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SiftCU: An Accelerated Cuda Based Implementation of SIFT

Mahdi S. Mohammadi, Mehdi Rezaeian

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GT 440 GT, nVidia GeForce GTX 670, Package

December 2, 2014 by hgpu

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

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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

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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

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Shenzhen, China

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Bangkok, Thailand

International Conference on Wireless Networks and Embedded Systems (ICWNES’20), 2020

2020

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Bangkok, Thailand

International Joint Conference on Signals, Systems and Computers (CSSC’20), 2020

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