We present the results obtained by using an evolution of our CUDA-based solution for the exploration, via a breadth first search, of large graphs. This latest version exploits at its best the features of the Kepler architecture and relies on a combination of techniques to reduce both the number of communications among the GPUs and the amount of exchanged data. The final result is a code that can visit more than 800 billion edges in a second by using a cluster equipped with 4,096 Tesla K20X GPUs.

Parallel Distributed Breadth First Search on the Kepler Architecture

Bernaschi M;Mastrostefano E
2016

Abstract

We present the results obtained by using an evolution of our CUDA-based solution for the exploration, via a breadth first search, of large graphs. This latest version exploits at its best the features of the Kepler architecture and relies on a combination of techniques to reduce both the number of communications among the GPUs and the amount of exchanged data. The final result is a code that can visit more than 800 billion edges in a second by using a cluster equipped with 4,096 Tesla K20X GPUs.
2016
Istituto Applicazioni del Calcolo ''Mauro Picone''
Inglese
27
7
2091
2102
http://www.scopus.com/inward/record.url?eid=2-s2.0-84976394878&partnerID=q2rCbXpz
Breadth First Search
CUDA
GPU
Large graphs
Parallel computing
3
info:eu-repo/semantics/article
262
Bisson, M; Bernaschi, M; Mastrostefano, E
01 Contributo su Rivista::01.01 Articolo in rivista
none
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/328046
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