Parallel simulation on the hypercube multiprocessor

SummaryThis paper focuses upon a particular conservative algorithm for parallel simulation, the Time of Next Event (TNE) suite of algorithms [13]. TNE relies upon a shortest path algorithm which is independently executed on each processor in order to unblock LPs in the processor and to increase the parallelism of the simulation. TNE differs fundamentally from other conservative approaches in that it takes advantage of having several LPs assigned to each processor, and does not rely upon message passing to provide lookahead. Instead, it relies upon a shortest path algorithm executed independently in each processor. A deadlock resolution algorithm is employed for interprocessor deadlocks. We describe an empirical investigation of the performance of TNE on the iPSC/i860 hypercube multiprocessor. Several factors which play an important role in TNE's behavior are identified, and the speedup relative to a fast uniprocessor-based event list algorithm is reported. Our results indicate that TNE yields good speedups and out-performs an optimized version of the Chandy&Misra-null message (CMB) algorithm. TNE was 2–5 times as fast as the CM approach for less than 10 processors (and 1.5–3 times as fast when more than 10 processors were used for the same population of processes.)

[1]  Douglas W. Jones,et al.  An empirical comparison of priority-queue and event-set implementations , 1986, CACM.

[2]  Rhonda Righter,et al.  Distributed simulation of discrete event systems , 1989, Proc. IEEE.

[3]  Jayadev Misra,et al.  Distributed discrete-event simulation , 1986, CSUR.

[4]  K. Mani Chandy,et al.  Distributed computation on graphs: shortest path algorithms , 1982, CACM.

[5]  Eric G. Manning,et al.  Distributed Simulation Using a Network of Processors , 1979, Comput. Networks.

[6]  Azzedine Boukerche,et al.  A static partitioning and mapping algorithm for conservative parallel simulations , 1994, PADS '94.

[7]  K. Mani Chandy,et al.  A Distributed Graph Algorithm: Knot Detection , 1982, TOPL.

[8]  K. Mani Chandy,et al.  Distributed Simulation: A Case Study in Design and Verification of Distributed Programs , 1979, IEEE Transactions on Software Engineering.

[9]  Boris D. Lubachevsky,et al.  Efficient distributed event driven simulations of multiple-loop networks , 1988, SIGMETRICS '88.

[10]  Takashi Sato,et al.  PACS: a parallel microprocessor array for scientific calculations , 1983, TOCS.

[11]  R. M. Fujimoto,et al.  Parallel discrete event simulation , 1989, WSC '89.

[12]  Miroslaw Malek,et al.  MOPAC: A Partitionable and Reconfigurable Multicomputer Array , 1983, ICPP.

[13]  David M. Nicol.  Parallel discrete-event simulation of FCFS stochastic queueing networks , 1988, PPoPP 1988.

[14]  Randal E. Bryant,et al.  SIMULATION OF PACKET COMMUNICATION ARCHITECTURE COMPUTER SYSTEMS , 1977 .

[15]  M NicolDavid.  Parallel discrete-event simulation of FCFS stochastic queueing networks , 1988 .

[16]  Robert E. Benner,et al.  Development of Parallel Methods for a $1024$-Processor Hypercube , 1988 .

[17]  Edward D. Lazowska,et al.  Conservative parallel simulation for systems with no lookahead prediction , 1990 .

[18]  David R. Jefferson,et al.  Virtual time , 1985, ICPP.

[19]  Azzedine Boukerche.  Performance analysis of distributed simulation , 1990 .

[20]  Charles L. Seitz,et al.  Variants of the Chandy-Misra-Bryant Distributed Discrete-Event Simulation Algorithm , 1988 .

[21]  Tse-yun Feng,et al.  A Survey of Interconnection Networks , 1981, Computer.

[22]  Carl Tropper,et al.  The distributed simulation of clustered processes , 1991, Distributed Computing.