2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation (2010)
May 17, 2010 to May 19, 2010
Carl Tropper , Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
Wei Zhang , Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
Sina Meraji , Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
In this paper, we present a dynamic load-balancing algorithm for optimistic gate level simulation making use of a machine learning approach. We first introduce two dynamic load-balancing algorithms oriented towards balancing the computational and communication load respectively in a Time Warp simulator. In addition, we utilize a multi-state Q-learning approach to create an algorithm which is a combination of the first two algorithms. The Q-learning algorithm determines the value of three important parameters- the number of processors which participate in the algorithm, the load which is exchanged during its execution and the type of load-balancing algorithm. We investigate the algorithm on gate level simulations of several open source VLSI circuits.
multistate q-learning approach, dynamic load balancing, time warp, machine learning approach, open source VLSI circuits, optimistic gate level simulation
Carl Tropper, Wei Zhang, Sina Meraji, "A Multi-State Q-Learning Approach for the Dynamic Load Balancing of Time Warp", 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation, vol. 00, no. , pp. 1-8, 2010, doi:10.1109/PADS.2010.5471661