high performance computing on graphics processing units: hgpu.org

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Implementing and Evaluating Candidate-Based Invariant Generation

Adam Betts, Nathan Chong, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Software Engineering

December 10, 2016 by hgpu

Integrating a large-scale testing campaign in the CK framework

Andrei Lascu, Alastair F. Donaldson

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Tags: Computer science, OpenCL, Software Engineering

November 11, 2015 by hgpu

Contract-Based General-Purpose GPU Programming

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package, Software Engineering

October 27, 2014 by hgpu

Teaching Parallel Programming Using Java

Aamir Shafi, Aleem Akhtar, Ansar Javed, Bryan Carpenter

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Tags: Computer science, CUDA, Education, Hadoop, Java, MapReduce, MPI, nVidia, Programming techniques, Software Engineering

October 4, 2014 by hgpu

GPGPU Programming for Games and Science

David H. Eberly

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Tags: Algorithms, Book, Computer science, DirectX, Games, HLSL, Package, Software Engineering

August 21, 2014 by hgpu

Numerical Computations with GPUs

Volodymyr Kindratenko

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Tags: Algorithms, ATI, Book, Computer science, CUDA, Differential equations, FFT, Linear Algebra, Mathematics, Numerical simulation, nVidia, OpenCL, Pseudo-random number generators, Software Engineering

August 13, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

Mohammadhossein Afrasiabi

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 580, nVidia GeForce GTX 590, Rendering, Sensing, Software Engineering, Thesis

December 13, 2013 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Jonathan Passerat-Palmbach

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Tags: Algorithms, Computer science, CUDA, Java, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Pseudo-random number generators, Software Engineering, Stochastic simulation, Tesla C2050, Tesla C2075, Tesla K20, Thesis

October 13, 2013 by hgpu

Cudagrind: A Valgrind Extension for CUDA

Thomas M. Baumann, Jose Gracia

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Tags: Computer science, CUDA, Memory, nVidia, Package, Software Engineering

October 4, 2013 by hgpu

Improving Resource Utilization in Heterogeneous CPU-GPU Systems

Michael Boyer

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Tags: Algorithms, ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Biology, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Package, Software Engineering, Thesis

September 23, 2013 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Implementing and Evaluating Candidate-Based Invariant Generation

Integrating a large-scale testing campaign in the CK framework

Contract-Based General-Purpose GPU Programming

Teaching Parallel Programming Using Java

GPGPU Programming for Games and Science

Numerical Computations with GPUs

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

Multilayered Abstractions for Partial Differential Equations

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Cudagrind: A Valgrind Extension for CUDA

Improving Resource Utilization in Heterogeneous CPU-GPU Systems

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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