high performance computing on graphics processing units: hgpu.org

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Fast BVH Construction on GPUs

C. Lauterbach, M. Garland, S. Sengupta, D. Luebke, D. Manocha

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Tags: Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Raytracing

January 7, 2011 by hgpu

Message passing on data-parallel architectures

Jeff A. Stuart, John D. Owens

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Tags: Computer science, GPU cluster, MPI, Networks, nVidia, nVidia GeForce GTX 280

January 6, 2011 by hgpu

An asymmetric distributed shared memory model for heterogeneous parallel systems

Isaac Gelado, John E. Stone, Javier Cabezas, Sanjay Patel, Nacho Navarro, Wen-mei W. Hwu

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Tags: Computer science, CUDA, Memory, Memory model, nVidia, nVidia GeForce GTX 280

January 6, 2011 by hgpu

q-state Potts model metastability study using optimized GPU-based Monte Carlo algorithms

Ezequiel E. Ferrero, Juan Pablo De Francesco, Nicolas Wolovick, Sergio A. Cannas

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Source codes

Tags: Computational Physics, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, nVidia GeForce GTX 480, Package, Physics, Statistical Mechanics

January 6, 2011 by hgpu

Revisiting sorting for GPGPU stream architectures

Duane G. Merrill, Andrew S. Grimshaw

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 9800 GT, nVidia GeForce 9800 GTX, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia Quadro FX 5600, Sorting

January 5, 2011 by hgpu

A complete modular resultant algorithm targeted for realization on graphics hardware

Pavel Emeliyanenko

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280

January 5, 2011 by hgpu

Modular Resultant Algorithm for Graphics Processors

Pavel Emeliyanenko

Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280

January 5, 2011 by hgpu

Nuclei: GPU-Accelerated Many-Core Network Coding

Hassan Shojania, Baochun Li, Xin Wang

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce 8600 M GT, nVidia GeForce 8800 GT, nVidia GeForce GTX 280

January 4, 2011 by hgpu

Local Search Algorithms on Graphics Processing Units. A Case Study: The Permutation Perceptron Problem

The Van Luong, Nouredine Melab, El-Ghazali Talbi

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8600 M GT, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Search

January 4, 2011 by hgpu

Towards Large-Scale Molecular Dynamics Simulations on Graphics Processors

Joseph E. Davis, Adnan Ozsoy, Sandeep Patel, Michela Taufer

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Tags: Biology, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia Quadro FX 5600

January 3, 2011 by hgpu

Cooperative Multitasking for GPU-Accelerated Grid Systems

Fumihiko Ino, Akihiro Ogita, Kentaro Oita, Kenichi Hagihara

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Tags: Computer science, CUDA, Grid, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 280

January 3, 2011 by hgpu

Generation of Random Numbers on Graphics Processors: Forced Indentation In Silico of the Bacteriophage HK97

A. Zhmurov, K. Rybnikov, Y. Kholodov, V. Barsegov

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Tags: Biological Physics, Chemical Physics, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Physics, Pseudo-random number generators

January 2, 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

Fast BVH Construction on GPUs

Message passing on data-parallel architectures

An asymmetric distributed shared memory model for heterogeneous parallel systems

q-state Potts model metastability study using optimized GPU-based Monte Carlo algorithms

Revisiting sorting for GPGPU stream architectures

A complete modular resultant algorithm targeted for realization on graphics hardware

Modular Resultant Algorithm for Graphics Processors

Nuclei: GPU-Accelerated Many-Core Network Coding

Local Search Algorithms on Graphics Processing Units. A Case Study: The Permutation Perceptron Problem

Towards Large-Scale Molecular Dynamics Simulations on Graphics Processors

Cooperative Multitasking for GPU-Accelerated Grid Systems

Generation of Random Numbers on Graphics Processors: Forced Indentation In Silico of the Bacteriophage HK97

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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