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Experiences with High-Level Programming Directives for Porting Applications to GPUs

Oscar Hernandez, Wei Ding, Barbara Chapman, Ramanan Sankaran, and Richard Graham

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Tags: Computer science, CUDA, nVidia, OpenCL, Tesla C2070

June 13, 2012 by hgpu

Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation

Holger Schwenk, Anthony Rousseau, Mohammed Attik

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Tags: Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Package, Speech recognition, Tesla M2090

June 11, 2012 by hgpu

GPUSync: Architecture-Aware Management of GPUs for Predictable Multi-GPU Real-Time Systems

Glenn A. Elliott, Bryan C. Ward, James H. Anderson

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470, Package, Performance

June 11, 2012 by hgpu

Range query processing in a multi-GPU environment

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Databases, GPU cluster, nVidia, OpenMP, Tesla C1060

June 11, 2012 by hgpu

Using the GPGPU for Scaling Up Mining Software Repositories

Rina Nagano, Hiroki Nakamura, Yasutaka Kamei, Bram Adams, Kenji Hisazumi, Naoyasu Ubayashi, Akira Fukuda

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Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 480, Package

June 10, 2012 by hgpu

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

K. Amyx, J. Balasalle, I. Pogorelov, M. Borland, R. Soliday, Y.Wang

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Tags: Computer science, CUDA, nVidia, Tesla C2070

June 10, 2012 by hgpu

S-buffer: Sparsity-aware Multi-fragment Rendering

Andreas A. Vasilakis, Ioannis Fudos

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Tags: Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, OpenGL, Package, Rendering

June 10, 2012 by hgpu

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

June 10, 2012 by hgpu

Scaling Fast Multipole Methods up to 4000 GPUs

Rio Yokota, Lorena Barba, Tetsu Narumi, Kenji Yasuoka

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, MPI, N-body simulation, nVidia, Tesla M2050

June 9, 2012 by hgpu

Autotuning Stencil-Based Computations on GPUs

Azamat Mametjanov, Daniel Lowell, Ching-Chen May, Boyana Norris

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Tags: Computer science, CUDA, Differential equations, nVidia, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C1060, Tesla C2070

June 9, 2012 by hgpu

Encapsulated synchronization and load-balance in heterogeneous programming

Yuri Torres, Arturo Gonzalez-Escribano, Diego Llanos

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8500 GT, nVidia GeForce 9600 GT, Performance

June 9, 2012 by hgpu

Sparse LU Factorization for Parallel Circuit Simulation on GPU

Ling Ren, Xiaoming Chen, Yu Wang, Chenxi Zhang, Huazhong Yang

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Tags: ATI, ATI Radeon HD 5870, CAD, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

June 9, 2012 by hgpu

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

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

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high performance computing on graphics processing units: hgpu.org

Applications

Experiences with High-Level Programming Directives for Porting Applications to GPUs

Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation

GPUSync: Architecture-Aware Management of GPUs for Predictable Multi-GPU Real-Time Systems

Range query processing in a multi-GPU environment

Using the GPGPU for Scaling Up Mining Software Repositories

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

S-buffer: Sparsity-aware Multi-fragment Rendering

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

Scaling Fast Multipole Methods up to 4000 GPUs

Autotuning Stencil-Based Computations on GPUs

Encapsulated synchronization and load-balance in heterogeneous programming

Sparse LU Factorization for Parallel Circuit Simulation on GPU

Recent source codes

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

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

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