high performance computing on graphics processing units: hgpu.org

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A Generic Approach for Developing Highly Scalable Particle-Mesh Codes for GPUs

W. Honig, F. Schmitt, R. Widera, H. Burau, G. Juckeland, M. S. Muller, and M. Bussmann

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Tags: ATI Radeon HD 5770, Computer science, CUDA, nVidia, OpenCL, Particle-in-cell methods, Plasma physics, Tesla C2050, Tesla S1070

February 19, 2011 by hgpu

Performance Comparison of Cholesky Decomposition on GPUs and FPGAs

Depeng Yang, Junqing Sun, JunKu Lee, Getao Liang, David D. Jenkins, Gregory D. Peterson, and Husheng Li

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Tags: Computer science, FPGA, Matrix decomposition, nVidia, nVidia GeForce GTX 480, OpenCL, Performance

February 18, 2011 by hgpu

OpenCL Evaluation for Numerical Linear Algebra Library Development

Peng Du, Piotr Luszczek, Jack Dongarra

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Tags: ATI Radeon HD 5870, Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla C2050

February 17, 2011 by hgpu

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Byunghyun Jang, Dana Schaa, Perhaad Mistry, and David Kaeli

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Tags: Computer science, Matrix multiplication, Memory model, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 285, OpenCL, Optimization

February 16, 2011 by hgpu

An experimental study on performance portability of OpenCL kernels

Sean Rul, Hans Vandierendonck, Joris D'Haene and Koen De Bosschere

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Tags: ATI, ATI FirePro V8700, Cell processor, Computer science, nVidia, OpenCL, Performance, Playstation, Tesla C1060

February 16, 2011 by hgpu

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

K.A. Hawick, A. Leist, and D.P. Playne

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Tags: Cell processor, Computer science, CUDA, Data parallelism, Finite difference, nVidia, nVidia GeForce GTX 295, OpenCL, Partial differential equations, PDEs, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Comparison of several parallel API for cloth modelling on modern GPUs

Tzvetomir Ivanov Vassilev

Tags: Computer science, CUDA, GLSL, nVidia, OpenCL, OpenGL, Performance

February 11, 2011 by hgpu

A New Data Layout For Set Intersection on GPUs

Rasmus Resen Amossen, Rasmus Pagh

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Tags: Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 285, OpenCL, PyOpenCL

February 7, 2011 by hgpu

Computational Fluid Dynamic on GPU

Ting Xingdong, Sen Li

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Tags: CUDA, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, OpenCL, Poster

February 1, 2011 by hgpu

Introduction to GPU programming for EDA

John F. Croix, Sunil P. Khatri

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Tags: Computational Engineering, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5800, OpenCL, Presentation, Review

February 1, 2011 by hgpu

OpenRCL: Low-Power High-Performance Computing with Reconfigurable Devices

Mingjie Lin, Ilia Lebedev, John Wawrzynek

Tags: Compilers, Computer science, FPGA, nVidia, nVidia GeForce 9400 M, OpenCL

January 31, 2011 by hgpu

Correlating Radio Astronomy Signals with Many-Core Hardware

Rob van Nieuwpoort, John Romein

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Tags: Astrophysics, ATI, ATI CAL, ATI Radeon HD 4870, ATI Stream, Brook, Cell processor, CUDA, nVidia, OpenCL, Performance, Physics, Signal processing, Tesla C1060

January 26, 2011 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

A Generic Approach for Developing Highly Scalable Particle-Mesh Codes for GPUs

Performance Comparison of Cholesky Decomposition on GPUs and FPGAs

OpenCL Evaluation for Numerical Linear Algebra Library Development

Static Memory Access Pattern Analysis on a Massively Parallel GPU

An experimental study on performance portability of OpenCL kernels

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Comparison of several parallel API for cloth modelling on modern GPUs

A New Data Layout For Set Intersection on GPUs

Computational Fluid Dynamic on GPU

Introduction to GPU programming for EDA

OpenRCL: Low-Power High-Performance Computing with Reconfigurable Devices

Correlating Radio Astronomy Signals with Many-Core Hardware

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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