high performance computing on graphics processing units: hgpu.org

Programming

Solving the Ghost-Gluon System of Yang-Mills Theory on GPUs

Markus Hopfer, Reinhard Alkofer, Gundolf Haase

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Tags: Computational Physics, CUDA, High Energy Physics - Phenomenology, High Energy Physics - Theory, Integral equations, nVidia, nVidia GeForce GTX 560 Ti, OpenMPI, Physics, QCD, Tesla C2070

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

Point to point processing of digital images using parallel computing

Eric Olmedo, Jorge de la Calleja, Antonio Benitez, Ma. Auxilio Medina

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GT 430

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

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

Fast Morphological Image Processing Open-Source Extensions for GPU processing with CUDA

Matthew J. Thurley, Victor Danell

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 470, Package, Signal processing

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

Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines

Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris, Marc Levoy, Saman Amarasinghe, Fredo Durand

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Tags: Algorithms, CUDA, Filtering, Image processing, nVidia, Tesla C2070

June 8, 2012 by hgpu

Ameliorating Memory Contention of OLAP operators on GPU Processors

Evangelia A. Sitaridi, Kenneth A. Ross

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

June 8, 2012 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Solving the Ghost-Gluon System of Yang-Mills Theory on GPUs

Using the GPGPU for Scaling Up Mining Software Repositories

Point to point processing of digital images using parallel computing

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

Scaling Fast Multipole Methods up to 4000 GPUs

Fast Morphological Image Processing Open-Source Extensions for GPU processing with CUDA

Autotuning Stencil-Based Computations on GPUs

Encapsulated synchronization and load-balance in heterogeneous programming

Sparse LU Factorization for Parallel Circuit Simulation on GPU

Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines

Ameliorating Memory Contention of OLAP operators on GPU Processors

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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