high performance computing on graphics processing units: hgpu.org

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cellGPU: massively parallel simulations of dynamic vertex models

Daniel M. Sussman

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Tags: Biological Physics, CUDA, nVidia, Package, Physics, Tesla K40

February 14, 2017 by hgpu

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis

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Tags: Biological Physics, Computational Physics, CUDA, MPI, nVidia, Package, Physics, Tesla K20

November 22, 2016 by hgpu

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Carsten Kutzner, Szilard Pall, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmuller

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Tags: Benchmarking, Biological Physics, Biomolecules, Computational Physics, Computer science, Molecular dynamics, Molecular simulation, MPI, nVidia, Package, Performance, Tesla K20, Tesla K40

July 6, 2015 by hgpu

Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

Tomas Ekeberg, Stefan Engblom, Jing Liu

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Tags: Algorithms, Biological Physics, Biology, Biomolecules, Cluster computing, Computational Complexity, CUDA, GPU cluster, Machine learning, nVidia, nVidia GeForce GTX 680, OpenMPI

September 16, 2014 by hgpu

Use of Multiple GPUs to Speedup the Execution of a Three-Dimensional Computational Model of the Innate Immune System

M. P. Xavier, T. M. do Nascimento, R. W. dos Santos, M. Lobosco

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Tags: Biological Physics, CUDA, Medicine, nVidia, Tesla M2050

April 18, 2014 by hgpu

The effects of nutrient chemotaxis on bacterial aggregation patterns with non-linear degenerate cross diffusion

J. Francisco Leyva, Carlos Malaga, Ramon G. Plaza

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Tags: Biological Physics, Biology, CUDA, Numerical simulation, nVidia, Tesla C2070

January 23, 2013 by hgpu

Acceleration of multivariate analysis techniques in TMVA using GPUs

A. Hoecker, H. McKendrick, J. Therhaag, A. Washbrook

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Tags: Biological Physics, CUDA, Neural networks, nVidia, Physics, Tesla C1060, Tesla C2050

December 16, 2012 by hgpu

GPU-Based Asynchronous Global Optimization with Particle Swarm

M. P. Wachowiak, A. E. Lambe Foster

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Tags: Biological Physics, Computational Physics, CUDA, nVidia, Particle swarm optimization, Physics, Tesla S1070

October 25, 2012 by hgpu

OpenCL/OpenGL aproach for studying active Brownian motion

Michal A. Zabicki

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Tags: Biological Physics, Computational Physics, nVidia, nVidia GeForce 9400 M, nVidia GeForce 9800 GT, OpenCL, OpenGL, Physics

April 14, 2011 by hgpu

Generation of Random Numbers on Graphics Processors: Forced Indentation In Silico of the Bacteriophage HK97

A. Zhmurov, K. Rybnikov, Y. Kholodov, V. Barsegov

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Tags: Biological Physics, Chemical Physics, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Physics, Pseudo-random number generators

January 2, 2011 by hgpu

Real-time digital holographic microscopy observable in multi-view and multi-resolution

Tomoyoshi Shimobaba, Nobuyuki Masuda, Yasuyuki Ichihashi, Tomoyoshi Ito

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Tags: Biological Physics, Computational Physics, Computer holography, CUDA, nVidia, nVidia GeForce GTX 295, Optics, Package, Physics

November 12, 2010 by hgpu

Langevin dynamics simulations of biomolecules on graphics processors

A. Zhmurov, R. I. Dima, Y. Kholodov, V. Barsegov

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Tags: Biological Physics, Biomolecules, Condensed matter, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 295, Physics, Soft Condensed Matter

November 12, 2010 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

cellGPU: massively parallel simulations of dynamic vertex models

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

Use of Multiple GPUs to Speedup the Execution of a Three-Dimensional Computational Model of the Innate Immune System

The effects of nutrient chemotaxis on bacterial aggregation patterns with non-linear degenerate cross diffusion

Acceleration of multivariate analysis techniques in TMVA using GPUs

GPU-Based Asynchronous Global Optimization with Particle Swarm

OpenCL/OpenGL aproach for studying active Brownian motion

Generation of Random Numbers on Graphics Processors: Forced Indentation In Silico of the Bacteriophage HK97

Real-time digital holographic microscopy observable in multi-view and multi-resolution

Langevin dynamics simulations of biomolecules on graphics processors

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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