high performance computing on graphics processing units: hgpu.org

Packages

hgpu.org » Applications

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Brian Ichter, Edward Schmerling, Ali-akbar Agha-mohammadi, Marco Pavone

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package, Robotics

July 28, 2016 by hgpu

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Marius Erbert, Steffen Rechner, Matthias Muller-Hannemann

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, Package

July 26, 2016 by hgpu

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Glenn Volkema, Gaurav Khanna

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Tags: AMD Radeon R9 Fury X, ATI, Benchmarking, Computer science, nVidia, nVidia Tegra TX1, OpenCL, Package, Performance, Tesla K40

July 20, 2016 by hgpu

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

Ye Fang, Yun Ding, Wei P. Feinstein, David M. Koppelman, Juana Moreno, Mark Jarrell, J. Ramanujam, Michal Brylinski

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Tags: Algorithms, Chemistry, CUDA, Heterogeneous systems, Intel Xeon Phi, Medicine, Molecular dynamics, nVidia, nVidia GeForce GTX 980, OpenMP, Package

July 16, 2016 by hgpu

OpenFace: A general-purpose face recognition library with mobile applications

Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan

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Tags: Computer science, Computer vision, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, Python, Tesla K40

July 13, 2016 by hgpu

GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations

Matthew Spellings, Ryan L. Marson, Joshua A. Anderson, Sharon C. Glotzer

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Tags: Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia Quadro M 6000, Package, Particle simulation, Physics

July 13, 2016 by hgpu

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Materials Science, Molecular dynamics, MPI, nVidia, Package, performance portability, Tesla K20, Tesla K40

July 13, 2016 by hgpu

Fast Predictive Image Registration

Xiao Yang, Roland Kwitt, Marc Niethammer

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Tags: Bayesian, Computer science, Computer vision, CUDA, Deep learning, Image registration, nVidia, nVidia GeForce GTX Titan X, Package, Python

July 11, 2016 by hgpu

Torchnet: An Open-Source Platform for (Deep) Learning Research

Ronan Collobert, Laurens van der Maaten, Armand Joulin

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Tags: Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Torch

July 8, 2016 by hgpu

Using the pyMIC Offload Module in PyFR

Michael Klemm, Freddie Witherden, Peter Vincent

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Tags: Computer science, CUDA, Fluid dynamics, Intel Xeon Phi, Navier-Stokes equations, NSEs, OpenCL, OpenMP, Package, Python

July 8, 2016 by hgpu

TTC: A Tensor Transposition Compiler for Multiple Architectures

Paul Springer, Aravind Sankaran, Paolo Bientinesi

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Tags: BLAS, Compilers, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Mathematical Software, nVidia, nVidia GeForce 840 M, Package, Performance, Tesla K40

July 8, 2016 by hgpu

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL

HPCToolkit

Refining HPCToolkit for application performance analysis at exascale

Open Catalog of Best Practices for Modernization and Optimization

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

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high performance computing on graphics processing units: hgpu.org

Packages

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Scientific Computing Using Consumer Video-Gaming Hardware Devices

IODA: an Input/Output Deep Architecture for image labeling

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

OpenFace: A general-purpose face recognition library with mobile applications

GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Fast Predictive Image Registration

Torchnet: An Open-Source Platform for (Deep) Learning Research

Using the pyMIC Offload Module in PyFR

TTC: A Tensor Transposition Compiler for Multiple Architectures

Recent source codes

Intel® SHMEM: Device initiated shared memory based communication library

AFOCL

miniLB: Lattice Botlzmann miniapp w/SYCL

2domination

MFC: Exascale simulation of multiphase/physics fluid dynamics

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

HPCToolkit

Open Catalog of Best Practices for Modernization and Optimization

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

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