high performance computing on graphics processing units: hgpu.org

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Toward a GPU-Accelerated Immersed Boundary Method for Wind Forecasting Over Complex Terrain

Rey DeLeon, Kyle Felzien, Inanc Senocak

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Tags: CUDA, Earth and Space Sciences, GPU cluster, MPI, nVidia, Tesla S1070, Weather prediction, Weather Prediction Model

September 28, 2013 by hgpu

Sparse Matrix Algorithms Using GPGPU

Kaupo Kuresson

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Computer science, nVidia, OpenCL, Package, Sparse matrix, Tesla S1070, Tesla T10, Thesis

September 18, 2013 by hgpu

A Stochastic-based Optimized Schwarz Method for the Gravimetry Equations on GPU Clusters

Abal-Kassim Cheik Ahamed, Frederic Magoules

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Tags: Algorithms, CUDA, Earth and Space Sciences, Geoscience, GPU cluster, nVidia, Tesla S1070

September 2, 2013 by hgpu

The development and expansion of HOOMD-blue through six years of GPU proliferation

Joshua A. Anderson, Sharon C. Glotzer

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Tags: Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 580, nVidia GeForce GTX Titan, Package, Physics, Python, Software Engineering, Tesla S1070

August 27, 2013 by hgpu

Performance Evaluation of the Ocean-Land-Atmosphere Model Using Graphics Processing Units

Claudio Schepke, Nicolas Maillard

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Tags: CUDA, Earth and Space Sciences, nVidia, Tesla S1070

July 22, 2013 by hgpu

GPU Acceleration of Algebraic Multigrid for Low-Frequency Finite Element Methods

Elia A. Attardo, Andrea Borsic

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Tags: Finite element method, GPU AMG, nVidia, Tesla S1070

June 9, 2013 by egpu

Efficient Execution of AMR Computations on GPU Systems

Hari K. Raghavan

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Tags: Computer science, CUDA, Finite difference, Numerical simulation, nVidia, Tesla C2070, Tesla S1070, Thesis, Visualization

June 4, 2013 by hgpu

From Experiment to Design – Fault Characterization and Detection in Parallel Computer Systems Using Computational Accelerators

Keun Soo Yim

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Tags: Computer science, CUDA, Fault tolerance, Heterogeneous systems, MPI, nVidia, Tesla S1070, Thesis

May 30, 2013 by hgpu

Modeling and Optimization of Parallel Matrix-based Computations on GPU

Andrew White

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Optimization, Tesla S1070, Thesis

April 27, 2013 by hgpu

Reduce, Reuse, Recycle (R^3): a Design Methodology for Sparse Matrix Vector Multiplication on Reconfigurable Platforms

Kevin Townsend, Joseph Zambreno

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Tags: Computer science, CUDA, nVidia, Sparse matrix, Tesla M2090, Tesla S1070

April 23, 2013 by hgpu

Exploiting Multiple Levels of Parallelism and Online Refinement of Unstructured Meshes in Atmospheric Model Application

Claudio Schepke

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Tags: CUDA, Earth and Space Sciences, MPI, nVidia, nVidia GeForce GTX 480, Tesla S1070, Thesis, Weather prediction

February 12, 2013 by hgpu

Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography – network performance and application analysis

Pawel Kapusta, Michal Majchrowicz, Dominik Sankowski, Lidia Jackowska-Strumillo, Robert Banasiak

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Tags: ATI, ATI Radeon HD 5970, FEM, Finite element method, Heterogeneous systems, Image processing, Image reconstruction, Linear Algebra, Medicine, nVidia, Tesla C2070, Tesla S1070, Tomography

February 9, 2013 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Toward a GPU-Accelerated Immersed Boundary Method for Wind Forecasting Over Complex Terrain

Sparse Matrix Algorithms Using GPGPU

A Stochastic-based Optimized Schwarz Method for the Gravimetry Equations on GPU Clusters

The development and expansion of HOOMD-blue through six years of GPU proliferation

Performance Evaluation of the Ocean-Land-Atmosphere Model Using Graphics Processing Units

GPU Acceleration of Algebraic Multigrid for Low-Frequency Finite Element Methods

Efficient Execution of AMR Computations on GPU Systems

From Experiment to Design – Fault Characterization and Detection in Parallel Computer Systems Using Computational Accelerators

Modeling and Optimization of Parallel Matrix-based Computations on GPU

Reduce, Reuse, Recycle (R^3): a Design Methodology for Sparse Matrix Vector Multiplication on Reconfigurable Platforms

Exploiting Multiple Levels of Parallelism and Online Refinement of Unstructured Meshes in Atmospheric Model Application

Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography – network performance and application analysis

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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