high performance computing on graphics processing units: hgpu.org

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General-purpose molecular dynamics simulations on GPU-based clusters

Christian R. Trott, Lars Winterfeld, Paul S. Crozier

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Tags: Computational Physics, Condensed matter, CUDA, GPU cluster, Materials Science, Molecular dynamics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Performance, Physics, Tesla C1060, Tesla S1070

March 8, 2011 by hgpu

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

Dana A. Jacobsen, Julien C. Thibault, Inanc Senocak

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Tags: CUDA, Fluid dynamics, GPU cluster, MPI, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 260, Tesla C1060, Tesla C870, Tesla S1070, Tesla S870

February 24, 2011 by hgpu

Flexible Hardware Mapping for Finite Element Simulations on Hybrid CPU / GPU Clusters

Aaron Becker, Isaac Dooley, Laxmikant V. Kale

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Tags: Computer science, CUDA, Finite element method, GPU cluster, nVidia, Tesla S1070

February 23, 2011 by hgpu

Probing biomolecular machines with graphics processors

James C. Phillips, John E. Stone

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Tags: Biology, Biomolecules, CUDA, GPU cluster, Molecular simulation, nVidia, OpenCL, Review, Tesla S1070

February 23, 2011 by hgpu

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms

Hartwig Anzt, Bjorn Rocker, Vincent Heuveline

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Tags: Computer science, Energy-efficient computing, Linear Algebra, Mixed precision, nVidia, Tesla C1060, Tesla S1070

February 22, 2011 by hgpu

Software-Based ECC for GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Error recovery, FFT, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

February 21, 2011 by hgpu

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

GPU Acceleration of Near-Minimal Logic Minimization

Ibrahim Savran, Jason D. Bakos

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla S1070

February 18, 2011 by hgpu

Using Graphics Processors to Accelerate Synthetic Aperture Sonar Imaging via Backpropagation

Daniel P. Campbell, Daniel A. Cook

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Tags: CUDA, Image reconstruction, nVidia, Physics, Tesla S1070

February 16, 2011 by hgpu

The fast multipole method on parallel clusters, multicore processors, and graphics processing units

Eric Darve, Cris Cecka, Toru Takahashi

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Tags: Algorithms, CUDA, Fast multipole method, Fluid dynamics, nVidia, Tesla C1060, Tesla S1070

February 11, 2011 by hgpu

Accelerating the solution of families of shifted linear systems with CUDA

Richard Galvez, Greg van Anders

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, Package, Physics, Tesla S1070, Tesla T10

February 11, 2011 by hgpu

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Benjamin G. Levine, John E. Stone, Axel Kohlmeyer

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Tags: Chemistry, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Tesla S1070

February 9, 2011 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

General-purpose molecular dynamics simulations on GPU-based clusters

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

Flexible Hardware Mapping for Finite Element Simulations on Hybrid CPU / GPU Clusters

Probing biomolecular machines with graphics processors

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms

Software-Based ECC for GPUs

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

GPU Acceleration of Near-Minimal Logic Minimization

Using Graphics Processors to Accelerate Synthetic Aperture Sonar Imaging via Backpropagation

The fast multipole method on parallel clusters, multicore processors, and graphics processing units

Accelerating the solution of families of shifted linear systems with CUDA

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

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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