high performance computing on graphics processing units: hgpu.org

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Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines

Antonio Wendell De Oliveira Rodrigues, Frederic Guyomarc'H, Jean-Luc Dekeyser, Yvonnick Le Menach

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Tags: Code generation, Computer science, Conjugate gradient solver, nVidia, OpenCL, Tesla S1070

July 5, 2011 by hgpu

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs

Jacek Blazewicz, Wojciech Frohmberg, Michal Kierzynka, Erwin Pesch, Pawel Wojciechowski

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Source codes

Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Package, Sequence alignment, Tesla S1070

June 21, 2011 by hgpu

Solving 2D Nonlinear Unsteady Convection-Diffusion Equations on Heterogenous Platforms with Multiple GPUs

Canqun Yang, Zhen Ge, Juan Chen, Feng Wang, Yunfei Du

Tags: Diffusion equation, Heterogeneous systems, nVidia, Physics, Tesla S1070

June 20, 2011 by hgpu

Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

Lifan Xu, Michela Taufer, Stuart Collins, Dionisios G. Vlachos

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Tags: Chemistry, CUDA, Data parallelism, GPU cluster, Monte Carlo simulation, nVidia, Physics, Tesla S1070

June 9, 2011 by hgpu

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Mathieu Faverge, Hatem Ltaief, Samuel Thibault, Stanimire Tomov

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Tags: Computer science, CUDA, Factorization, GPU cluster, Linear Algebra, nVidia, nVidia Quadro FX 5800, Tesla S1070

June 5, 2011 by hgpu

A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators

Hatem Ltaief, Stanimire Tomov, Rajib Nath, Peng Du and Jack Dongarra

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla S1070

June 4, 2011 by hgpu

Modeling Rotor Wakes with a Hybrid OVERFLOW-Vortex Method on a GPU Cluster

Mark J. Stock, Adrin Gharakhani, Christopher P. Stone

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Tags: Fluid dynamics, Fluid simulation, GPU cluster, nVidia, nVidia GeForce GTX 275, Tesla S1070

June 3, 2011 by hgpu

A GPU-accelerated Boundary Element Method and Vortex Particle Method

Mark J. Stock, Adrin Gharakhani

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Tags: Fluid dynamics, Fluid simulation, GPU cluster, nVidia, nVidia GeForce GTX 275, Tesla S1070

June 3, 2011 by hgpu

Spherical harmonic transform on heterogeneous architectures using hybrid programming

Mikolaj Szydlarski, Pierre Esterie, Joel Falcou, Laura Grigori, Radek Stompor

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, MPI, nVidia, OpenMP, Tesla S1070

June 2, 2011 by hgpu

Optimizing Sweep3D for Graphic Processor Unit

Chunye Gong, Jie Liu, Zhenghu Gong, Jin Qin, Jing Xie

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Tags: Boltzmann equation, Computational Physics, CUDA, nVidia, Physics, Tesla S1070

May 25, 2011 by hgpu

Accelerating the Sweep3D for a Graphic Processor Unit

Chunye Gong, Jie Liu, Haitao Chen, Jing Xie, Zhenghu Gong

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Tags: Boltzmann equation, Computational Physics, CUDA, nVidia, Physics, Tesla S1070

May 25, 2011 by hgpu

A Modeling Approach based on UML/MARTE for GPU Architecture

Antonio Wendell De Oliveira Rodrigues, Frederic Guyomarc'H, Jean-Luc Dekeyser

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Tags: Code generation, Computer science, Conjugate gradient solver, nVidia, OpenCL, Tesla S1070

May 24, 2011 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs

Solving 2D Nonlinear Unsteady Convection-Diffusion Equations on Heterogenous Platforms with Multiple GPUs

Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators

Modeling Rotor Wakes with a Hybrid OVERFLOW-Vortex Method on a GPU Cluster

A GPU-accelerated Boundary Element Method and Vortex Particle Method

Spherical harmonic transform on heterogeneous architectures using hybrid programming

Optimizing Sweep3D for Graphic Processor Unit

Accelerating the Sweep3D for a Graphic Processor Unit

A Modeling Approach based on UML/MARTE for GPU Architecture

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

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

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