high performance computing on graphics processing units: hgpu.org

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A self-organization based optical flow estimator with GPU implementation

Manish Shiralkar, Robert Schalkoff

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5600, nVidia Quadro NVS 140 M, Optical flow, Self-organizing map, Tesla T10, Thesis

July 22, 2011 by hgpu

Efficient Embarrassingly Parallel on Graphics Processor Unit

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, Pseudo-random number generators, Tesla T10

June 5, 2011 by hgpu

Application of the OpenCL API for Implementation of the NIPALS Algorithm for Principal Component Analysis of Large Data Sets

Joshua C. Bowden

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Tags: BLAS, Computer science, GPU cluster, MPI, nVidia, nVidia Quadro FX 580, OpenCL, Performance, Tesla C1060, Tesla S2050, Tesla T10

March 31, 2011 by hgpu

Implementing a GPU Programming Model on a non-GPU Accelerator Architecture

Stephen M. Kofsky, Daniel R. Johnson, John A. Stratton, Wen-Mei W. Hwu, Sanjay J. Patel, Steven S. Lumetta

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Tags: Computer science, CUDA, nVidia, Optimization, Programming techniques, Tesla T10

March 19, 2011 by hgpu

Patterns of Inefficient Performance Behavior in GPU Applications

Dominic Eschweiler, Daniel Becker, Felix Wolf

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Tesla T10

March 16, 2011 by hgpu

Fully accelerating quantum Monte Carlo simulations of real materials on GPU clusters

Kenneth P. Esler, Jeongnim Kim, David M. Ceperley

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Tags: CUDA, GPU cluster, Monte Carlo simulation, MPI, nVidia, Package, Physics, QMC, Tesla T10

February 18, 2011 by hgpu

Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

A GPU-based Flood Simulation Framework

Siddharth Shankar, Alfred J. Kalyanapu, Charles D. Hansen, Steven J. Burian

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce 8400 GS, Tesla T10

February 16, 2011 by hgpu

Accelerating the solution of families of shifted linear systems with CUDA

Richard Galvez, Greg van Anders

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, Package, Physics, Tesla S1070, Tesla T10

February 11, 2011 by hgpu

Uncontracted Rys Quadrature Implementation of up to G Functions on Graphical Processing Units

Andrey Asadchev, Veerendra Allada, Jacob Felder, Brett M. Bode, Mark S. Gordon and Theresa L. Windus

Tags: Chemistry, CUBLAS, CUDA, Molecular modeling, Molecular simulation, nVidia, nVidia GeForce GTX 275, Tesla T10

February 1, 2011 by hgpu

Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units

Jarad Niemi, Matthew Wheeler

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Tags: Bayesian, Biology, Chemistry, CUDA, nVidia, Stochastic simulation, Tesla T10

January 26, 2011 by hgpu

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Veerendra Allada, Troy Benjegerdes, Brett Bode

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Presentation, Tesla S1070, Tesla T10

December 27, 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

A self-organization based optical flow estimator with GPU implementation

Efficient Embarrassingly Parallel on Graphics Processor Unit

Application of the OpenCL API for Implementation of the NIPALS Algorithm for Principal Component Analysis of Large Data Sets

Implementing a GPU Programming Model on a non-GPU Accelerator Architecture

Patterns of Inefficient Performance Behavior in GPU Applications

Fully accelerating quantum Monte Carlo simulations of real materials on GPU clusters

Evaluating one-sided programming models for GPU cluster computations

A GPU-based Flood Simulation Framework

Accelerating the solution of families of shifted linear systems with CUDA

Uncontracted Rys Quadrature Implementation of up to G Functions on Graphical Processing Units

Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

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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