high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla C1060

Using many-core hardware to correlate radio astronomy signals

Rob V. van Nieuwpoort, John W. Romein

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Tags: Algorithms, Astrophysics, ATI, ATI CAL, ATI Radeon HD 4870, ATI Stream, Cell processor, CUDA, nVidia, Performance, Physics, Signal processing, Tesla C1060

September 19, 2011 by hgpu

Data-intensive document clustering on graphics processing unit (GPU) clusters

Yongpeng Zhang, Frank Mueller, Xiaohui Cui, Thomas Potok

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Tags: Algorithms, Clustering, Computer science, CUDA, Data mining, GPU cluster, nVidia, nVidia GeForce GTX 280, Tesla C1060

September 13, 2011 by hgpu

Parallel implementation of conjugate gradient method on graphics processors

Marcin Wozniak, Tomasz Olas, Roman Wyrzykowski

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Sparse matrix, Tesla C1060

September 7, 2011 by hgpu

A CUDA Based Implementation of an Image Authentication Algorithm

Caiwei Lin, Lei Zhao, Jiwen Yang

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Tags: Algorithms, Computer science, CUDA, nVidia, Security, Tesla C1060

September 5, 2011 by hgpu

Computing room acoustics with CUDA – 3D FDTD schemes with boundary losses and viscosity

Craig J. Webb, Stefan Bilbao

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Tags: Acoustics, CUDA, FDTD, Finite difference, Finite-difference time-domain, Numerical simulation, nVidia, Physics, Tesla C1060, Tesla C2050

September 4, 2011 by hgpu

Multicore bundle adjustment

Changchang Wu, Sameer Agarwal, Brian Curless, Steven M. Seitz

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Source codes

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, Image reconstruction, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 295, Package, Tesla C1060

August 31, 2011 by hgpu

FastMag: Fast micromagnetic simulator for complex magnetic structures

R. Chang, S. Li, M. V. Lubarda, B. Livshitz, V. Lomakin

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Tags: CUDA, Electromagnetics simulation, nVidia, nVidia GeForce GTX 480, ODEs, Ordinary differential equations, Physics, Tesla C1060, Tesla C2070

August 31, 2011 by hgpu

A structured parallel periodic arnoldi shooting algorithm for RF-PSS analysis based on GPU platforms

Xue-Xin Liu, Hao Yu, Jacob Relles, Sheldon X.-D. Tan

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Tags: Algorithms, CAD, Computer science, CUDA, Numerical Analysis, nVidia, Performance, Tesla C1060

August 27, 2011 by hgpu

Considering GPGPU for HPC Centers: Is It Worth the Effort?

Hans Hacker, Carsten Trinitis, Josef Weidendorfer, Matthias Brehm

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

August 25, 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

GRace: a low-overhead mechanism for detecting data races in GPU programs

Mai Zheng, Vignesh T. Ravi, Feng Qin, Gagan Agrawal

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Tags: Algorithms, Computer science, CUDA, nVidia, Programming techniques, Tesla C1060

August 20, 2011 by hgpu

Benchmarking and modelling of POWER7, Westmere, BG/P, and GPUs: an industry case study

J. A. Herdman, W. P. Gaudin, D. Turland, S. D. Hammond

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, MPI, nVidia, nVidia GeForce GTX 285, OpenCL, Performance, Tesla C1060

August 19, 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

Using many-core hardware to correlate radio astronomy signals

Data-intensive document clustering on graphics processing unit (GPU) clusters

Parallel implementation of conjugate gradient method on graphics processors

A CUDA Based Implementation of an Image Authentication Algorithm

Computing room acoustics with CUDA – 3D FDTD schemes with boundary losses and viscosity

Multicore bundle adjustment

FastMag: Fast micromagnetic simulator for complex magnetic structures

A structured parallel periodic arnoldi shooting algorithm for RF-PSS analysis based on GPU platforms

Considering GPGPU for HPC Centers: Is It Worth the Effort?

Implementing the PGI Accelerator model

GRace: a low-overhead mechanism for detecting data races in GPU programs

Benchmarking and modelling of POWER7, Westmere, BG/P, and GPUs: an industry case study

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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