high performance computing on graphics processing units: hgpu.org

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An efficient parallel algorithm for accelerating computational protein design

Yichao Zhou, Wei Xu, Bruce R. Donald, Jianyang Zeng

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Java, nVidia, Package, Tesla K20

June 23, 2014 by hgpu

MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

Yang You, Shuaiwen Leon Song, Haohuan Fu, Andres Marquez, Guangwen Yang, Kevin Barker, Kirk W. Cameron, Maryam Mehri Dehnavi, Amanda Peters Randles

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Tags: Computer science, CUDA, Intel Xeon Phi, Machine learning, nVidia, Tesla K20, Vector Machine

June 20, 2014 by hgpu

Computing on Knights and Kepler Architectures

G. Bortolotti, M. Caberletti, G. Crimi, A. Ferraro, F. Giacomini, M. Manzali, G. Maron, M. Pivanti, D. Salomoni, S. F. Schifano, R. Tripiccione, M. Zanella

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Tags: CUDA, Fluid dynamics, Intel Xeon Phi, Lattice Boltzmann model, nVidia, Tesla K20

June 18, 2014 by hgpu

Expansion Techniques for Collisionless Stellar Dynamical Simulations

Yohai Meiron, Baile Li, Kelly Holley-Bockelmann, Rainer Spurzem

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Laplace and Poisson equation, nVidia, Poisson equation, Tesla K20

June 18, 2014 by hgpu

A Portable OpenCL Lattice Boltzmann Code for Multi- and Many-core Processor Architectures

Enrico Calore, Sebastiano Fabio Schifano, Raffaele Tripiccione

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, Lattice Boltzmann model, nVidia, OpenCL, Tesla K20

June 17, 2014 by hgpu

Synergia CUDA: GPU-accelerated accelerator modeling package

Q. Lu, J. Amundson

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Tags: CUDA, FFT, GPU cluster, MPI, nVidia, Package, Particle-in-cell methods, Physics, Tesla C1070, Tesla K20

June 17, 2014 by hgpu

Grover: Looking for Performance Improvement by Disabling Local Memory Usage in OpenCL Kernels

Jianbin Fang, Henk Sips, Pekka Jaaskelainen, Ana Lucia Varbanescu

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Tags: ATI, ATI Radeon HD 7970, Intel Xeon Phi, Local memory, nVidia, OpenCL, Reverse Engineering, Tesla C2050, Tesla K20

June 16, 2014 by jfang

Unified Development for Mixed Multi-GPU and Multi-Coprocessor Environments using a Lightweight Runtime Environment

Azzam Haidar, Chongxiao Cao, Asim YarKhan, Piotr Luszczek, Stanimire Tomov, Khairul Kabir, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, nVidia, Tesla K20, Tesla M2090

June 14, 2014 by hgpu

Performance Improvement of Data Mining in Weka through GPU Acceleration

Tiago Augusto Engel, Andrea Schwertner Charao, Manuele Kirsch-Pinheiro, Luiz-Angelo Steffenel

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Tags: Comuter science, CUDA, Data mining, Java, Matrix multiplication, nVidia, nVidia Quadro K 2000, Package, Tesla K20, Tesla M2050

June 13, 2014 by hgpu

Multi-threaded Kernel Offloading to GPGPU Using Hyper-Q on Kepler Architecture

Florian Wende, Thomas Steinke, Frank Cordes

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Tesla M2090

June 12, 2014 by hgpu

Enabling High Performance Computing in Cloud Infrastructure using rCUDA

Yousuf Sait I, Vijayalakshmi R

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Tags: Cloud, Computer science, CUDA, nVidia, Tesla C2075, Tesla K20, Virtualization

June 11, 2014 by hgpu

An Improved Parallel Algorithm using GPU for Siting Observers on Terrain

Guilherme C. Pena, Marcus V. A. Andrade, Salles V. G. Magalhaes, W. R. Franklin, Chaulio R. Ferreira

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Tags: Algorithms, Computer science, CUDA, nVidia, Search strategies, Tesla K20

June 6, 2014 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

high performance computing on graphics processing units: hgpu.org

An efficient parallel algorithm for accelerating computational protein design

MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

Computing on Knights and Kepler Architectures

Expansion Techniques for Collisionless Stellar Dynamical Simulations

A Portable OpenCL Lattice Boltzmann Code for Multi- and Many-core Processor Architectures

Synergia CUDA: GPU-accelerated accelerator modeling package

Grover: Looking for Performance Improvement by Disabling Local Memory Usage in OpenCL Kernels

Unified Development for Mixed Multi-GPU and Multi-Coprocessor Environments using a Lightweight Runtime Environment

Performance Improvement of Data Mining in Weka through GPU Acceleration

Multi-threaded Kernel Offloading to GPGPU Using Hyper-Q on Kepler Architecture

An Improved Parallel Algorithm using GPU for Siting Observers on Terrain

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