high performance computing on graphics processing units: hgpu.org

Programming

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

Julian Romera

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenMPI, Performance, Tesla K80, Tesla M2090, Thesis

April 7, 2017 by hgpu

LookNN: Neural Network with No Multiplication

Mohammad Samragh Razlighi, Mohsen Imani, Farinaz Koushanfar, Tajana Rosing

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, Neural networks, OpenCL

March 28, 2017 by hgpu

APUNet: Revitalizing GPU as Packet Processing Accelerator

Younghwan Go, Muhammad Jamshed, YoungGyoun Moon, Changho Hwang, KyoungSoo Park

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Tags: Algorithms, AMD, APU, ATI, Computer science, nVidia, nVidia GeForce GTX 980, OpenCL, Performance

March 28, 2017 by hgpu

GPU accelerated population annealing algorithm

Lev Yu. Barash, Martin Weigel, Michal Borovsky, Wolfhard Janke, Lev N. Shchur

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Tags: Algorithms, CUDA, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla K80

March 14, 2017 by hgpu

Compiling Parallel Functional Code with Data Parallel Idealised Algol

Robert Atkey, Michel Steuwer, Sam Lindley, Christophe Dubach

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, performance portability

March 14, 2017 by hgpu

Model-independent partial wave analysis using a massively-parallel fitting framework

Liang Sun, Rafael Aoude, Alberto Correa Dos Reis, Michael David Sokoloff

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Tags: Algorithms, CUDA, High Energy Physics - Experiment, nVidia, OpenMP, Package, Physics, Tesla K40

March 14, 2017 by hgpu

Improving the Neural GPU Architecture for Algorithm Learning

Karlis Freivalds, Renars Liepins

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, Memory model, nVidia, Package, Python, TensorFlow, Tesla K40

March 5, 2017 by hgpu

An Efficient Multiway Mergesort for GPU Architectures

Henri Casanova, John Iacono, Ben Karsin, Nodari Sitchinava, Volker Weichert

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 770, nVidia Quadro M 4000, Sorting, Tesla K40

February 28, 2017 by hgpu

Is GPGPU CCL worth it? A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images

Alex Sundstrom, Victor Ahdel

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Tags: Algorithms, AMD Radeon R9 280X, ATI, Computer science, Image processing, nVidia, nVidia GeForce GTX 960, OpenCL, Performance, Thesis

February 26, 2017 by hgpu

Large-Scale Stochastic Learning using GPUs

Thomas Parnell, Celestine Dunner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro M 4000

February 26, 2017 by hgpu

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

Hung T. Nguyen, Alberto Cano, Tam Vu, Thang N. Dinh

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Tags: Algorithms, Computer science, CUDA, Network communications, nVidia, nVidia GeForce GTX Titan X

February 22, 2017 by hgpu

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Ramin Javadi, Saleh Ashkboos

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M

February 18, 2017 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

Programming

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

LookNN: Neural Network with No Multiplication

APUNet: Revitalizing GPU as Packet Processing Accelerator

GPU accelerated population annealing algorithm

Compiling Parallel Functional Code with Data Parallel Idealised Algol

Model-independent partial wave analysis using a massively-parallel fitting framework

Improving the Neural GPU Architecture for Algorithm Learning

An Efficient Multiway Mergesort for GPU Architectures

Is GPGPU CCL worth it? A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images

Large-Scale Stochastic Learning using GPUs

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

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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