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Reproducible and Accurate Matrix Multiplication for GPU Accelerators

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, OpenCL, Performance, Tesla K20

January 21, 2015 by hgpu

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Jonathan Wong, Ellen Kuhl, Eric Darve

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Sparse matrix

January 5, 2015 by hgpu

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Michael Driscoll

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Tags: 3D Graphics and Realism, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sparse matrix

January 5, 2015 by hgpu

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Jose M. Andion, Manuel Arenaz, Francois Bodin, Gabriel Rodriguez, Juan Tourino

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, Tesla S1070, Tesla S2050

December 14, 2014 by hgpu

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

Kazuya Matsumoto, Naohito Nakasato, Stanislav Sedukhin

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Tags: ATI, ATI Radeon HD 7970, BLAS, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan, OpenCL

October 29, 2014 by hgpu

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 580, OpenCL, Task scheduling

September 28, 2014 by hgpu

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

Ziming Zhong

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, Numerical simulation, nVidia, Tesla S2050, Thesis

August 27, 2014 by hgpu

A Study on GPU Computing and Accelerating Simulation of Sedimentary Rock Structure

Koushik Mandal

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2075, Thesis

July 23, 2014 by hgpu

Efficient implementation of computationally intensive algorithms on parallel computing platforms

Csaba Nemes

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 570, Tesla K20, Thesis

July 17, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

Evaluation of DGEMM Implementation on Intel Xeon Phi Coprocessor

Pawel Gepner, Victor Gamayunov, David L. Fraser, Eric Houdard, Ludovic Sauge, Damien Declat, Mathieu Dubois

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Tags: Algorithms, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Performance

June 28, 2014 by hgpu

Performance Improvement of Data Mining in Weka through GPU Acceleration

Tiago Augusto Engel, Andrea Schwertner Charao, Manuele Kirsch-Pinheiro, Luiz-Angelo Steffenel

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Tags: Comuter science, CUDA, Data mining, Java, Matrix multiplication, nVidia, nVidia Quadro K 2000, Package, Tesla K20, Tesla M2050

June 13, 2014 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

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

A Study on GPU Computing and Accelerating Simulation of Sedimentary Rock Structure

Efficient implementation of computationally intensive algorithms on parallel computing platforms

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Evaluation of DGEMM Implementation on Intel Xeon Phi Coprocessor

Performance Improvement of Data Mining in Weka through GPU Acceleration

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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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