Publication Abstract
Investigating a Dynamic Loop Scheduling with Reinforcement Learning Approach to Load Balancing in Scientific Applications
Rashid, M., Banicescu, I., & Carino, R.L. (2008). Investigating a Dynamic Loop Scheduling with Reinforcement Learning Approach to Load Balancing in Scientific Applications. Proc. 7th International Symposium on Parallel and Distributed Computing – ISPDC 2008. Krakow, Poland: IEEE Computer Society Press. 123-130.
Abstract
The advantages of integrating reinforcement learning
(RL) techniques into scientific parallel time-stepping applications
have been revealed in research work over the
past few years. In some of these previous works, the object
of the integration is to automatically select the most appropriate
dynamic loop scheduling (DLS) algorithm from
a set of available algorithms with the purpose of improving
the application performance via load balancing during
the application execution. This paper investigates the
performance of such a dynamic loop scheduling with reinforcement
learning (DLS-with-RL) approach to load balancing.
The DLS-with-RL is most suitable for use in timestepping
scientific applications with parallel loops where
the RL agent can learn application performance at the end
of a time-step and take appropriate measures prior to starting
the next time-step. The automatic selection is performed
by the RL agent. The RL agent’s characteristics depend on
a learning rate parameter and a discount factor parameter.
In order to investigate the influences of these parameters,
an application that simulates wavepacket dynamics is incorporated
with a DLS-with-RL approach and allowed to
execute on a cluster of workstations. The application contains
three parallel loops with different time-varying characteristics.
The RL agent implemented two RL algorithms:
QLEARN and SARSA learning. Preliminary results indicate
that on a fixed number of processors, the simulation
completion time is not sensitive to the values of the learning
parameters used in the experiments. The results also
indicate that for this application, there is no advantage of
choosing one RL technique over another, even though the
techniques differed significantly in the number of times they
selected the various DLS algorithms.