flashattention wmma
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Jun 2, 2026 - Cuda
Handwritten Flash Attention 2 CUDA kernel for Blackwell (SM120) with TMA, swizzle, double buffering & warp specialization
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May 25, 2026 - Cuda
CUDA matrix multiplication benchmarking on Jetson Orin Nano. Four implementations, three power modes, five matrix sizes. 99.5% mathematical validation. C++/CUDA and Python.
machine-learning robotics cuda cublas scientific-computing matrix-multiplication high-performance-computing gpu-computing performance-optimization autonomous-systems edge-computing nvidia-jetson edge-ai embeded-systems tensor-cores ml-deployment jetson-orin-nano gpu-benchmarking power-efficiency-benchmark cuda-optimization
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Apr 2, 2026 - Python
Lynn 原生 LLM 推理引擎 · W4A8/NVFP4 量化 · 自写 CUDA/Triton kernel · MoE · 投机解码 | Lynn-native LLM inference engine for NVIDIA Blackwell
python deep-learning gpu cuda pytorch high-performance-computing triton gpu-acceleration quantization inference-engine mixture-of-experts tensor-cores blackwell ai-inference llm fp8 llm-serving llm-inference speculative-decoding nvfp4
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Jun 8, 2026 - Python
Vulkan & GLSL implementation of FlashAttention-2
vulkan glsl artificial-intelligence gpu-acceleration attention gpu-computing deel-learning tensor-cores large-language-models llm flash-attention flash-attention-2
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Jan 19, 2025 - C++
CUDA 12-first backend inference for Unsloth on Kaggle — Optimized for small GGUF models (1B-5B) on dual Tesla T4 GPUs (15GB each, SM 7.5)
python machine-learning ai deep-learning jupyter gpu cuda inference pytorch nvidia cuda-kernels google-colab tensor-cores tesla-t4 llm gguf unsloth flashattention
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Feb 1, 2026 - Jupyter Notebook
Bilingual CUDA SGEMM optimization tutorial and reference implementation, from naive kernels to Tensor Core WMMA | 双语 CUDA SGEMM 优化教程与参考实现，从朴素内核到 Tensor Core WMMA
tutorial cuda matrix-multiplication high-performance-computing cuda-kernels shared-memory gemm sgemm gpu-optimization bank-conflict tensor-cores wmma
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Jul 1, 2026 - Cuda
A benchmarking framework for correlators of FX telescope arrays
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Oct 20, 2023 - Cuda
Production-grade FlashAttention FP8 e4m3 forward kernel for NVIDIA Blackwell consumer GPUs (sm_120a, e.g. RTX PRO 6000). 647–652 TFLOPS at hd=128, sl=8192. Multi-kernel dispatcher, C library with Go and Python bindings
cuda transformer attention gpu-kernels tensor-cores blackwell fp8 flash-attention fp8e4m3 sm120 rtx-pro-6000 sm-120a
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Jun 12, 2026 - Cuda
The only deployed sparse FP4 GEMM on SM120: beats CUTLASS 80b on every shape, wins end-to-end request latency in 81 of 112 serving regimes vs dense NVFP4.
cuda sparse quantization gpu-kernels mixed-precision tensor-cores blackwell vllm llm-inference fp4 sm120 nvfp4 2-4-sparsity
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Jul 10, 2026 - Cuda
Drop-in exact bf16 flash-attention for CUDA with a deterministic backward, tuned for Blackwell (sm_120 / RTX 5090).
deep-learning gpu cuda inference pytorch transformer triton attention memory-efficient attention-mechanism deterministic memory-optimization bfloat16 tensor-cores blackwell kv-cache llm-inference flash-attention rtx-5090 sm120
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Jul 8, 2026 - Python
INT8 Sparse Tensor Core GEMM for PyTorch — built for Windows
windows gpu cuda inference pytorch nvidia sparse quantization gemm int8 ptx structured-sparsity tensor-cores vram-optimization
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Feb 16, 2026 - Cuda
The MNIST classification problem is a fundamental machine learning task that involves recognizing handwritten digits (0- 9) from a dataset of 70,000 grayscale images (28x28 pixels each). It serves as a benchmark for evaluating machine learning models, particularly neural networks.
benchmarking deep-learning parallel-computing cuda mnist neural-networks high-performance-computing gpu-acceleration profiling shared-memory openacc performance-optimization c-cpp nsight tensor-cores cuda-streams pinned-memory
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Sep 12, 2025 - Cuda
High-performance CUDA kernels with step-by-step optimization, profiling, and analysis. A growing collection of GPU solutions demonstrating warp-level tuning, memory optimization, and Tensor Core acceleration.
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Nov 12, 2025 - Cuda
Hand-tuned NVIDIA SASS kernels for RTX 3070 Ti (GA104, sm_86): 31,910 GFLOPS HGEMM, 41,721 dense-equiv 2:4 sparse, 11,453 GFLOPS Flash Attention, no cuBLAS / cuDNN / PyTorch. Includes cuasmR, a CRAN-ready R package for cubin read/write + GPU benchmark measurement. 6-chapter tutorial + Chladni-pattern memory layout study.
sass performance-engineering cuda nvidia rstats gpu-computing r-package ampere gemm cymatics gpu-programming tensor-cores int8-quantization sparse-gemm hgemm flash-attention kernel-optimization sm-86 rtx-3070-ti cuassembler
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Jun 5, 2026 - Cuda
RA-SpMM: Regime-Aware Sparse Matrix Multiplication for GNN Workloads on GPUs. 8-rule router, 6 preprocessing-free kernels, 3.25x over cuSPARSE (FGCS 2026).
cuda benchmarks gpu-computing graph-neural-networks gnn spmm tensor-cores sparse-matrix-multiplication sparse-linear-algebra
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Jul 9, 2026 - Python
Neural Network C is an advanced neural network implementation in pure C, optimized for high performance on CPUs and NVIDIA GPUs.
machine-learning encryption deep-learning neural-network openmp mpi parallel-computing cuda transformers artificial-intelligence high-performance-computing convolutional-neural-networks gpu-computing quantization bayesian-optimization multi-gpu c-programming real-time-monitoring secure-computing tensor-cores
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Mar 29, 2025 - C
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