high performance computing on graphics processing units: hgpu.org

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CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

Yongchao Liu, Adrianto Wirawan, Bertil Schmidt

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 690, Package, PTX, Sequence alignment, Smith-Waterman algorithm

April 10, 2013 by hgpu

OpenCL Fast Fourier Transform

Ruobing Li

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Tags: Computer science, FFT, nVidia, OpenCL, Package, Tesla M2070

April 9, 2013 by hgpu

A Performance Comparison of Different Graphics Processing Units Running Direct N-Body Simulations

R. Capuzzo-Dolcettaa, M. Spera

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Tags: Astrophysics, ATI, ATI Radeon HD 6970, ATI Radeon HD 7870, ATI Radeon HD 7970, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Package, Tesla C1060, Tesla C2050, Tesla K20

April 9, 2013 by hgpu

Atomic-free Irregular Computations on GPUs

Rupesh Nasre, Martin Burtscher, Keshav Pingali

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Package, Performance, Tesla K20

April 7, 2013 by hgpu

Adapting Particle Filter Algorithms to Many-Core Architectures

Mehdi Chitchian, Alexander S. van Amesfoort, Andrea Simonetto, Tamas Keviczky, Henk J. Sips

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Tags: Algorithms, ATI, ATI Radeon HD 6970, ATI Radeon HD 7970, Bayesian, Computer science, Computer vision, CUDA, Filtering, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Package, Particle filtering

April 4, 2013 by hgpu

Optimising Purely Functional GPU Programs

Trevor L. McDonell, Manuel M. T. Chakravarty, Gabriele Keller, Ben Lippmeier

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Tags: Computer science, CUDA, Haskell, nVidia, Package, Performance, Tesla T10

April 4, 2013 by hgpu

C Language Extensions for Hybrid CPU/GPU Programming with StarPU

Ludovic Courtes

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Tags: Computer science, Hybrid computing, Mathematical Software, MPI, OpenCL, Package

April 4, 2013 by hgpu

A journey from single-GPU to optimized multi-GPU SPH with CUDA

E. Rustico, G. Bilotta, G. Gallo, A. Herault, C. Del Negro, R. A. Dalrymple

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, Package

March 31, 2013 by hgpu

CMCpy: Genetic Code-Message Coevolution Models in Python

Peter J. Becich, Brian P. Stark, Harish S. Bhat, David H. Ardell

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 460, Package, Python

March 31, 2013 by hgpu

High Performance Computing using GPGPU’s

Omar Usman Khan

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Tags: Electrodynamics, FFT, nVidia, nVidia GeForce GTX 260, OpenCL, Package, Thesis

March 29, 2013 by hgpu

How to Benefit from AMD, Intel and Nvidia Accelerator Technologies in Scilab

Thibault Person, Francois Bodin

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Intel, Intel Phi, nVidia, OpenCL, Package, Performance, Portability, Tesla K20

March 24, 2013 by hgpu

GRay: a Massively Parallel GPU-Based Code for Ray Tracing in Relativistic Spacetimes

Chi-Kwan Chan, Dimitrios Psaltis, Feryal Ozel

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GT 650 M, Package, Raytracing, Tesla M2090

March 23, 2013 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Packages

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

OpenCL Fast Fourier Transform

A Performance Comparison of Different Graphics Processing Units Running Direct N-Body Simulations

Atomic-free Irregular Computations on GPUs

Adapting Particle Filter Algorithms to Many-Core Architectures

Optimising Purely Functional GPU Programs

C Language Extensions for Hybrid CPU/GPU Programming with StarPU

A journey from single-GPU to optimized multi-GPU SPH with CUDA

CMCpy: Genetic Code-Message Coevolution Models in Python

High Performance Computing using GPGPU’s

How to Benefit from AMD, Intel and Nvidia Accelerator Technologies in Scilab

GRay: a Massively Parallel GPU-Based Code for Ray Tracing in Relativistic Spacetimes

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