high performance computing on graphics processing units: hgpu.org

Programming

Lattice-boltzmann water waves

Robert Geist, Christopher Corsi, Jerry Tessendorf, James Westall

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Tags: 3D Graphics and Realism, Computer science, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 480, OpenCL, Rendering

September 11, 2011 by hgpu

Importance-driven compositing window management

Manuela Waldner, Markus Steinberger, Raphael Grasset, Dieter Schmalstieg

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Tags: Computer science, Interface tracking, nVidia, nVidia GeForce GTX 480, OpenCL, OpenGL, Visualization

September 11, 2011 by hgpu

Enabling multiple accelerator acceleration for Java/OpenMP

Ronald Veldema, Thorsten Blass, Michael Philippsen

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Tags: Benchmarking, Computer science, CUDA, Java, nVidia, OpenCL, OpenMP, OpenMPI, Tesla M1060

September 9, 2011 by hgpu

Programming Many-Core Chips

Andras Vajda

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Tags: Book, Cluster computing, Computer science, CUDA, MPI, nVidia, OpenCL, OpenMP, Programming techniques

September 8, 2011 by hgpu

GPU Computation in Bioinspired Algorithms: A Review

M. G. Arenas, A. M. Mora, G. Romero and P. A. Castillo

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Tags: Algorithms, ATI, Bioinformatics, Biology, Computational biology, CUDA, nVidia, OpenCL, Overview, Review

September 8, 2011 by hgpu

Towards GPGPU Assisted Computing in Virtualized Environments

Thilo Schmitt, Alexander Weggerle, Christian Himpel, Peter Schulthess

Tags: ATI, ATI Stream, Computer science, CUDA, nVidia, OpenCL, Review, Virtualization

September 8, 2011 by hgpu

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Jacquelyne Forgette, Renata Wachowiak-Smolikova, Mark Wachowiak

Tags: Algorithms, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 260, OpenCL, Signal processing

September 8, 2011 by hgpu

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Peter Thoman, Klaus Kofler, Heiko Studt, John Thomson, Thomas Fahringer

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, nVidia, nVidia GeForce GTX 275, nVidia GeForce GTX 460, OpenCL, Optimization, Tesla C2050

September 8, 2011 by hgpu

Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL

Leonid Djinevski, Igor Mishkovski, Dimitar Trajanov

Tags: Algorithms, Clustering, Computer science, Networks, OpenCL, OpenMP

September 8, 2011 by hgpu

CUDA-level performance with python-level productivity for Gaussian mixture model applications

H. Cook, E. Gonina, S. Kamil, G. Friedland, D. Patterson, A. Fox

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Python

September 7, 2011 by hgpu

Towards dynamic reconfigurable load-balancing for hybrid desktop platforms

Alecio P.D. Binotto, Carlos E. Pereira, Dieter W. Fellner

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Tags: Fluid dynamics, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, OpenCL, Poster

August 31, 2011 by hgpu

Partial wave analysis at BES III harnessing the power of GPUs

Niklaus Berger

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Tags: ATI Radeon HD 4870, Brook, High Energy Physics - Experiment, nVidia, OpenCL, Physics, Statistics and Probability

August 31, 2011 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Lattice-boltzmann water waves

Importance-driven compositing window management

Enabling multiple accelerator acceleration for Java/OpenMP

Programming Many-Core Chips

GPU Computation in Bioinspired Algorithms: A Review

Towards GPGPU Assisted Computing in Virtualized Environments

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL

CUDA-level performance with python-level productivity for Gaussian mixture model applications

Towards dynamic reconfigurable load-balancing for hybrid desktop platforms

Partial wave analysis at BES III harnessing the power of GPUs

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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