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GPU Array Access Auto-Tuning

Nicolas Weber

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GT 620, nVidia GeForce GT 730, nVidia GeForce GTX 1080, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 570, nVidia GeForce GTX 590, nVidia GeForce GTX 680, nVidia GeForce GTX 780, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Performance, performance portability, Tesla C2070, Tesla K20, Thesis

August 8, 2017 by hgpu

An hybrid AES-256-GCM implementation for NEON CPU & CUDA GPU

Romain Dolbeau

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Tags: AES, Computer science, CUDA, nVidia, nVidia GeForce GT 620, Package, Security

September 28, 2014 by hgpu

Graphics Processing Unit Bloom Filters: Classical and Probabilistic

Joshua Michael Pyle

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Tags: Bloom filter, Computer science, CUDA, Data Structures and Algorithms, Databases, nVidia, nVidia GeForce GT 620, Security, Thesis

August 13, 2014 by hgpu

Accelerating Fast Fourier Transforms Using Hadoop and CUDA

Rostislav Tsiomenko, Bradley S. Rees

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Tags: Computer science, CUDA, FFT, nVidia, nVidia GeForce GT 620

January 22, 2013 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

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Naha, Okinawa, Japan

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

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

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