high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Chai: Collaborative Heterogeneous Applications for Integrated-architectures

Juan Gomez-Luna, Izzat El Hajj, Li-Wen Chang, Victor Garcia-Flores, Simon Garcia de Gonzalo, Thomas B. Jablin, Antonio J. Pena, Wen-mei Hwu

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

April 3, 2017 by hgpu

FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis

Kohei Nagasu, Kentaro Sano, Fumiya Kono, Naohito Nakasato

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Tags: AMD Radeon R9 280X, ATI, Earth and Space Sciences, FPGA, nVidia, OpenCL, Performance, Tesla K20

April 3, 2017 by hgpu

LookNN: Neural Network with No Multiplication

Mohammad Samragh Razlighi, Mohsen Imani, Farinaz Koushanfar, Tajana Rosing

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, Neural networks, OpenCL

March 28, 2017 by hgpu

Energy conservation techniques for GPU computing

Xinxin Mei

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Task scheduling, Thesis

March 28, 2017 by hgpu

Parallelized Vlasov-Fokker-Planck solver for desktop personal computers

Patrik Schonfeldt, Miriam Brosi, Markus Schwarz, Johannes L. Steinmann, Anke-Susanne Muller

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Tags: AMD Radeon R9 290, ATI, OpenCL, Package, Physics

March 28, 2017 by hgpu

APUNet: Revitalizing GPU as Packet Processing Accelerator

Younghwan Go, Muhammad Jamshed, YoungGyoun Moon, Changho Hwang, KyoungSoo Park

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Tags: Algorithms, AMD, APU, ATI, Computer science, nVidia, nVidia GeForce GTX 980, OpenCL, Performance

March 28, 2017 by hgpu

Comparing Programmer Productivity in OpenACC and CUDA: an Empirical Investigation

Xuechao Li, Po-Chou Shih, Jeffrey Overbey, Cheryl Seals, Alvin Lim

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Tags: Computer science, CUDA, nVidia, OpenACC, Package

March 20, 2017 by hgpu

Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matlab, Neural networks, nVidia, nVidia GeForce GTX 960, Package, TensorFlow, Torch

March 20, 2017 by hgpu

GPU accelerated population annealing algorithm

Lev Yu. Barash, Martin Weigel, Michal Borovsky, Wolfhard Janke, Lev N. Shchur

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Tags: Algorithms, CUDA, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla K80

March 14, 2017 by hgpu

Compiling Parallel Functional Code with Data Parallel Idealised Algol

Robert Atkey, Michel Steuwer, Sam Lindley, Christophe Dubach

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, performance portability

March 14, 2017 by hgpu

Large-scale image analysis using docker sandboxing

B. Sengupta, E. Vazquez, M. Sasdelli, Y. Qian, M. Peniak, L. Netherton, G. Delfino

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia Quadro M 6000

March 14, 2017 by hgpu

Model-independent partial wave analysis using a massively-parallel fitting framework

Liang Sun, Rafael Aoude, Alberto Correa Dos Reis, Michael David Sokoloff

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Tags: Algorithms, CUDA, High Energy Physics - Experiment, nVidia, OpenMP, Package, Physics, Tesla K40

March 14, 2017 by hgpu

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

Chai: Collaborative Heterogeneous Applications for Integrated-architectures

FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis

LookNN: Neural Network with No Multiplication

Energy conservation techniques for GPU computing

Parallelized Vlasov-Fokker-Planck solver for desktop personal computers

APUNet: Revitalizing GPU as Packet Processing Accelerator

Comparing Programmer Productivity in OpenACC and CUDA: an Empirical Investigation

Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

GPU accelerated population annealing algorithm

Compiling Parallel Functional Code with Data Parallel Idealised Algol

Large-scale image analysis using docker sandboxing

Model-independent partial wave analysis using a massively-parallel fitting framework

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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