Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)

TL;DR

This paper presents a parallel local search algorithm optimized for the Cell Broadband Engine architecture, demonstrating near-linear speedups and reduced variance in solving large combinatorial constraint problems.

Contribution

It introduces a parallel local search method tailored for Cell/BE that requires no shared memory, achieving efficient scalability on multiprocessor systems.

Findings

Achieves mostly linear and sometimes super-linear speedups

Reduces variance in solution times

Effective on large optimization benchmarks

Abstract

We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical.