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GPU accelerated simulations of bluff body flows using vortex particle methods

Diego Rossinelli, Michael Bergdorf, Georges-Henri Cottet, Petros Koumoutsakos

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce 8000 GTS, nVidia GeForce 9800 GX2, nVidia Quadro FX 5600, OpenGL

November 7, 2010 by hgpu

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

CUDAnative.jl: Julia support for native CUDA programming

Abstractions for Programming Graphics Processors in High-Level Programming Languages

GPU explicit-state model checking

Porting tree-based hash table compression to GPGPU model checking

OCLADock: OpenCL Accelerated Molecular Docking

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

jax-md: Differentiable, Hardware Accelerated, Molecular Dynamics

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

libmolgrid: Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Single-Assignment C Base

Array Languages Make Neural Networks Fast

PyTorch: An Imperative Style, High-Performance Deep Learning Library

PyTorch: An Imperative Style, High-Performance Deep Learning Library

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2020

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27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

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13-15

Boston, USA

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Boston, MA, USA

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Larnaca, Cyprus

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