high performance computing on graphics processing units: hgpu.org

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A GPU-supported High-Level Programming Language for Image Processing

Ami Ono, Katsuhiko Kondo, Takafumi Inaba, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: CUDA, Data parallelism, High-level Languages, Image processing, nVidia, nVidia GeForce GTX 280

December 25, 2011 by hgpu

Toolchain for programming, simulating and studying the XMT many-core architecture

Fuat Keceli, Alexandros Tzannes, George C. Caragea, Rajeev Barua, Uzi Vishkin

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Tags: Algorithms, Compilers, Computer science, Hardware Architecture, nVidia, nVidia GeForce GTX 280, Package

December 24, 2011 by hgpu

Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU

Jacques M. Bahi, Raphael Couturier, Christophe Guyeux, Pierre-Cyrille Heam

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Pseudo-random number generators, Security, Tesla C1060

December 23, 2011 by hgpu

Porting and optimizing MAGFLOW on CUDA

Giuseppe Bilotta, Eugenio Rustico, Alexis Herault, Annamaria Vicari, Giovanni Russo, Ciro Del Negro, Giovanni Gallo

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Tags: CUDA, Earth and Space Sciences, Geoscience, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization

December 21, 2011 by hgpu

Parallel paradigms in optimal structural design

Van Huyssteen, Salomon Stephanus

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

December 18, 2011 by hgpu

Massively Parallel Logic Simulation with GPUs

Yuhao Zhu, Bo Wang, Yangdong Deng

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Tags: Algorithms, Computer science, CUDA, Electronic design automation, nVidia, nVidia GeForce GTX 280, Optimization, Performance

December 17, 2011 by hgpu

Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng, Naren Ramakrishnan

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Tags: Algorithms, Computer science, CUDA, Data mining, Neuroscience, nVidia, nVidia GeForce GTX 280

December 17, 2011 by hgpu

Customizable Memory Schemes for Data Parallel Accelerators

Chunyang Gou

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques, Thesis

December 17, 2011 by hgpu

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Uzi Vishkin, Rajeev Barua

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Tags: Algorithms, Benchmarking, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Prefetch, Rendering, Thesis

December 16, 2011 by hgpu

On the numerical solution of chaotic dynamical systems using extend precision floating point arithmetic and very high order numerical methods

Scott A. Sarra, Clyde Meador

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Tags: Chaotic Dynamics, CUDA, Extended precision, nVidia, nVidia GeForce GTX 280, ODEs, Ordinary differential equations, Package, Physics

December 15, 2011 by hgpu

Accelerating Live Graph-Cut-Based Object Tracking Using CUDA

Ismael Daribo, Zachary A. Garrett, Yuki Takaya, Hideo Saito

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 280

December 14, 2011 by hgpu

RaVioli: a GPU Supported High-Level Pseudo Real-time Video Processing Library

Katsuhiko Kondo, Takafumi Inaba, Hiroko Sakurai, Masaomi Ohno, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 280

December 13, 2011 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

A GPU-supported High-Level Programming Language for Image Processing

Toolchain for programming, simulating and studying the XMT many-core architecture

Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU

Porting and optimizing MAGFLOW on CUDA

Parallel paradigms in optimal structural design

Massively Parallel Logic Simulation with GPUs

Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Customizable Memory Schemes for Data Parallel Accelerators

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

On the numerical solution of chaotic dynamical systems using extend precision floating point arithmetic and very high order numerical methods

Accelerating Live Graph-Cut-Based Object Tracking Using CUDA

RaVioli: a GPU Supported High-Level Pseudo Real-time Video Processing Library

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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