Memory Aware Load Balance Strategy on a Parallel Branch-and-Bound Application

TL;DR

This paper proposes a memory-aware load balancing strategy for parallel branch-and-bound algorithms on multicore systems, utilizing a new model to improve performance by addressing memory contention issues.

Contribution

It introduces the Multicore Cluster Model (MCM) that captures key performance factors like memory hierarchy and contention, and applies it to enhance load balancing in a branch-and-bound application.

Findings

Improved performance using MCM-based load balancing.

Effective handling of memory contention in multicore systems.

Demonstrated applicability to modern high-performance systems.

Abstract

The latest trends in high-performance computing systems show an increasing demand on the use of a large scale multicore systems in a efficient way, so that high compute-intensive applications can be executed reasonably well. However, the exploitation of the degree of parallelism available at each multicore component can be limited by the poor utilization of the memory hierarchy available. Actually, the multicore architecture introduces some distinct features that are already observed in shared memory and distributed environments. One example is that subsets of cores can share different subsets of memory. In order to achieve high performance it is imperative that a careful allocation scheme of an application is carried out on the available cores, based on a scheduling model that considers the main performance bottlenecks, as for example, memory contention. In this paper, the {\em Multicore Cluster Model} (MCM) is proposed, which captures the most relevant performance characteristics in multicores systems such as the influence of memory hierarchy and contention. Better performance was achieved when a load balance strategy for a Branch-and-Bound application applied to the Partitioning Sets Problem is based on MCM, showing its efficiency and applicability to modern systems.