high performance computing on graphics processing units: hgpu.org

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Sparse systems solving on GPUs with GMRES

Raphael Couturier, Stephane Domas

Tags: Computer science, CUBLAS, CUDA, nVidia, Sparse linear iterative solvers, Sparse matrix

February 18, 2011 by hgpu

Accelerating Power Flow studies on Graphics Processing Unit

Jaideep Singh, Ipseeta Aruni

Tags: Computer science, CUDA, nVidia, Performance

February 18, 2011 by hgpu

Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

GPU Accelerated Particle System for Triangulated Surface Meshes

Brad Peterson, Manasi Datar, Mary Hall, Ross Whitaker

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Particle simulation, Triangular meshes

February 17, 2011 by hgpu

Medium-Grained Functions Mapping using Modern GPUs

Jiri Filipovic, Jan Fousek

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance

February 17, 2011 by hgpu

Simulations of Large Membrane Regions using GPU-enabled Computations – Preliminary Results

Narayan Ganesan, Michela Taufer, Sandeep Patel

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Tags: Chemistry, CUDA, Membrane computing, Molecular dynamics, nVidia, Performance

February 17, 2011 by hgpu

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Hatem Ltaief, Raymond Namyst, Jean Roman, Samuel Thibault, Stanimire Tomov

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro FX 5800, Task scheduling

February 17, 2011 by hgpu

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Pei-Hung Lin, Jagan Jayaraj, and Paul R. Woodward

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Tags: Cell processor, Code generation, Compilers, Computer science, CUDA, Fortran, High-level Languages, Memory model, nVidia, Tesla C1060

February 17, 2011 by hgpu

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

Devon Yablonski, Miriam Leeser, Dana Brooks

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Package, Sparse matrix

February 17, 2011 by hgpu

Takagi Factorization on GPU using CUDA

Gagandeep S. Sachdev, Vishay Vanjani and Mary W. Hall

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Tags: Computer science, CUDA, Factorization, Matrix decomposition, nVidia, nVidia GeForce GTX 260

February 17, 2011 by hgpu

Automatically Tuned Dense Linear Algebra for Multicore+GPU

Xing Fu, Xue Li, Gregory D. Peterson

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package

February 17, 2011 by hgpu

GpuC: Data parallel language extension to CUDA

Zeki Bozkus

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization

February 16, 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

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high performance computing on graphics processing units: hgpu.org

Sparse systems solving on GPUs with GMRES

Accelerating Power Flow studies on Graphics Processing Unit

Evaluating one-sided programming models for GPU cluster computations

GPU Accelerated Particle System for Triangulated Surface Meshes

Medium-Grained Functions Mapping using Modern GPUs

Simulations of Large Membrane Regions using GPU-enabled Computations – Preliminary Results

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

Takagi Factorization on GPU using CUDA

Automatically Tuned Dense Linear Algebra for Multicore+GPU

GpuC: Data parallel language extension to CUDA

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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