Brief announcement: a reinforcement learning approach for dynamic load-balancing of parallel digital logic simulation

Authors:
Sina Meraji;Wei Zhang;Carl Tropper
Affiliations:
School of Computer Science, McGill University, Montreal, Canada;School of Computer Science, McGill University, Montreal, Canada;School of Computer Science, McGill University, Montreal, Canada
Venue:
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Year:
2010

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Abstract

In this paper, we present a dynamic load-balancing algorithm for parallel digital logic simulation making use of reinforcement learning. We first introduce two dynamic load-balancing algorithms oriented towards balancing the computational and communication load respectively and then utilize reinforcement learning to create an algorithm which is a combination of the first two algorithms. In addition, the algorithm determines the value of two important parameters-the number of processors which participate in the algorithm and the load which is exchanged during its execution. We investigate the algorithms on gate level simulations of several open source VLSI circuits.