high performance computing on graphics processing units: hgpu.org

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APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

Roberto Ammendola, Andrea Biagioni, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Pier Stanislao Paolucci, Davide Rossetti, Andrea Salamon, Gaetano Salina, Francesco Simula, Laura Tosoratto, Piero Vicini

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Tags: Computational Physics, Distributed computing, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, OpenMPI, Physics, QCD, Tesla C1060, Tesla M2050

February 21, 2011 by hgpu

Final Project Implementing Extremely Randomized Trees in CUDA

Michael A. Geary, HyoukJoong Lee, Davide Signorelli, Jordan Vaughan

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, OpenMP, Tesla C1060

February 20, 2011 by hgpu

XMalloc: A Scalable Lock-free Dynamic Memory Allocator for Many-core Machines

Xiaohuang Huang, Christopher I. Rodrigues, Stephen Jones, Ian Buck, Wen-mei Hwu

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Tags: Algorithms, Computer science, CUDA, Memory, Memory model, nVidia, Tesla C1060

February 20, 2011 by hgpu

Accelerating Particle Image Velocimetry Using Hybrid Architectures

Vivek Venugopal, Cameron D. Patterson, Kevin Shinpaugh

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Tags: Computer science, CUDA, FPGA, nVidia, Tesla C1060, Visualization

February 19, 2011 by hgpu

Efficiency Considerations of Cauchy Reed-Solomon Implementations on Accelerator and Multi-Core Platforms

Thomas Steinke, Kathrin Peter, and Sebastian Borchert

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Tags: Cell processor, Computer science, CUDA, Error recovery, FPGA, nVidia, Tesla C1060, Tesla C870

February 19, 2011 by hgpu

Optimization of HEP codes on GPUs

M. Al-Turany

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Tags: CUDA, High Energy Physics - Experiment, nVidia, nVidia GeForce 8400 GT, nVidia GeForce 8800 GT, nVidia GeForce GTX 480, nVidia Quadro NVS 290, Optimization, Physics, Tesla C1060

February 18, 2011 by hgpu

Power-aware Performance of Mixed Precision Linear Solvers for FPGAs and GPGPUs

JunKyu Lee, Junqing Sun, Gregory D. Peterson, Robert J. Harrison, Robert J. Hinde

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Tags: Computer science, Energy-efficient computing, FPGA, Linear Algebra, Mixed precision, nVidia, Performance, Tesla C1060

February 18, 2011 by hgpu

Accelerating Double Precision Floating-point Hessenberg Reduction on FPGA and Multicore Architectures

Miaoqing Huang, Lingyuan Wang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, Extended precision, FPGA, nVidia, nVidia GeForce GTX 480, OpenMP, Performance, Tesla C1060

February 18, 2011 by hgpu

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Pei-Hung Lin, Jagan Jayaraj, and Paul R. Woodward

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Tags: Cell processor, Code generation, Compilers, Computer science, CUDA, Fortran, High-level Languages, Memory model, nVidia, Tesla C1060

February 17, 2011 by hgpu

Enhancing the simulation of P systems for the SAT problem on GPUs

J.M. Cecilia, J.M. Garcia, G.D. Guerrero, M.A. Martinez-Amor, M.J. Perez-Jimenez, M. Ujaldon

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Tags: Biology, CUDA, nVidia, P systems, SAT, Tesla C1060

February 16, 2011 by hgpu

Accelerating the Stochastic Simulation Algorithm using Emerging Architectures

David Dewayne Jenkins

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Tags: Biology, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce 8600 M, Stochastic simulation, Tesla C1060, Tesla C870, Thesis

February 16, 2011 by hgpu

GPU Accelerated Stochastic Simulation

David D. Jenkins, Gregory D. Peterson

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Tags: Biology, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, Stochastic simulation, Tesla C1060

February 16, 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

APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

Final Project Implementing Extremely Randomized Trees in CUDA

XMalloc: A Scalable Lock-free Dynamic Memory Allocator for Many-core Machines

Accelerating Particle Image Velocimetry Using Hybrid Architectures

Efficiency Considerations of Cauchy Reed-Solomon Implementations on Accelerator and Multi-Core Platforms

Optimization of HEP codes on GPUs

Power-aware Performance of Mixed Precision Linear Solvers for FPGAs and GPGPUs

Accelerating Double Precision Floating-point Hessenberg Reduction on FPGA and Multicore Architectures

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Enhancing the simulation of P systems for the SAT problem on GPUs

Accelerating the Stochastic Simulation Algorithm using Emerging Architectures

GPU Accelerated Stochastic Simulation

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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