high performance computing on graphics processing units: hgpu.org

Programming

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An OpenMP Programming Environment on Mobile Devices

Tyng-Yeu Liang, Hung-Fu Li, Yu-Chih Chen

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Tags: Computer science, Heterogeneous systems, LLVM, OpenCL, OpenMP

May 28, 2016 by hgpu

Theano-MPI: a Theano-based Distributed Training Framework

He Ma, Fei Mao, Graham W. Taylor

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Tags: Computer science, CUDA, Deep learning, Machine learning, MPI, nVidia, Package, Tesla K20, Tesla K80

May 28, 2016 by hgpu

Faster GPU-based convolutional gridding via thread coarsening

Bruce Merry

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Tags: Algorithms, AMD Radeon R9 290X, Astrophysics, ATI, Computer science, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 980, OpenCL

May 26, 2016 by hgpu

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua

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Tags: Algorithms, ARM, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Performance

May 26, 2016 by hgpu

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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Tags: Algorithms, CNN, Computer science, Deep learning, Finance, Intel Xeon Phi, Neural networks

May 26, 2016 by hgpu

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Zhe Zhou, Wenrui Diao, Xiangyu Liu, Zhou Li, Kehuan Zhang, Rui Liu

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Tags: AMD Radeon R7 250, ATI, Cloud, Computer science, nVidia, nVidia GeForce GTX 750, OpenCL, Operating systems, Security

May 26, 2016 by hgpu

PROJECTION Algorithm for Motif Finding on GPUs

Jhoirene B. Clemente, Francis George C. Cabarle, Henry N. Adorna

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Tags: Algorithms, Biology, Computational biology, CUDA, nVidia

May 26, 2016 by hgpu

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Philipp Gysel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K40, Thesis

May 23, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 2016 by hgpu

Graphics Supercomputing Applied to Brain Image Analysis with NiftyReg

Francisco Nurudin Alvarez Gonzalez

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Tags: CUDA, Image processing, Image registration, Medicine, nVidia, nVidia GeForce GTX 980, Thesis

May 23, 2016 by hgpu

ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Computer science, Image processing, nVidia, nVidia GeForce GTX 960, OpenCL, Performance, performance portability, Programming Languages, Tesla K40

May 23, 2016 by hgpu

A Practical Performance Model for Compute and Memory Bound GPU Kernels

Elias Konstantinidis, Yiannis Cotronis

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Tags: Benchmarking, CUDA, OpenCL, Performance prediction

May 23, 2016 by ekondis

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

An OpenMP Programming Environment on Mobile Devices

Theano-MPI: a Theano-based Distributed Training Framework

Faster GPU-based convolutional gridding via thread coarsening

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

PROJECTION Algorithm for Motif Finding on GPUs

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Graphics Supercomputing Applied to Brain Image Analysis with NiftyReg

ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems

A Practical Performance Model for Compute and Memory Bound GPU Kernels

Recent source codes

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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