high performance computing on graphics processing units: hgpu.org

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Data Transfer Matters for GPU Computing

Yusuke Fujii, Takuya Azumi, Nobuhiko Nishio, Shinpei Kato, Masato Edahiro

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Package, Performance

December 13, 2013 by hgpu

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

Mohammadhossein Afrasiabi

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 580, nVidia GeForce GTX 590, Rendering, Sensing, Software Engineering, Thesis

December 13, 2013 by hgpu

All-pairs Shortest Path Algorithm based on MPI+CUDA Distributed Parallel Programming Model

Wu Qingshuang, Tong Chunya, Wang Qiang, Cheng Xiangfu

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Tags: Algorithms, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GT 410 M, Path problems

December 13, 2013 by hgpu

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

Hector Ortega-Arranz, Yuri Torres, Diego R. Llanos, Arturo Gonzalez-Escribano

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, OpenMP

December 13, 2013 by hgpu

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr

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Tags: Bayesian, CUDA, Machine learning, Mathematics, nVidia, nVidia GeForce GTX Titan, Programming Languages, Statistics

December 13, 2013 by hgpu

GPU Based Dose Calculation

Dmitry Tsigelnitskiy

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Tags: CUDA, Intel Xeon Phi, Medicine, nVidia, Radiotherapy, Tesla C2050, Tesla K20, Thesis

December 12, 2013 by hgpu

Lessons learned from contrasting a BLAS kernel implementations

Andres More

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Tags: BLAS, Computer science, CUDA, nVidia, nVidia Quadro FX 770 M, Performance

December 12, 2013 by hgpu

Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Abdullah Gharaibeh, Elizeu Santos-Neto, Lauro Beltrao Costa, Matei Ripeanu

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Tags: Algorithms, Computer science, CUDA, Graph theory, Heterogeneous systems, Hybrid computing, nVidia, Package

December 12, 2013 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

Job Parallelism using Graphical Processing Unit Individual Multi-Processors and Localised Memory

D.P. Playne, K.A. Hawick

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Tags: Computer science, CUDA, Data parallelism, nVidia, Tesla M2075

December 11, 2013 by hgpu

A New Software Based GPU Framework

Evgeny Miretsky

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Thesis

December 11, 2013 by hgpu

A GPU-Accelerated Framework for Image Processing and Computer Vision

Ashwini.A.Patil, Pankaja.A.Shahapure

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Tags: Computer vision, CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 280, OpenCV

December 11, 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

Data Transfer Matters for GPU Computing

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

All-pairs Shortest Path Algorithm based on MPI+CUDA Distributed Parallel Programming Model

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

GPU Based Dose Calculation

Lessons learned from contrasting a BLAS kernel implementations

Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Multilayered Abstractions for Partial Differential Equations

Job Parallelism using Graphical Processing Unit Individual Multi-Processors and Localised Memory

A New Software Based GPU Framework

A GPU-Accelerated Framework for Image Processing and Computer Vision

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