Memory Bank Partitioning for Fixed-Priority Tasks in a Multi-core System

Abstract

In a multi-core platform, resources, such as memory banks and buses, are mostly shared among all cores for power, performance, and cost reasons. The access interference on the shared resources poses a major challenge on the analysis of real-time properties, but can be alleviated if task data partition onto memory banks is applied with care. In this paper, we consider to schedule RAS (resource access sporadic) tasks onto a platform consisting of homogeneous cores and capacity-limited memory banks. According to our observation, we should avoid internal data spreading among the memory banks for a task while advocate external data spreading among memory banks for a given task set. We propose a two-phase algorithm with (4 + ρ + 3(2γ+1)/γ) speedup factor and (γ + 1) memory augmentation factor, where ρ γ 0 and ρ ≥ 1. The derived adjustable resource augmentation factors can be useful in terms of system synthesis and schedulability. Moreover, under the premise that a given task set is feasible, we devise a bi-section approach that can derive a schedulable solution requiring the least amount of memory augmentation. According to our experiment results, the proposed algorithm significantly outperformed the state-of-the-art algorithm [15] in terms of schedulability test even when memory augmentation is prohibited.