🤖FFPA: Extends FlashAttention-2 via Split-D for large headdims, 1.5x~3×↑🎉 vs SDPA, up to 430T🎉 on H200.
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May 30, 2026 - Python
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tensor-cores
Here are 30 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
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
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++
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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May 28, 2026 - Python
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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May 28, 2026 - Cuda
A benchmarking framework for correlators of FX telescope arrays
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Oct 20, 2023 - Cuda
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
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
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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Apr 30, 2026 - Python
Tensor-core CUDA kernels for Nyström attention, linear-time forward and backward with exact autograd gradients. Faster than flash-attention at long sequence length.
cuda pytorch transformer attention attention-mechanism tensor-cores linear-attention nystromformer long-context flash-attention
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May 24, 2026 - Cuda
🔍 Analyze CUDA matrix multiplication performance and power consumption on NVIDIA Jetson Orin Nano across multiple implementations and settings.
machine-learning robotics cuda cublas matrix-multiplication high-performance-computing gpu-computing performance-optimization autonomous-systems edge-computing nvidia-jetson embeded-systems tensor-cores ml-deployment jetson-orin-nano gpu-benchmarking power-efficiency-benchmark cuda-optimization
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May 30, 2026 - Python
10,000-image LeNet-5 forward pass in ~28 ms on a single A40 via fused convolution and Tensor Cores (TF32).
parallel-computing cuda inference cnn matrix-multiplication lenet convolution gpu-computing ampere gpu-programming lenet-5 im2col cuda-programming tensor-cores kernel-optimization tf32 wmma
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Apr 26, 2026 - Cuda
TsuruTune is a comprehensive deep learning model optimization tool designed specifically for NVIDIA Jetson platforms and edge devices.. It leverages Tensor Core acceleration and memory bandwidth alignment to achieve optimal performance for deep learning inference on edge devices.
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Jan 8, 2026 - Python
Multi-GPU CUDA stress test with Tensor Core power filler for board power testing
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May 25, 2026 - C++
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