A hybrid GPU cluster and volunteer computing platform for scalable deep learning

2018 ◽  
Vol 74 (7) ◽  
pp. 3236-3263 ◽  
Author(s):  
Ekasit Kijsipongse ◽  
Apivadee Piyatumrong ◽  
Suriya U-ruekolan
Keyword(s):  
Deep Learning ◽  
Volunteer Computing ◽  
Gpu Cluster ◽  
Computing Platform

scholarly journals Lightweight Volunteer Computing Platform using Web Workers

2017 ◽  
Vol 108 ◽  
pp. 948-957 ◽  
Author(s):  
Pawel Chorazyk ◽  
Mateusz Godzik ◽  
Kamil Pietak ◽  
Wojciech Turek ◽  
Marek Kisiel-Dorohinicki ◽  
...  
Keyword(s):  
Volunteer Computing ◽  
Computing Platform

scholarly journals Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

2020 ◽  
Vol 245 ◽  
pp. 09014
Author(s):  
Chao Jiang ◽  
David Ojika ◽  
Sofia Vallecorsa ◽  
Thorsten Kurth ◽  
Prabhat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Heterogeneous Computing ◽  
Scientific Data ◽  
Great Promise ◽  
University Of Florida ◽  
Computing Platform ◽  
Scientific Data Analysis ◽  
The University ◽  
Heterogeneous Computing Platform

AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.

Interference-aware parallelization for deep learning workload in GPU cluster

2020 ◽  
Vol 23 (4) ◽  
pp. 2689-2702 ◽  
Author(s):  
Xin Geng ◽  
Haitao Zhang ◽  
Zhengyang Zhao ◽  
Huadong Ma
Keyword(s):  
Deep Learning ◽  
Gpu Cluster

CrowdVision: A Computing Platform for Video Crowdprocessing Using Deep Learning

2019 ◽  
Vol 18 (7) ◽  
pp. 1513-1526 ◽  
Author(s):  
Zongqing Lu ◽  
Kevin Chan ◽  
Shiliang Pu ◽  
Thomas La Porta
Keyword(s):  
Deep Learning ◽  
Computing Platform

Virtual EZ Grid

2012 ◽  
Vol 3 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Mohamed Ben Belgacem ◽  
Nabil Abdennadher ◽  
Marko Niinimaki
Keyword(s):  
Distributed Computing ◽  
Computing System ◽  
Medical Applications ◽  
Volunteer Computing ◽  
Computing Platform

This paper presents the Virtual EZ Grid project, based on the XtremWeb-CH (XWCH) volunteer computing platform. The goal of the project is to introduce a flexible distributed computing system, with (i) an infrastructure with a non-trivial amount of computing resources from various institutes, (ii) a stable platform that manages these computing resources and provides advanced interfaces for applications, and (iii) a set of applications that take benefit of the platform. This paper concentrates on the application support of the new version of XWCH, and describes how two medical applications, MedGIFT and NeuroWeb, utilise it.

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