Improving scalability of Bag-of-Tasks applications running on master-slave platforms
Tipo
Artigo
Data de publicação
2009
Periódico
Parallel Computing
Citações (Scopus)
24
Autores
da Silva F.A.B.
Senger H.
Senger H.
Orientador
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Programa
Resumo
Bag-of-Tasks applications are parallel applications composed of independent tasks. Examples of Bag-of-Tasks (BoT) applications include Monte Carlo simulations, massive searches (such as key breaking), image manipulation applications and data mining algorithms. This paper analyzes the scalability of Bag-of-Tasks applications running on master-slave platforms and proposes a scalability-related measure dubbed input file affinity. In this work, we also illustrate how the input file affinity, which is a characteristic of an application, can be used to improve the scalability of Bag-of-Tasks applications running on master-slave platforms. The input file affinity was considered in a new scheduling algorithm dubbed Dynamic Clustering, which is oblivious to task execution times. We compare the scalability of the Dynamic Clustering algorithm to several other algorithms, oblivious and non-oblivious to task execution times, proposed in the literature. We show in this paper that, in several situations, the oblivious algorithm Dynamic Clustering has scalability performance comparable to non-oblivious algorithms, which is remarkable considering that our oblivious algorithm uses much less information to schedule tasks. © 2008 Elsevier B.V. All rights reserved.
Descrição
Palavras-chave
Assuntos Scopus
Bag-of-Tasks applications , Image manipulations , Independent tasks , Master-slave platforms , Mining algorithms , Monte Carlo Simulation (MCS) , Parallel applications , Scalability analysis , Task executions