high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

Harri Renney, Benedict R. Gaster, Tom Mitchell

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Tags: AMD Radeon 530, ATI, FDTD, Finite-difference time-domain, OpenCL, OpenGL, Package, Physics

April 14, 2019 by hgpu

Distributed Deep Learning Strategies For Automatic Speech Recognition

Wei Zhang, Xiaodong Cui, Ulrich Finkler, Brian Kingsbury, George Saon, David Kung, Michael Picheny

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Tags: Computer science, CUDA, Deep learning, LSTM, Machine learning, nVidia, OpenMPI, Speech recognition, Tesla P100

April 14, 2019 by hgpu

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

John Lawson, Mehdi Goli, Duncan McBain, Daniel Soutar, Louis Sugy

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Tags: AMD R9 Nano, ATI, BLAS, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, OpenCL, Package, Performance, performance portability, SYCL

April 14, 2019 by hgpu

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

Ulysse Beaugnon, Basile Clément, Nicolas Tollenaere, Albert Cohen

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Tags: Analytical model, Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro K4000, Programming Languages

April 14, 2019 by hgpu

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

Kun Yang, Yi-Fan Chen, Georgios Roumpos, Chris Colby, John Anderson

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Tags: Cloud, CUDA, Ising model, Machine learning, Monte Carlo simulation, Neural networks, nVidia, Package, Physics, Tesla V100, TPU

April 7, 2019 by hgpu

The Study of the OpenCL Processing Models for the FPGA Devices

Piotr Szkotak, Pawel Russek, Kazimierz Wiatr

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Tags: AMD Radeon R9 290, ATI, Computer science, FPGA, HLS, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

April 7, 2019 by hgpu

Full-System Simulation of Mobile CPU/GPU Platforms

Kuba Kaszyk, Harry Wagstaff, Tom Spink, Bjorn Franke, Mike O'Boyle, Bruno Bodin

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Tags: Computer science, Computer vision, OpenCL

April 7, 2019 by hgpu

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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Tags: Computer science, CUDA, NLP, nVidia, Package, PyTorch, Video

April 7, 2019 by hgpu

Methods for Accelerating Machine Learning in High Performance Computing

Robert Lim

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla P100, Thesis

March 31, 2019 by hgpu

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

Dynamic Application Autotuning for Self-Aware Approximate Computing

Davide Gadioli

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Tags: Auto-Tuning, Computer science, OpenCL, Package, Thesis

March 31, 2019 by hgpu

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Masab Ahmad, Halit Dogan, Christopher J. Michael, Omer Khan

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Tags: Algorithms, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL, Package, Performance

March 31, 2019 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

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high performance computing on graphics processing units: hgpu.org

Programming

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

Distributed Deep Learning Strategies For Automatic Speech Recognition

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

The Study of the OpenCL Processing Models for the FPGA Devices

Full-System Simulation of Mobile CPU/GPU Platforms

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Methods for Accelerating Machine Learning in High Performance Computing

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Dynamic Application Autotuning for Self-Aware Approximate Computing

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

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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