high performance computing on graphics processing units: hgpu.org

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StoreGPU: exploiting graphics processing units to accelerate distributed storage systems

Samer Al-Kiswany, Abdullah Gharaibeh, Elizeu Santos-Neto, George Yuan, Matei Ripeanu

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 9800 GTX, Package, Storage system

November 6, 2010 by hgpu

Accelerating Dust Temperature Calculations with Graphics Processing Units

Patrik Jonsson, Joel Primack

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Package, Physics, Tesla C1060

November 6, 2010 by hgpu

Fast scale invariant feature detection and matching on programmable graphics hardware

Nico Cornelis, Luc Van Gool

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce 8800 GTX

November 6, 2010 by hgpu

Efficient simulation of large-scale spiking neural networks using CUDA graphics processors

Jayram M. Nageswaran, Nikil Dutt, Jeffrey L. Krichmar, Alex Nicolau, Alex Veidenbaum

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Tags: Biology, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, STDP

November 6, 2010 by hgpu

Magnetohydrodynamics simulations on graphics processing units

Hon-Cheng Wong, Un-Hong Wong, Xueshang Feng, Zesheng Tang

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Tags: Computational Physics, CUDA, Electrodynamics, Magnetohydrodynamics, nVidia, nVidia GeForce GTX 295, Physics

November 6, 2010 by hgpu

SAPPORO: A way to turn your graphics cards into a GRAPE-6

Evghenii Gaburov, Stefan Harfst, Simon P. Zwart

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce 9800 GX2, Package, Physics

November 6, 2010 by hgpu

PacketShader: a GPU-accelerated software router

Sangjin Han, Keon Jang, KyoungSoo Park, Sue Moon

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Tags: Computer science, CUDA, Network communications, nVidia, nVidia GeForce GTX 480, Software router

November 6, 2010 by hgpu

GPU’s for event reconstruction in the FairRoot framework

M. {Al-Turany}, F. {Uhlig}, R. {Karabowicz}

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Tags: CUDA, Instrumentation and measurement, Nuclear physics, nVidia, Package, Particle physics and field theory, Physics, Tesla C1060

November 5, 2010 by hgpu

Clustering billions of data points using GPUs

Ren Wu, Bin Zhang, Meichun Hsu

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Tags: Algorithms, Clustering, Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 280

November 5, 2010 by hgpu

On Dynamic Load Balancing on Graphics Processors

Daniel Cederman, Philippas Tsigas

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 9600 GT, Optimization, Performance

November 5, 2010 by hgpu

MPI within a GPU

Bobby Dalton Young

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Tags: Computer science, CUDA, Data parallelism, MPI, nVidia, nVidia GeForce 8800 GTS, Thesis, Virtualization

November 5, 2010 by hgpu

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Allen D. Malony,Scott Biersdorff,Wyatt Spear,Shangkar Mayanglambam

Tags: Computer science, CUDA, nVidia, Performance

November 5, 2010 by hgpu

sycl-bench: SYCL Benchmark Suite

SYCL-Bench: A Versatile Cross-Platform Benchmark Suite for Heterogeneous Computing

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Ginkgo: a high-performance linear algebra library for manycore systems

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

GROMACS with CUDA-aware MPI direct GPU communication support

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Rodinia_SYCL: The Rodinia Benchmark Suite in SYCL

The Rodinia Benchmark Suite in SYCL

Neural Architecture Search Without Training

Neural Architecture Search without Training

OpenABLext: An extension to OpenABL, a framework to generate high- performance code for agent-based simulations

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

SOFF: SNU OpenCL Framework for FPGAs

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

GCOOSpDM: Sparse-dense matrix-matrix multiplication on GPUs

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Programming Your GPU with OpenMP: A Hands-On Introduction

Evaluating the performance of HPC-style SYCL applications

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2020

November

24-27

Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2021

January

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Shenzhen, China

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14-16

Bangkok, Thailand

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