high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Performance evaluation of GPU memory hierarchy using the FFT

Jacobo Lobeiras, Margarita Amor, Ramon Doallo

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Tags: Benchmarking, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Signal processing

March 30, 2012 by hgpu

Auto-tuning a High-Level Language Targeted to GPU Codes

Scott Grauer-Gray, Lifan Xu, Robert Searles, Sudhee Ayalasomayajula, John Cavazos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 280, OpenCL, Optimization, Performance, Programming techniques, Stereo vision, Tesla C2050

March 28, 2012 by hgpu

Improving Performance of OpenCL on CPUs

Ralf Karrenberg, Sebastian Hack

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Tags: Code generation, Computer science, Data parallelism, OpenCL, Performance

March 27, 2012 by hgpu

OpenMPC: Extended OpenMP for Efficient Programming and Tuning on GPUs

Seyong Lee, Rudolf Eigenmann

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, OpenMP, Performance, Programming techniques

March 26, 2012 by hgpu

Impact of asynchronism on GPU accelerated parallel iterative computations

Sylvain Contassot-Vivier, Thomas Jost, Stephane Vialle

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Partial differential equations, PDEs, Performance

March 22, 2012 by hgpu

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Weizhi Xu, Zhiyong Liu, Dongrui Fan, Shuai Jiao, Xiaochun Ye, Fenglong Song, Chenggang Yan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 295, Performance, Sparse matrix

March 21, 2012 by hgpu

Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

Ahmed H. El Zein, Alistair P. Rendell

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla S2050

March 19, 2012 by hgpu

Towards user transparent parallel multimedia computing on GPU-clusters

Ben van Werkhoven, Jason Maassen, Frank J. Seinstra

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Tesla M1060

March 18, 2012 by hgpu

Enabling Fast, Noncontiguous GPU Data Movement in Hybrid MPI+GPU Environments

John Jenkins, Pavan Balaji, James Dinan, Nagiza F. Samatova, Rajeev Thakur

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Tags: Computer science, CUDA, MPI, nVidia, Performance, Programming Languages, Programming techniques, Tesla C2050

March 18, 2012 by hgpu

Usable assembly language for GPUs: a success story

Daniel J. Bernstein, Hsieh-Chung Chen, Chen-Mou Cheng, Tanja Lange, Ruben Niederhagen, Peter Schwabe, Bo-Yin Yang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, PTX, Security

March 18, 2012 by hgpu

Performance analysis and optimization of the OP2 framework on many-core architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly

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Source codes

Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, nVidia, OpenMP, Optimization, Package, Performance, Tesla C2050

March 15, 2012 by hgpu

Analyzing CUDA’s Compiler through the Visualization of Decoded GPU Binaries

Cedric Nugteren, Bart Mesman, Henk Corporaal

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Performance, Visualization

March 13, 2012 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance evaluation of GPU memory hierarchy using the FFT

Auto-tuning a High-Level Language Targeted to GPU Codes

Improving Performance of OpenCL on CPUs

OpenMPC: Extended OpenMP for Efficient Programming and Tuning on GPUs

Impact of asynchronism on GPU accelerated parallel iterative computations

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

Towards user transparent parallel multimedia computing on GPU-clusters

Enabling Fast, Noncontiguous GPU Data Movement in Hybrid MPI+GPU Environments

Usable assembly language for GPUs: a success story

Performance analysis and optimization of the OP2 framework on many-core architectures

Analyzing CUDA’s Compiler through the Visualization of Decoded GPU Binaries

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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