high performance computing on graphics processing units: hgpu.org

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Using CUDA Architecture for the Computer Simulation of the Casting Solidification Process

G. Michalski, N. Sczygiol

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Tags: CUDA, Differential equations, nVidia, nVidia GeForce GTX 260, nVidia Tesla C1060, Thermodynamics

March 30, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation

Huayou Su, Chunyuan Zhang, Mei Wen, Nan Wu

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Tags: Algorithms, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 460, Tesla C2050, Video encoding

January 17, 2014 by hgpu

An octree-based proxy for collision detection in large-scale particle systems

An WenShan, Wang Bin, Paul Jean-Claude, Sun JiaGuang

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Tags: Collision detection, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 260, Particle simulation

January 10, 2014 by hgpu

High speed cipher cracking: the case of Keeloq on CUDA

Giovanni Agosta, Alessandro Barenghi, Gerardo Pelosi

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 470, Security

November 12, 2013 by hgpu

Expressed Sequence Tag Clustering using Commercial Gaming Hardware

Charl van Deventer, Willem A. Clarke, Scott Hazelhurst

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, Package, Thesis

September 15, 2013 by hgpu

The discrete dipole approximation code DDscat.C++: features, limitations and plans

V. Ya. Choliy

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Tags: Astrophysics, CUDA, Fortran, nVidia, nVidia GeForce GTX 260, Package

September 4, 2013 by hgpu

Image Processing with CUDA

Jia Jun Tse

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 260, Thesis

July 30, 2013 by hgpu

Automatic Generation of FFT Libraries for GPU Platforms

Christos Angelopoulos

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 260, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Thesis

July 22, 2013 by hgpu

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

George Zaki

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Tags: Algorithms, Code generation, Computational Engineering, CUDA, Data parallelism, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, Signal processing, Thesis

July 12, 2013 by hgpu

Accelerating Genetic Programming Using Graphics Processing Units

Tony Lewis

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 260, Thesis

June 10, 2013 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

Using CUDA Architecture for the Computer Simulation of the Casting Solidification Process

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation

An octree-based proxy for collision detection in large-scale particle systems

High speed cipher cracking: the case of Keeloq on CUDA

Expressed Sequence Tag Clustering using Commercial Gaming Hardware

The discrete dipole approximation code DDscat.C++: features, limitations and plans

Image Processing with CUDA

Automatic Generation of FFT Libraries for GPU Platforms

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

Accelerating Genetic Programming Using Graphics Processing Units

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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