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Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

Aaron Jarmusch, Sunita Chandrasekaran

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Tags: Benchmarking, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia B200, nVidia H200, PTX

December 7, 2025 by hgpu

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Stepan Vanecek, Manuel Walter Mussbacher, Dominik Groessler, Urvij Saroliya, Martin Schulz

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI210, AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia H100, nVidia Quadro P 6000, nVidia V100, Package, PTX

November 16, 2025 by hgpu

Towards Calculating HPC CUDA Kernel Performance on Nvidia GPUs

Dumeni Manatschal

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 3080, Performance, PTX, Thesis

September 14, 2025 by hgpu

Fuzz4cuda: Fuzzing Your Nvidia Gpu Libraries Through Debug Interface

Yuhao Zhou, Peng Jia, Jiayong Liu, Ximing Fan

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Tags: Computer science, CUDA, nVidia, nVidia RTX A6000, PTX, Security

August 24, 2025 by hgpu

Dissecting the NVIDIA Blackwell Architecture with Microbenchmarks

Aaron Jarmusch, Nathan Graddon, Sunita Chandrasekaran

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, nVidia GeForce RTX 5080, nVidia H100, Performance, PTX

July 20, 2025 by hgpu

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Ahmed Heakl, Sarim Hashmi, Gustavo Bertolo Stahl, Seung Hun Eddie Han, Salman Khan, Abdulrahman Mahmoud

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Tags: AI, AMD Radeon RX 7900 XT, ATI, Computer science, CUDA, HIP, Machine learning, nVidia, nVidia A100, OpenCL, Package, Programming Languages, PTX

May 25, 2025 by hgpu

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

Dimitar Mileski, Nikola Petrovski, Marjan Gusev

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Tags: Computer science, CUDA, Deep learning, HPC, LLM, nVidia, nVidia A100, PTX, PyTorch

April 13, 2025 by hgpu

TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives

Size Zheng, Jin Fang, Xuegui Zheng, Qi Hou, Wenlei Bao, Ningxin Zheng, Ziheng Jiang, Dongyang Wang, Jianxi Ye, Haibin Lin, Li-Wen Chang, Xin Liu

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Tags: Computer science, Deep learning, LLM, nVidia, nVidia H800, PTX

March 30, 2025 by hgpu

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Timothée David--Cléris, Guillaume Laibe, Yona Lapeyre

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Tags: AMD, AMD Radeon Instinct MI250X, Astrophysics, CUDA, MPI, nVidia, nVidia A100, OpenMP, Package, Physics, PTX, ROCm, SYCL

March 23, 2025 by hgpu

Mpache: Interaction Aware Multi-level Cache Bypassing on GPUs

Mengyue Xi, Tianyu Guo, Xuanteng Huang, Zejia Lin, Xianwei Zhang

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Tags: cache, Compilers, Computer science, nVidia, PTX

March 10, 2025 by hgpu

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Yafan Huang, Sheng Di, Guanpeng Li, Franck Cappello

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Tags: Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Package, PTX

February 16, 2025 by hgpu

Fully-Automated Code Generation for Efficient Computation of Sparse Matrix Permanents on GPUs

Deniz Elbek, Kamer Kaya

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia A100, nVidia Quadro GV100, PTX, Sparse matrix

February 3, 2025 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

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high performance computing on graphics processing units: hgpu.org

Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Towards Calculating HPC CUDA Kernel Performance on Nvidia GPUs

Fuzz4cuda: Fuzzing Your Nvidia Gpu Libraries Through Debug Interface

Dissecting the NVIDIA Blackwell Architecture with Microbenchmarks

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

Mpache: Interaction Aware Multi-level Cache Bypassing on GPUs

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Fully-Automated Code Generation for Efficient Computation of Sparse Matrix Permanents on GPUs

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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