high performance computing on graphics processing units: hgpu.org

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A middleware for efficient stream processing in CUDA

Shinta Nakagawa, Fumihiko Ino, Kenichi Hagihara

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Optimization

November 20, 2010 by hgpu

Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU

Wenfeng Shen, Daming Wei, Weimin Xu, Xin Zhu, Shizhong Yuan

Tags: CUDA, Electrocardiograms simulation, Medicine, nVidia, nVidia GeForce 8800 GT, OpenMP, Optimization

November 8, 2010 by hgpu

Automatic fitting of spiking neuron models to electrophysiological recordings

Cyrille Rossant, Dan F. Goodman, Jonathan Platkiewicz, Romain Brette

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Tags: Adaptive threshold, Biology, CUDA, Distributed computing, Neuroscience, nVidia, nVidia GeForce GTX 295, Optimization, Package

November 6, 2010 by hgpu

On Dynamic Load Balancing on Graphics Processors

Daniel Cederman, Philippas Tsigas

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 9600 GT, Optimization, Performance

November 5, 2010 by hgpu

Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA

Ogier Maitre, Laurent A. Baumes, Nicolas Lachiche, Avelino Corma, Pierre Collet

Tags: Computer science, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 260, Optimization

November 5, 2010 by hgpu

Program optimization space pruning for a multithreaded gpu

Shane Ryoo, Christopher I. Rodrigues, Sam S. Stone, Sara S. Baghsorkhi, Sain Z. Ueng, John A. Stratton, Wen mei

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Performance, Programming techniques

November 1, 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

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high performance computing on graphics processing units: hgpu.org

A middleware for efficient stream processing in CUDA

Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU

Automatic fitting of spiking neuron models to electrophysiological recordings

On Dynamic Load Balancing on Graphics Processors

Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA

Program optimization space pruning for a multithreaded 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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