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Efficient Implementation of MrBayes on multi-GPU

Jie Bao, Hongju Xia, Jianfu Zhou, Xiaoguang Liu, Gang Wang

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Tags: Bayesian, Biology, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, Package, Task scheduling

January 28, 2013 by hgpu

A Multi-GPU Programming Library for Real-Time Applications

Sebastian Schaetz, Martin Uecker

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Tags: Algorithms, CUDA, GPU cluster, Image reconstruction, Magnetic resonance imaging, MPI, MRI, nVidia, nVidia GeForce GTX 580, Performance, Signal processing

January 8, 2013 by hgpu

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

George Teodoro, Tony Pan, Tahsin M. Kurc, Jun Kong, Lee A. D. Cooper, Norbert Podhorszki, Scott Klasky, Joel H. Saltz

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Tags: CUDA, GPU cluster, Image processing, Microscopy, nVidia, Prefetch, Tesla M2090

January 6, 2013 by hgpu

Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

Qing Hou, Min Li, Yulu Zhou, Jiechao Cui, Zhenguo Cui, Jun Wang

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Tags: Computational Physics, CUDA, GPU cluster, Materials Science, Molecular dynamics, nVidia, Physics, Tesla C2050

January 2, 2013 by hgpu

Coulomb, Landau and Maximally Abelian Gauge Fixing in Lattice QCD with Multi-GPUs

Mario Schrock, Hannes Vogt

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Tags: CUDA, GPU cluster, High Energy Physics - Lattice, Mixed precision, Monte Carlo simulation, MPI, nVidia, Physics, QCD, Tesla C2070

December 23, 2012 by hgpu

IceCubes GPGPU’s cluster for extensive MC production

Martin Merck, Dmitry Chirkin, Juan Carlos Diaz Velez, Heath Skarlupka

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Tags: Computational Physics, CUDA, GPU cluster, Monte Carlo simulation, Nuclear physics, nVidia, Particle physics and field theory, Physics, Raytracing, Tesla M2070

December 20, 2012 by hgpu

A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters

Shusen Liu, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, Valerio Pascucci

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Tags: Computer science, CUDA, GPU cluster, nVidia, Rendering, Tesla M2070

December 14, 2012 by hgpu

Multi-level Parallelism for Incompressible Flow Computations on GPU Clusters

Dana A. Jacobsen, Inanc Senocak

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Tags: Computer science, CUDA, Fluid dynamics, GPU cluster, MPI, nVidia, Tesla C1060, Tesla S1070

December 12, 2012 by hgpu

ValuePack: value-based scheduling framework for CPU-GPU clusters

Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Tesla C2050

November 21, 2012 by hgpu

Device specialization in heterogeneous multi-GPU environments

Gabriele Cocco, Antonio Cisternino

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Tags: AMD Fusion, ATI, ATI IL, ATI Radeon HD 5870, Computer science, GPU cluster, Heterogeneous systems, Task scheduling

November 15, 2012 by hgpu

Efficient implementation of data flow graphs on multi-gpu clusters

Vincent Boulos, Sylvain Huet, Vincent Fristot, Luc Salvo, Dominique Houzet

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Tags: Algorithms, CUDA, GPU cluster, Image processing, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 460 M, Signal processing

November 7, 2012 by hgpu

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Vlad Slavici, Raghu Varier, Gene Cooperman, Robert J. Harrison

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Tags: Chemistry, Computational chemistry, Computer science, CUBLAS, CUDA, GPU cluster, nVidia, Tesla M2090

November 6, 2012 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

Efficient Implementation of MrBayes on multi-GPU

A Multi-GPU Programming Library for Real-Time Applications

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

Coulomb, Landau and Maximally Abelian Gauge Fixing in Lattice QCD with Multi-GPUs

IceCubes GPGPU’s cluster for extensive MC production

A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters

Multi-level Parallelism for Incompressible Flow Computations on GPU Clusters

Device specialization in heterogeneous multi-GPU environments

Efficient implementation of data flow graphs on multi-gpu clusters

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

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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