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Python Workflows on HPC Systems

Dominik Strassel, Philipp Reusch, Janis Keuper

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Tags: Computer science, CUDA, Deep learning, GPU cluster, HPC, Machine learning, nVidia, PC cluster, Python

December 6, 2020 by hgpu

Scaling Deep Learning on GPU and Knights Landing clusters

Yang You, Aydin Buluc, James Demmel

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Tags: Cloud, Computer science, Deep learning, GPU cluster, Intel Xeon Phi, Machine learning, Neural networks, nVidia, Package, PC cluster, Tesla K80, Tesla M100

August 17, 2017 by hgpu

A Study of Scheduling a Neuro-imaging Application On a Heterogeneous CPU-GPU Cluster

Reza Nakhjavani

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Tags: Algorithms, CUDA, GPU cluster, Heterogeneous systems, Image processing, Medicine, nVidia, nVidia GeForce GTX Titan, PC cluster, Thesis

April 25, 2015 by hgpu

Porting Large HPC Applications to GPU Clusters: The Codes GENE and VERTEX

Tilman Dannert, Andreas Marek, Markus Rampp

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Tags: Computational Physics, CUDA, GPU cluster, Heterogeneous systems, nVidia, PC cluster, Physics, Plasma physics, Solar and Stellar Astrophysics, Tesla K20

October 8, 2013 by hgpu

Object support for OpenMP-style programming of GPU clusters in Java

Carolin Wolf, Georg Dotzler, Ronald Veldema, Michael Philippsen

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Tags: Computer science, CUDA, GPU cluster, Java, nVidia, PC cluster, Tesla M1060

July 16, 2013 by hgpu

Interference-driven resource management for GPU-based heterogeneous clusters

Rajat Phull, Cheng-Hong Li, Kunal Rao, Srihari Cadambi, Srimat Chakradhar

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, PC cluster, Performance, Tesla C2050

August 1, 2012 by hgpu

Implementation and Evaluation of Recurrence Equation Solvers on GPGPU systems using Rearrangement of Array Configurations

Akiyoshi Wakatani

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Tags: Algorithms, Computer science, CUDA, nVidia, PC cluster, Tesla C1060

July 29, 2012 by hgpu

A virtual memory based runtime to support multi-tenancy in clusters with GPUs

Michela Becchi, Kittisak Sajjapongse, Ian Graves, Adam Procter, Vignesh Ravi, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, PC cluster, Tesla C1060, Tesla C2050, Virtualization

July 27, 2012 by hgpu

A Generic Library for Stencil Computations

Mauro Bianco, Ugo Varetto

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Mathematical Software, MPI, nVidia, PC cluster, Performance, Tesla M2090

July 10, 2012 by hgpu

Astrophysical Particle Simulations on Heterogeneous CPU-GPU Systems

Naohito Nakasato, Go Ogiya, Yohei Miki, Masao Mori, Ken'ichi Nomoto

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Tags: Algorithms, Astrophysics, ATI, ATI Radeon HD 6530, ATI Radeon HD 7970, Computational Physics, Heterogeneous systems, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, OpenCL, Particle simulation, PC cluster, Tesla M2090

June 8, 2012 by hgpu

Designing Numerical Solvers for Next Generation High Performance Computing

Mark James Mawson

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Tags: Algorithms, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia Quadro FX 6000, PC cluster, Review, Thesis

January 9, 2012 by hgpu

rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

J. Duato, Pena, A.J. a, F. Silla, R. Mayo, E.S. Quintana-Orti

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Tags: Computer science, CUDA, Distributed computing, Energy-efficient computing, nVidia, Package, PC cluster, Tesla C1060

November 12, 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

Python Workflows on HPC Systems

A Study of Scheduling a Neuro-imaging Application On a Heterogeneous CPU-GPU Cluster

Porting Large HPC Applications to GPU Clusters: The Codes GENE and VERTEX

Object support for OpenMP-style programming of GPU clusters in Java

Interference-driven resource management for GPU-based heterogeneous clusters

Implementation and Evaluation of Recurrence Equation Solvers on GPGPU systems using Rearrangement of Array Configurations

A Generic Library for Stencil Computations

Astrophysical Particle Simulations on Heterogeneous CPU-GPU Systems

Designing Numerical Solvers for Next Generation High Performance Computing

rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

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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