Result: Challenges in parallel graph processing

Title:
Challenges in parallel graph processing
Authors:
LUMSDAINE, Andrew, GREGOR, Douglas, HENDRICKSON, Bruce, BERRY, Jonathan
Source:
Clusters and computational grids for scientific computingParallel processing letters. 17(1):5-20
Publisher Information:
Singapore: World Scientific Publishing, 2007.
Publication Year:
2007
Physical Description:
print, 21 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Electronics, Electronique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Analyse donnée, Data analysis, Análisis datos, Calcul réparti, Distributed computing, Cálculo repartido, Calcul scientifique, Scientific computation, Computación científica, Extraction information, Information extraction, Extracción información, Fouille donnée, Data mining, Busca dato, Haute performance, High performance, Alto rendimiento, Multitâche, Multithread, Multitarea, Mémoire répartie, Distributed memory, Memoria compartida, Parallélisme, Parallelism, Paralelismo, Plus court chemin, Shortest path, Camino más corto, Théorie graphe, Graph theory, Teoría grafo, Traitement parallèle, Parallel processing, Tratamiento paralelo, Parallel architectures, distributed memory, graph algorithms, graph theory, multithreading, shortest paths
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Indiana University, Bloomington, Indiana 47401, United States
Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
ISSN:
0129-6264
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18702949
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18702949
Database:
PASCAL Archive

Further Information

Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel computing resources are required to meet their computational and memory requirements. Unfortunately, the algorithms, software, and hardware that have worked well for developing mainstream parallel scientific applications are not necessarily effective for large-scale graph problems. In this paper we present the inter-relationships between graph problems, software, and parallel hardware in the current state of the art and discuss how those issues present inherent challenges in solving large-scale graph problems. The range of these challenges suggests a research agenda for the development of scalable high-performance software for graph problems.