high performance computing on graphics processing units: hgpu.org

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OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

Christian T. Jacobs, Satya P. Jammy, Neil D. Sandham

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Tags: cfd, Code generation, Computer science, CUDA, Finite difference, Fluid dynamics, Mathematical Software, MPI, nVidia, OpenCL, Tesla K40

September 10, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Ahmed E. Helal, Paul Sathre, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, OpenCL, performance portability, Tesla K20

August 23, 2016 by hgpu

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Tingxing Dong, Azzam Haidar, Piotr Luszczek, Stanimire Tomov, Ahmad Abdelfattah, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Factorization, Fluid dynamics, Linear Algebra, nVidia, Tesla K40

August 23, 2016 by hgpu

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

Hartwig Anzt, Marc Baboulin, Jack Dongarra, Yvan Fournier, Frank Hulsemann, Amal Khabou, Yushan Wang

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Tags: cfd, Conjugate gradient solver, CUDA, Fluid dynamics, nVidia, OpenCL, Tesla K40

July 18, 2016 by hgpu

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

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Tags: 3D Graphics and Realism, CNN, Computational Complexity, CUDA, Deep learning, Fluid dynamics, Fluid simulation, Machine learning, nVidia, nVidia GeForce GTX Titan X

July 16, 2016 by hgpu

Using the pyMIC Offload Module in PyFR

Michael Klemm, Freddie Witherden, Peter Vincent

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Tags: Computer science, CUDA, Fluid dynamics, Intel Xeon Phi, Navier-Stokes equations, NSEs, OpenCL, OpenMP, Package, Python

July 8, 2016 by hgpu

GPU Based Real-Time Welding Simulation with Smoothed-Particle Hydrodynamics

Qing Gu

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Tags: cfd, CUDA, Fluid dynamics, nVidia, OpenGL, Physics, Thesis

June 28, 2016 by hgpu

Fluid Simulation by the Smoothed Particle Hydrodynamics Method: A Survey

T. Weaver, Z. Xiao

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Tags: CUDA, Fluid dynamics, Fluid simulation, nVidia, survey

April 16, 2016 by hgpu

Heterogeneous parallel algorithms for Computational Fluid Dynamics on unstructured meshes

Guillermo Andres Oyarzun Altamirano

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenCL, SoC, Tesla M2090, Thesis

March 5, 2016 by hgpu

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

G.R. Mudalige, I.Z. Reguly, M.B. Giles

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Tags: cfd, Code generation, Computer science, CUDA, Fluid dynamics, Fortran, nVidia, Programming techniques, Tesla K80

February 25, 2016 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Using the pyMIC Offload Module in PyFR

GPU Based Real-Time Welding Simulation with Smoothed-Particle Hydrodynamics

Fluid Simulation by the Smoothed Particle Hydrodynamics Method: A Survey

Heterogeneous parallel algorithms for Computational Fluid Dynamics on unstructured meshes

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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