high performance computing on graphics processing units: hgpu.org

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Efficient Static and Dynamic Memory Management Techniques for Multi-GPU Systems

Max Grossman, Mauricio Araya-Polo

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K10

December 1, 2015 by hgpu

OpenCL Based Digital Image Projection Acceleration

Bryan Michael Badalamenti

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Tags: Algorithms, Computer science, FPGA, Intel Xeon Phi, OpenCL, Tesla K10, Tesla K40, Thesis

September 24, 2015 by hgpu

Design and optimization of DBSCAN Algorithm based on CUDA

Bingchen Wang, Chenglong Zhang, Lei Song, Lianhe Zhao, Yu Dou, Zihao Yu

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla K10

June 10, 2015 by hgpu

Flexible Software Profiling of GPU Architectures

Mark Stephenson, Siva Kumar Sastry Hari, Yunsup Lee, Eiman Ebrahimi, Daniel R. Johnson, David Nellans, Mike O'Connor, Stephen W. Keckler

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Tags: Computer science, CUDA, Design space exploration, nVidia, PTX, Tesla K10, Tesla K20, Tesla K40

April 25, 2015 by hgpu

Sparse Matrix-Vector Multiplication on GPU

Arash Ashari

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan, Sparse matrix, Tesla K10, Thesis

February 9, 2015 by hgpu

Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Jose Ritomar Carneiro Torquato, Esteban Walter Gonzalez Clua

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance, Tesla K10, Tesla K20

December 14, 2014 by hgpu

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K10, Tesla K20

October 29, 2014 by hgpu

Integrated Modelling of Hydrodynamic Processes, Faecal Indicator Organisms and Related Parameters with Improved Accuracy using Parallel (GPU) Computing

Brian Adjetey Boye

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Tags: CUDA, Earth and Space Sciences, nVidia, nVidia GeForce GTX 590, Tesla K10, Tesla K20, Thesis

June 2, 2014 by hgpu

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 480, Package, Python, Sequence alignment, Tesla K10, Tesla K20

May 25, 2014 by hgpu

An efficient GPU acceptance-rejection algorithm for the selection of the next reaction to occur for Stochastic Simulation Algorithms

Neri Mickael, Denis Mestivier

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Tags: Algorithms, Biology, Computer science, CUDA, nVidia, Optimization, Stochastic simulation, Tesla K10, Tesla K20, Tesla M2075, Tesla M2090

April 4, 2014 by hgpu

A comparison between parallelization approaches in molecular dynamics simulations on GPUs

Lorenzo Rovigatti, Petr Sulc, Istvan Z. Reguly, Flavio Romano

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Tags: Computational Physics, CUDA, Molecular dynamics, nVidia, Package, Physics, Soft Condensed Matter, Tesla C2070, Tesla K10, Tesla K20

January 23, 2014 by hgpu

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Michal Januszewski, Marcin Kostur

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Tags: Code generation, Computational Physics, CUDA, Fluid dynamics, Fluid simulation, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 285, OpenCL, Package, Physics, Python, Tesla C1060, Tesla C2050, Tesla K10, Tesla K20

November 12, 2013 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Efficient Static and Dynamic Memory Management Techniques for Multi-GPU Systems

OpenCL Based Digital Image Projection Acceleration

Design and optimization of DBSCAN Algorithm based on CUDA

Flexible Software Profiling of GPU Architectures

Sparse Matrix-Vector Multiplication on GPU

Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Integrated Modelling of Hydrodynamic Processes, Faecal Indicator Organisms and Related Parameters with Improved Accuracy using Parallel (GPU) Computing

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

An efficient GPU acceptance-rejection algorithm for the selection of the next reaction to occur for Stochastic Simulation Algorithms

A comparison between parallelization approaches in molecular dynamics simulations on GPUs

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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