high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Fluid dynamics

Fast GPU-based fluid simulations using SPH

Oystein E. Krog, Anne C. Elster

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Tags: CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 470, Package, Tesla C2050

April 16, 2012 by hgpu

Shallow water simulations on multiple GPUs

Martin Lilleeng Saetra, Andre Rigland Brodtkorb

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, Tesla C2050, Tesla S1070

March 21, 2012 by hgpu

Performance analysis and optimization of the OP2 framework on many-core architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, nVidia, OpenMP, Optimization, Package, Performance, Tesla C2050

March 15, 2012 by hgpu

GPU-Accelerated Large-Eddy Simulation of Turbulent Channel Flows

Rey DeLeon, Inanc Senocak

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Tags: CUDA, Fluid dynamics, GPU cluster, Numerical simulation, nVidia

March 10, 2012 by hgpu

CUDA-enabled LBM Flow Simulation around Three Equilateral Cylinders using GPU Computing Processor

Muhammad Shafiqul Alam, Liang Cheng

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia

February 27, 2012 by hgpu

Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units

Xiong QinGang, Li Bo, Xu Ji, Fang XiaoJian, Wang XiaoWei, Wang LiMin, He XianFeng, Ge Wei

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Tags: Algorithms, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, Numerical simulation, nVidia, Tesla C2050

February 22, 2012 by hgpu

Multiscale Hemodynamics Using GPU Clusters

Mauro Bisson, Massimo Bernaschi, Simone Melchionna, Sauro Succi, Efthimios Kaxiras

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Tags: Biology, CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, Medicine, nVidia, Tesla C1060

February 17, 2012 by hgpu

Computational Fluid Dynamics using OpenCL – a Practical Introduction

T. Bednarz, Luke Domanski, J. A. Taylor

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Tags: Algorithms, Finite difference, Fluid dynamics, Navier-Stokes equations, nVidia, nVidia GeForce GT 330 M, OpenCL, Review

January 25, 2012 by hgpu

Efficient GPU implementation of a two waves WAF method for the two-dimensional one layer Shallow Water system on structured meshes

Marc de la Asuncion, Manuel J. Castro, E. D. Fernandez-Nieto, Jose M. Mantas, Sergio Ortega Acosta, Jose Manuel Gonzalez-Vida

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580

January 24, 2012 by hgpu

Scientific Computation on Graphics Processing Unit using CUDA

Pradip Narendrakumar Panchal

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Tags: Computer science, CUDA, Differential equations, FDTD, Finite difference, Finite-difference time-domain, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs, Thesis

January 23, 2012 by hgpu

GPGPU calculations of gas thermodynamic quantities

Igor Mracka, Peter Somora, Tibor Zacik

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Tags: CUDA, Differential equations, Fluid dynamics, nVidia, nVidia GeForce GTX 570, Tesla C2050

January 21, 2012 by hgpu

A Practical Visualization Strategy for Large-Scale Supernovae CFD Simulations

Derek K. Gerstmann, Toby Potter, Michael Houston, Paul Bourke, Kwan-Liu Ma, Andreas Wicenec

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Tags: ATI, ATI Radeon HD 6750 M, Fluid dynamics, OpenCL, OpenGL, Visualization

January 21, 2012 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

Applications

Fast GPU-based fluid simulations using SPH

Shallow water simulations on multiple GPUs

Performance analysis and optimization of the OP2 framework on many-core architectures

GPU-Accelerated Large-Eddy Simulation of Turbulent Channel Flows

CUDA-enabled LBM Flow Simulation around Three Equilateral Cylinders using GPU Computing Processor

Efficient parallel implementation of the lattice Boltzmann method on large clusters of graphic processing units

Multiscale Hemodynamics Using GPU Clusters

Computational Fluid Dynamics using OpenCL – a Practical Introduction

Efficient GPU implementation of a two waves WAF method for the two-dimensional one layer Shallow Water system on structured meshes

Scientific Computation on Graphics Processing Unit using CUDA

GPGPU calculations of gas thermodynamic quantities

A Practical Visualization Strategy for Large-Scale Supernovae CFD Simulations

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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