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Data-aware scheduling of legacy kernels on heterogeneous platforms with distributed memory

Michela Becchi, Surendra Byna, Srihari Cadambi, Srimat Chakradhar

Tags: Computer science, CUDA, Distributed data structures, Heterogeneous systems, nVidia, Performance, Programming Languages, Sparse matrix, Tesla C870

May 10, 2011 by hgpu

Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems

Gregory F. Diamos, Andrew R. Kerr, Sudhakar Yalamanchili, Nathan Clark

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Source codes

Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Programming Languages, PTX

March 9, 2011 by hgpu

A Tuning Framework for Software-Managed Memory Hierarchies

Manman Ren, Ji Young Park, Mike Houston, Alex Aiken, William J. Dally

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Tags: Algorithms, Cell processor, Compilers, Computer science, Optimization, Performance, Playstation, Programming Languages

February 26, 2011 by hgpu

Many-core GPU computing with NVIDIA CUDA

Mark Harris

Tags: Computer science, CUDA, High-level Languages, nVidia, Programming Languages

February 24, 2011 by hgpu

Mobile 3D Graphics

Alessio Malizia

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Tags: 3D Graphics and Realism, Book, Computer science, Programming Languages, Tutorial

February 8, 2011 by hgpu

Introduction to the Report “Interlanguages and Synchronic Models of Computation.”

Alexander Victor Berka

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Tags: Computer science, High-level Languages, Programming Languages

January 19, 2011 by hgpu

Space and the Synchronic A-Ram

Alex V Berka

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Tags: Computer science, High-level Languages, Programming Languages

January 19, 2011 by hgpu

Interlanguages and synchronic models of computation

Alexander Victor Berka

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Tags: Computer science, FPGA, High-level Languages, Programming Languages

January 19, 2011 by hgpu

RapidMind: Portability across Architectures and its Limitations

Iris Christadler, Volker Weinberg

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Tags: Cell processor, Computer science, CUDA, nVidia, Performance, Programming Languages, Tesla C1060, Tesla S1070

November 12, 2010 by hgpu

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

E. Z. Zhang, Y. Jiang, G. Ziyu, X. Shen

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

November 5, 2010 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

Data-aware scheduling of legacy kernels on heterogeneous platforms with distributed memory

Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems

A Tuning Framework for Software-Managed Memory Hierarchies

Many-core GPU computing with NVIDIA CUDA

Mobile 3D Graphics

Introduction to the Report “Interlanguages and Synchronic Models of Computation.”

Space and the Synchronic A-Ram

Interlanguages and synchronic models of computation

RapidMind: Portability across Architectures and its Limitations

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

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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