high performance computing on graphics processing units: hgpu.org

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Parallelizing compiler framework and API for power reduction and software productivity of real-time heterogeneous multicores

Akihiro Hayashi, Yasutaka Wada, Takeshi Watanabe, Takeshi Sekiguchi, Masayoshi Mase, Jun Shirako, Keiji Kimura, Hironori Kasahara

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Tags: Compilers, Computer science, Heterogeneous systems, Performance

August 25, 2011 by hgpu

A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction

Allen Leung, Nicolas Vasilache, Benoit Meister, Muthu Baskaran, David Wohlford, Cedric Bastoul, Richard Lethin

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Tags: Algorithms, Code generation, Compilers, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 285, Programming Languages

August 24, 2011 by hgpu

Implementing the PGI Accelerator model

Michael Wolfe

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization, Programming Languages, Tesla C1060, Tesla S1070

August 23, 2011 by hgpu

Compiling Python to a hybrid execution environment

Rahul Garg, Jose Nelson Amaral

Tags: ATI, ATI CAL, BLAS, Compilers, Computer science, OpenCL, Python

August 21, 2011 by hgpu

Sponge: portable stream programming on graphics engines

Amir H. Hormati, Mehrzad Samadi, Mark Woh, Trevor Mudge, Scott Mahlke

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Optimization, Performance, Programming Languages

August 21, 2011 by hgpu

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

Jing Guo, Jeyarajan Thiyagalingam, Sven-Bodo Scholz

Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, Performance, Programming Languages

August 21, 2011 by hgpu

Performance Comparison with OpenMP Parallelization for Multi-core Systems

Chao-Tung Yang, Tzu-Chieh Chang, Hsien-Yi Wang, William C.C. Chu, Chih-Hung Chang

Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, Fortran, nVidia, OpenCL, OpenMP, Performance

August 8, 2011 by hgpu

Performance Debugging of GPGPU Applications with the Divergence Map

Bruno Coutinho, Diogo Sampaio, Fernando M. Q. Pereira, Wagner Meira Jr.

Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Performance, PTX

August 1, 2011 by hgpu

A refactoring tool to extract GPU kernels

Kostadin Damevski, Madhan Muralimanohar

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Presentation

July 22, 2011 by hgpu

GLOpenCL: OpenCL support on hardware- and software-managed cache multicores

Konstantis Daloukas, Christos D. Antonopoulos, Nikolaos Bellas

Tags: Code generation, Compilers, Computer science, Heterogeneous systems, OpenCL

July 22, 2011 by hgpu

MATLAB Parallelization through Scalarization

Chun-Yu Shei, Adarsh Yoga, Madhav Ramesh, Arun Chauhan

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, Tesla C1060

July 11, 2011 by hgpu

Many-core parallel computing – Can compilers and tools do the heavy lifting?

W.-m.W. Hwu

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Tags: Compilers, Computer science, CUDA, Image processing, nVidia, Presentation, Review

July 10, 2011 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

Parallelizing compiler framework and API for power reduction and software productivity of real-time heterogeneous multicores

A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction

Implementing the PGI Accelerator model

Compiling Python to a hybrid execution environment

Sponge: portable stream programming on graphics engines

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

Performance Comparison with OpenMP Parallelization for Multi-core Systems

Performance Debugging of GPGPU Applications with the Divergence Map

A refactoring tool to extract GPU kernels

GLOpenCL: OpenCL support on hardware- and software-managed cache multicores

MATLAB Parallelization through Scalarization

Many-core parallel computing – Can compilers and tools do the heavy lifting?

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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