STOCHASTIC AUTOMATA NETWORK FOR MODELING PARALLEL SYSTEMS

被引:123
作者
PLATEAU, B [1 ]
ATIF, K [1 ]
机构
[1] INST MATH APPL GRENOBLE,CALCUL PARALLECE GRP,F-38031 GRENOBLE,FRANCE
关键词
PERFORMANCE EVALUATION; MARKOV CHAIN; TENSOR PRODUCT; PARALLEL SYSTEMS; DISCRETE TIME SCALE;
D O I
10.1109/32.99196
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This paper is motivated by the study of the performance of parallel systems. The performance models of such systems are often complex to describe and hard to solve. The method presented here uses a modular representation of the system as a network of state-transition graphs. The state space explosion is handled by a decomposition technique. The dynamic behavior of the algorithm is analyzed under Markovian assumptions. The transition matrix of the chain is automatically derived using tensor algebra operators, under a format which involves a very limited storage cost.
引用
收藏
页码:1093 / 1108
页数:16
相关论文
共 22 条
[1]  
AGGARWAL S, 1983, PROTOCOL SPECIFICATI, V3
[2]  
Arbib M. A., 1968, ALGEBRAIC THEORY MAC
[3]  
ARNOLD A, 1989, 3RD P WORKSH PROT VE
[4]  
BERSON S, 1990, 1ST P INT C NUM SOL
[5]  
CHARRONBOST B, 1989, THESIS PARIS 7 U FRA
[6]  
CHIOLA G, 1991, 5TH P INT C MOD TECH, P117
[7]  
DAVIO M, 1981, IEEE T COMPUTERS, V30
[8]  
GENIET D, 1988, RR432 PAR SUD U TECH
[9]  
Hartmanis J., 1966, ALGEBRAIC STRUCTURE
[10]  
KOBAYASHI H, 1983, PROBABILITY THEORY C, pCH4