high performance computing on graphics processing units: hgpu.org

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R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses

Benoit Liquet, Leonardo Bottolo, Gianluca Campanella, Sylvia Richardson, Marc Chadeau-Hyam

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Tags: Bayesian, Computer science, CUDA, CULA, nVidia, Package

February 1, 2016 by hgpu

RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures

Sergey Voronin, Per-Gunnar Martinsson

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Tags: Algorithms, CUDA, CULA, Mathematical Software, Mathematics, Matrix decomposition, Numerical Analysis, nVidia, Package, Tesla M2070

February 22, 2015 by hgpu

Bayesian neural networks for detecting epistasis in genetic association studies

Andrew L Beam, Alison Motsinger-Reif, Jon Doyle

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Tags: Bayesian, Biology, CUDA, CULA, Neural networks, nVidia, Package, Python

December 15, 2014 by hgpu

Fast Hamiltonian Monte Carlo Using GPU Computing

Andrew L. Beam, Sujit K. Ghosh, Jon Doyle

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Tags: Algorithms, Bayesian, CUDA, CULA, Monte Carlo simulation, nVidia, Package, Python, Statistics, Tesla K20

February 19, 2014 by hgpu

Application of the Characteristic Basis Function Method using CUDA

Juan Ignacio Perez, Eliseo Garcia, Jose A. de Frutos, Felipe Catedra

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Tags: Computer science, CUDA, CULA, nVidia, Tesla C2075

February 16, 2014 by hgpu

Performance Evaluation of R with Intel Xeon Phi Coprocessor

Yaakoub El-Khamra, Niall Gaffney, David Walling, Eric Wernert, Weijia Xu, Hui Zhang

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Tags: Computer science, CUDA, CULA, High-level Languages, Intel Phi, Intel Xeon Phi, nVidia, Package, Performance, Tesla K20

November 12, 2013 by hgpu

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs

Furong Huang, Niranjan U N, Mohammad Umar Hakeem, Prateek Verma, Animashree Anandkumar

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Tags: Computer science, CUDA, CULA, Machine learning, nVidia, nVidia Quadro K4000, nVidia Quadro K5000

September 9, 2013 by hgpu

Parallel Cosegmentation via Submodular Optimization on Anisotropic Diffusion

Dinesh Majeti, Aditya Prakash, S. Balasubramanian, PK Baruah

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Tags: Clustering, CUDA, CULA, Image processing, nVidia, Optimization, Tesla S2050

December 5, 2012 by hgpu

The impact of GPU/Multicore in Signal Processing: a quantitative approach

V.M Garcia, A.Gonzalez, C. Gonzalez, F.J. Martinez-Zaldivar, C.Ramiro, S. Roger, A.M. Vidal

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Tags: CUDA, CULA, nVidia, nVidia Quadro FX 5800, Signal processing, Tesla C2070

October 20, 2012 by hgpu

CULA: hybrid GPU accelerated linear algebra routines

John R. Humphrey, Daniel K. Price, Kyle E. Spagnoli, Aaron L. Paolini, Eric J. Kelmelis

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Tags: Computer science, CUDA, CULA, Linear Algebra, nVidia, Package, Tesla C1060

April 11, 2011 by hgpu

Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

Mark Van Heeswijk, Yoan Miche, Erkki Oja, Amaury Lendasse

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Tags: Computer science, CUDA, CULA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 295, Tesla C1060

March 20, 2011 by hgpu

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography

Bo Zhang, Xiang Yang, Fei Yang, Xin Yang, Chenghu Qin, Dong Han, Xibo Ma, Kai Liu, Jie Tian

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Tags: CUBLAS, CUDA, CULA, Image processing, Image reconstruction, Medicine, nVidia GeForce GT 240

November 8, 2010 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

R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses

RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures

Bayesian neural networks for detecting epistasis in genetic association studies

Fast Hamiltonian Monte Carlo Using GPU Computing

Application of the Characteristic Basis Function Method using CUDA

Performance Evaluation of R with Intel Xeon Phi Coprocessor

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs

Parallel Cosegmentation via Submodular Optimization on Anisotropic Diffusion

The impact of GPU/Multicore in Signal Processing: a quantitative approach

CULA: hybrid GPU accelerated linear algebra routines

Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography

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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