high performance computing on graphics processing units: hgpu.org

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Scaling Radio Astronomy Signal Correlation on Heterogeneous Supercomputers Using Various Data Distribution Methodologies

Ruonan Wang, Christopher Harris

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Tags: Astrophysics, GPU cluster, Heterogeneous systems, Instrumentation and Methods for Astrophysics, nVidia, OpenCL, Signal processing, Tesla C2075

May 27, 2013 by hgpu

On-Demand Generating and 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

May 25, 2013 by hgpu

OpenCL Performance Evaluation on Modern Multi Core CPUs

Joo Hwan Lee, Kaushik Patel, Nimit Nigania, Hyojong Kim, Hyesoon Kim

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

May 25, 2013 by hgpu

Quantifying the Energy Efficiency of FFT on Heterogeneous Platforms

Yash Ukidave, Amir Kavyan Ziabari, Perhaad Mistry, Gunar Schirner, David Kaeli

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Tags: ATI, ATI Radeon HD 7770, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Signal processing

May 25, 2013 by hgpu

Composing multiple StarPU applications over heterogeneous machines: a supervised approach

A.-E Hugo A. Guermouche P.-A. Wacrenier R. Namyst

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Tesla M2070

May 23, 2013 by hgpu

Sequential Consistency for Heterogeneous-Race-Free: Programmer-centric Memory Models for Heterogeneous Platforms

Derek R. Hower, Bradford M. Beckmann, Benedict R. Gaster, Blake A. Hechtman, Mark D. Hill, Steven K. Reinhardt, David A. Wood

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Performance

May 23, 2013 by hgpu

CLgrep: A Parallel String Matching Tool

Peng Wu

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Source codes

Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Package, String matching, Thesis

May 20, 2013 by hgpu

Solving Linear Equations with Conjugate Gradient Method on OpenCL Platforms

Caner Sayin

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Tags: Computer science, Heterogeneous systems, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla C2050, Tesla C2070, Thesis

May 20, 2013 by hgpu

In-Place Recursive Approach for All-Pairs Shortest Paths Problem Using OpenCL

Manish Pandey, Himanshu Pandey, Sanjay Sharma

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Tags: Algorithms, ATI, ATI Radeon HD 6850, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 630 M, OpenCL

May 19, 2013 by hgpu

Programming for scientific computing on peta-scale heterogeneous parallel systems

Yang Can-qun, Wu Qiang, Tang Tao, Wang Feng, Xue Jing-ling

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Tags: Computer science, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, Tesla M2050

May 13, 2013 by hgpu

A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets

Da Li, Kittisak Sajjapongse, Huan Truong, Gavin Conant, Michela Becchi

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Tags: Bioinformatics, Biology, Computational biology, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, nVidia Quadro FX 2000, Sequence alignment, Tesla K20

May 11, 2013 by hgpu

libWater: Heterogeneous Distributed Computing Made Easy

Ivan Grasso, Simone Pellegrini, Biagio Cosenza, Thomas Fahringer

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Distributed computing, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 480, OpenCL

May 9, 2013 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Scaling Radio Astronomy Signal Correlation on Heterogeneous Supercomputers Using Various Data Distribution Methodologies

On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms

OpenCL Performance Evaluation on Modern Multi Core CPUs

Quantifying the Energy Efficiency of FFT on Heterogeneous Platforms

Composing multiple StarPU applications over heterogeneous machines: a supervised approach

Sequential Consistency for Heterogeneous-Race-Free: Programmer-centric Memory Models for Heterogeneous Platforms

CLgrep: A Parallel String Matching Tool

Solving Linear Equations with Conjugate Gradient Method on OpenCL Platforms

In-Place Recursive Approach for All-Pairs Shortest Paths Problem Using OpenCL

Programming for scientific computing on peta-scale heterogeneous parallel systems

A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets

libWater: Heterogeneous Distributed Computing Made Easy

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)