high performance computing on graphics processing units: hgpu.org

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High-performance SIMT code generation in an active visual effects library

Jay L.T. Cornwall, Lee Howes, Paul H.J. Kelly, Phil Parsonage, Bruno Nicoletti

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Tags: Algorithms, Code generation, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 260, Performance, Software Engineering, Visualization

September 11, 2011 by hgpu

The Arcane development framework

Gilles Grospellier, Benoit Lelandais

Tags: Computer science, Numerical simulation, Performance, Software Engineering

August 28, 2011 by hgpu

PFunc: modern task parallelism for modern high performance computing

Prabhanjan Kambadur, Anshul Gupta, Amol Ghoting, Haim Avron, Andrew Lumsdaine

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Tags: Computer science, Package, Performance, Programming techniques, Software Engineering

August 26, 2011 by hgpu

Challenging cloning related problems with GPU-based algorithms

Thierry Lavoie, Michael Eilers-Smith, Ettore Merlo

Tags: Computer science, Software Engineering, String matching

August 26, 2011 by hgpu

Implementation of a programming environment with a multithread model for reconfigurable systems

Keisuke Dohi, Yuichiro Shibata, Tsuyoshi Hamada, Tomonari Masada, Kiyoshi Oguri, Duncan A. Buell

Tags: Computer science, Performance, Software Engineering

August 24, 2011 by hgpu

SCF: a device- and language-independent task coordination framework for reconfigurable, heterogeneous systems

V. Aggarwal, R. Garcia, G. Stitt, A. George, H. Lam

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Tags: Computer science, FPGA, Heterogeneous systems, Performance, Software Engineering, Task scheduling

August 24, 2011 by hgpu

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

Andreas Klockner, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih

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Tags: Code generation, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 9400 M, nVidia GeForce GTX 295, nVidia GeForce GTX 480, OpenCL, Package, PyOpenCL, Python, Software Engineering, Tesla C1060

March 30, 2011 by hgpu

Programming Massively Parallel Architectures using MARTE: a Case Study

A. Wendell Rodrigues, Frederic Guyomarc'h, Jean-Luc Dekeyser

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Tags: Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Signal processing, Software Engineering

March 28, 2011 by hgpu

Using common graphics hardware for multi-agent traffic simulation with CUDA

David Strippgen, Kai Nagel

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GT, nVidia GeForce GTX 280, Software Engineering

March 8, 2011 by hgpu

OpenGL application live migration with GPU acceleration in personal cloud

Yunbiao Lin, Wei Wang, Kyle Gui

Tags: Algorithms, Computer science, OpenGL, Software Engineering, Virtualization

February 25, 2011 by hgpu

Scalable SMT-based verification of GPU kernel functions

Guodong Li, Ganesh Gopalakrishnan

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Tags: Computer science, CUDA, nVidia, Package, Software Engineering

February 9, 2011 by hgpu

DiVinE-CUDA – A Tool for GPU Accelerated LTL Model Checking

Jiri Barnat, Lubos Brim, Milan Ceska

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Tags: Computer science, CUDA, Distributed computing, nVidia, nVidia GeForce GTX 280, Software Engineering

November 9, 2010 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

High-performance SIMT code generation in an active visual effects library

The Arcane development framework

PFunc: modern task parallelism for modern high performance computing

Challenging cloning related problems with GPU-based algorithms

Implementation of a programming environment with a multithread model for reconfigurable systems

SCF: a device- and language-independent task coordination framework for reconfigurable, heterogeneous systems

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

Programming Massively Parallel Architectures using MARTE: a Case Study

Using common graphics hardware for multi-agent traffic simulation with CUDA

OpenGL application live migration with GPU acceleration in personal cloud

Scalable SMT-based verification of GPU kernel functions

DiVinE-CUDA – A Tool for GPU Accelerated LTL Model Checking

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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