  This paper presents a multiagent approach as a paradigm for scheduling
parallel jobs in a parallel system. Scheduling parallel jobs is performed as a
means to balance the load of a system in order to improve the performance of a
parallel application. Parallel job scheduling is presented as a mapping between
two graphs: one represents the dependency of jobs and the other represents the
interconnection among processors. The usual implementation of parallel job
scheduling algorithms is via the master-slave paradigm. The master-slave
paradigm has inherent communication bottleneck that reduces the performance of
the system when more processors are needed to process the jobs. The multiagent
approach attempts to distribute the communication latency among the processors
which improves the performance of the system as the number of participating
processors increases. Presented in this paper is a framework for the behavior
of an autonomous agent that cooperates with other agents to achieve a community
goal of minimizing the processing time. Achieving this goal means an agent must
truthfully share information with other agents via {\em normalization}, {\em
task sharing}, and {\em result sharing} procedures. The agents consider a
parallel scientific application as a finite-horizon game where truthful
information sharing results into performance improvement for the parallel
application. The performance of the multiagent-based algorithm is compared to
that of an existing one via a simulation of the wavepacket dynamics using the
quantum trajectory method (QTM) as a test application. The average parallel
cost of running the QTM using the multiagent-based system is lower at higher
number of processors.
