high performance computing on graphics processing units: hgpu.org

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GPU-Accelerated Large-Eddy Simulation of Turbulent Channel Flows

Rey DeLeon, Inanc Senocak

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Tags: CUDA, Fluid dynamics, GPU cluster, Numerical simulation, nVidia

March 10, 2012 by hgpu

Scalable framework for mapping streaming applications onto multi-GPU systems

Huynh Phung Huynh, Andrei Hagiescu, Weng-Fai Wong, Rick Siow Mong Goh

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Tags: Algorithms, Code generation, Computer science, CUDA, GPU cluster, nVidia, Tesla C2070

March 8, 2012 by hgpu

Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units

Xiong QinGang, Li Bo, Xu Ji, Fang XiaoJian, Wang XiaoWei, Wang LiMin, He XianFeng, Ge Wei

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Tags: Algorithms, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, Numerical simulation, nVidia, Tesla C2050

February 22, 2012 by hgpu

Multiscale Hemodynamics Using GPU Clusters

Mauro Bisson, Massimo Bernaschi, Simone Melchionna, Sauro Succi, Efthimios Kaxiras

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Tags: Biology, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, Medicine, nVidia, Tesla C1060

February 17, 2012 by hgpu

Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA

Sabine McConnell, Robert Sturgeon, Gregory Henry, Andrew Mayne, Richard Hurley

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Tags: Algorithms, Computer science, CUDA, Data mining, GPU cluster, nVidia, nVidia GeForce GT 220, OpenCL, Self-organizing map, Tesla S1070

February 11, 2012 by hgpu

Implementing Molecular Dynamics on Hybrid High Performance Computers – Particle-Particle Particle-Mesh

W. Michael Brown, Axel Kohlmeyer, Steven J. Plimpton, Arnold N. Tharrington

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Source codes

Tags: Algorithms, Chemistry, CUDA, Electrostatics, GPU cluster, Heterogeneous systems, Molecular dynamics, nVidia, OpenCL, OpenMPI, Package, Physics, Tesla M2070

February 10, 2012 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 8, 2012 by hgpu

How well do STARLAB and NBODY compare? II: Hardware and accuracy

P. Anders, H. Baumgardt, E. Gaburov, S. Portegies Zwart

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Tags: Algorithms, Astrophysics, Cosmology and Extragalactic Astrophysics, Galaxy Astrophysics, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce 9800 GX2

January 30, 2012 by hgpu

Automatic Code Generation and Adaptive Grid Scheduling for GPU Cluster Computing

Xuyuan Jin

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Tags: Cluster computing, Code generation, Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 570

January 21, 2012 by hgpu

Combining approximate inference methods for efficient learning on large computer clusters

Zhenwen Dai, Jacquelyn A. Shelton, Jorg Bornschein, Abdul Saboor Sheikh, Jorg Lucke

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Tags: Algorithms, Computational Complexity, Computer science, GPU cluster, Machine learning, MPI, nVidia, nVidia GeForce GTX 480, OpenCL, Python

January 19, 2012 by hgpu

CPU/GPU computing for long-wave radiation physics on large GPU clusters

Fengshun Lu, Junqiang Song, Xiaoqun Cao, Xiaoqian Zhu

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Tags: CUDA, Earth and Space Sciences, Geoscience, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla M2050

January 17, 2012 by hgpu

Graphics Processor Clusters for High Speed Backpropagation

Daniel P. Campbell, Daniel A. Cook

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Tags: CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, Optimization, Signal processing, Tesla C2050

January 14, 2012 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

GPU-Accelerated Large-Eddy Simulation of Turbulent Channel Flows

Scalable framework for mapping streaming applications onto multi-GPU systems

Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units

Multiscale Hemodynamics Using GPU Clusters

Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA

Implementing Molecular Dynamics on Hybrid High Performance Computers – Particle-Particle Particle-Mesh

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

How well do STARLAB and NBODY compare? II: Hardware and accuracy

Automatic Code Generation and Adaptive Grid Scheduling for GPU Cluster Computing

Combining approximate inference methods for efficient learning on large computer clusters

CPU/GPU computing for long-wave radiation physics on large GPU clusters

Graphics Processor Clusters for High Speed Backpropagation

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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