high performance computing on graphics processing units: hgpu.org

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Multi-dimensional characterization of electrostatic surface potential computation on graphics processors

Mayank Daga, Wu-chun Feng

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Tags: Algorithms, Biology, Biomolecules, Computational Complexity, CUDA, Electrostatics, Molecular modeling, nVidia, Optimization, Tesla C1060, Tesla C2050

May 12, 2012 by hgpu

Enhancing GPU Parallelism in Nature-Inspired Algorithms

Jose M. Cecilia, Andy Nisbet, Martyn Amos, Jose M. Garcia, Manuel Ujaldon

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Tags: Algorithms, Computational modelling, Computer science, CUDA, Data parallelism, nVidia, Optimization, P systems, Tesla C1060, Tesla C2050

May 12, 2012 by hgpu

CT to Cone-beam CT Deformable Registration With Simultaneous Intensity Correction

Xin Zhen, Xuejun Gu, Hao Yan, Linghong Zhou, Xun Jia, Steve B. Jiang

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Tags: Computed tomography, CT, CUDA, Image registration, Medical Physics, Medicine, nVidia, Physics, Tesla C1060, Tomography

May 3, 2012 by hgpu

A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes

A.H. Hassan, C.J. Fluke, D.G. Barnes

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Rendering, Tesla C1060, Tesla C2050, Visualization

May 3, 2012 by hgpu

Application of GPUs for the Calculation of Two Point Correlation Functions in Cosmology

Rafael Ponce, Miguel Cardenas-Montes, Juan Jose Rodriguez-Vazquez, Eusebio Sanchez, Ignacio Sevilla

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Tags: Algorithms, Astrophysics, Computational Physics, Cosmology, Cosmology and Extragalactic Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, MPI, nVidia, nVidia GeForce GTX 295, Package, Tesla C1060, Tesla C2050

May 1, 2012 by hgpu

Solving Stochastic Differential Equations Using General Purpose Graphics Processing Unit

Lev Alexandrovich Neiman

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Tags: Algorithms, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 480, Package, Tesla C1060, Tesla C2075

April 28, 2012 by hgpu

Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

Robert Hochberg

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GT 330 M, Tesla C1060

April 27, 2012 by hgpu

Comparison of Different Parallel Implementaions of the 2+1-Dimensional KPZ Model and the 3-Dimensional KMC Model

Jeffrey Kelling, Odor Geza, Mate Nagy Ferenc, Henrik Schulz, Karl-Heinz Heinig

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 6970, Computer science, CUDA, Materials Science, MPI, nVidia, OpenCL, Physics, Tesla C1060, Tesla C2050, Tesla C2070

April 25, 2012 by hgpu

An On-Demand Fast Parallel Pseudo Random Number Generator with Applications

Dip Sankar Banerjee, Aman Kumar Bahl, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, List ranking, Monte Carlo simulation, nVidia, Pseudo-random number generators, Tesla C1060

April 21, 2012 by hgpu

Implementation of Massive Artificial Neural Networks with CUDA

Domen Verber

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 595, Tesla C1060

April 17, 2012 by hgpu

A High-Performance Parallel FDTD Method Enhanced by Using SSE Instruction Set

Dau-Chyrh Chang, Lihong Zhang, Xiaoling Yang, Shao-Hsiang Yen, Wenhua Yu

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Tags: CUDA, Electrodynamics, FDTD, Finite difference, Finite-difference time-domain, nVidia, Tesla C1060

April 6, 2012 by hgpu

Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

Masato Yoshimi, Takuya Kuhara, Kaname Nishimoto, Mitsunori Miki, Tomoyuki Hiroyasu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8400 GS, nVidia GeForce GTX 280, Optimization, Self-organizing map, Tesla C1060, Visualization

April 5, 2012 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

Multi-dimensional characterization of electrostatic surface potential computation on graphics processors

Enhancing GPU Parallelism in Nature-Inspired Algorithms

CT to Cone-beam CT Deformable Registration With Simultaneous Intensity Correction

A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes

Application of GPUs for the Calculation of Two Point Correlation Functions in Cosmology

Solving Stochastic Differential Equations Using General Purpose Graphics Processing Unit

Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

Comparison of Different Parallel Implementaions of the 2+1-Dimensional KPZ Model and the 3-Dimensional KMC Model

Implementation of Massive Artificial Neural Networks with CUDA

A High-Performance Parallel FDTD Method Enhanced by Using SSE Instruction Set

Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

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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