Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources

TL;DR

This paper introduces a methodology for designing optimal schedules for parallelizing anytime algorithms that share resources, improving performance through statistical analysis and empirical validation.

Contribution

It provides a formal analysis and an algorithm for optimal scheduling of shared-resource processes, with theoretical and empirical evaluation.

Findings

The scheduling algorithm performs optimally under various distribution types.

Empirical results demonstrate improved performance on the Latin Square problem.

The methodology effectively leverages statistical characteristics of algorithms.

Abstract

The performance of anytime algorithms can be improved by simultaneously solving several instances of algorithm-problem pairs. These pairs may include different instances of a problem (such as starting from a different initial state), different algorithms (if several alternatives exist), or several runs of the same algorithm (for non-deterministic algorithms). In this paper we present a methodology for designing an optimal scheduling policy based on the statistical characteristics of the algorithms involved. We formally analyze the case where the processes share resources (a single-processor model), and provide an algorithm for optimal scheduling. We analyze, theoretically and empirically, the behavior of our scheduling algorithm for various distribution types. Finally, we present empirical results of applying our scheduling algorithm to the Latin Square problem.