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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

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

Zheng Li, Weiyan Wang, Ruiyuan Li, Chao Chen, Xianlei Long, Linjiang Zheng, Quanqing Xu, Chuanhui Yang

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Tags: Algorithms, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce RTX 5080, Package

November 16, 2025 by hgpu

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

Zijian Zhang, Rong Wang, Shiyang Li, Yuebo Luo, Mingyi Hong, Caiwen Ding

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, nVidia GeForce RTX 4090, nVidia RTX 6000 Ada, Package, Performance

November 9, 2025 by hgpu

RDMA Point-to-Point Communication for LLM Systems

Nandor Licker, Kevin Hu, Vladimir Zaytsev, Lequn Chen

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H200, Package, Performance, RDMA

November 9, 2025 by hgpu

AMD MI300X GPU Performance Analysis

Chandrish Ambati, Trung Diep

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Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, nVidia H100, Performance

November 9, 2025 by hgpu

FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error

Fengjuan Wang, Zhiyi Su, Xingzhu Hu, Cheng Wang, Mou Sun

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Tags: Artificial intelligence, Computer science, Machine learning, nVidia, nVidia H100

November 9, 2025 by hgpu

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

Brandon Alexander Burtchell, Martin Burtscher

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Tags: Algorithms, Compilers, Compression, Computer science, CUDA, HIP, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce RTX 3080 Ti, nVidia GeForce RTX 4090, Package

November 9, 2025 by hgpu

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

Marcin Spoczynski, Marcela S. Melara

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Tags: Computer science, Machine learning, nVidia, oneAPI, Package, PyTorch, Rust, Security, SYCL

November 2, 2025 by hgpu

Serve Programs, Not Prompts

In Gim, Lin Zhong

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Tags: Computer science, LLM, NLP

November 2, 2025 by hgpu

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

Patricia Siwinska,Jie Lei,Adrian Castello,Pedro Alonso-Jord́a,Enrique S. Quintana-Orti

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, Package, performance portability

November 2, 2025 by hgpu

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Zhongzhen Wen, Hongyu Liu, Tingwei Zhu, Minxue Pan, Shaohua Wang, Yuanyi Lin, Kairui Liu, Tian Zhang, Xuandong Li

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Tags: Computer science, CUDA, Deep learning, nVidia, Package, PyTorch

November 2, 2025 by hgpu

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

Mengzhao Chen, Meng Wu, Hui Jin, Zhihang Yuan, Jing Liu, Chaoyi Zhang, Yunshui Li, Jie Huang, Jin Ma, Zeyue Xue, Zhiheng Liu, Xingyan Bin, Ping Luo

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Tags: Computer science, CUDA, Machine learning, nVidia, Package

November 2, 2025 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

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

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

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

RDMA Point-to-Point Communication for LLM Systems

AMD MI300X GPU Performance Analysis

FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

Serve Programs, Not Prompts

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

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