Abstract

Many-core systems connected by 3D Networks-on-Chip (NoC) are emerging as a promising computation engine for systems like cloud computing servers, big data systems, etc . Mapping applications at runtime to 3D NoCs is the key to maintain high throughput of the overall chip under a thermal/power constraint. However, the goals of optimizing both the communication latency and chip peak temperature are contradicting due to several reasons. First, exploiting the vertical TSV links can accelerate communications, while low peak temperature prefers that the tasks to be mapped closer to the heat sink, instead of using the vertical links. Second, mapping tasks in close proximity can reduce communication latency, but at the cost of poor heat dissipation. To address these issues, in this paper, we propose an efficient runtime mapping algorithm to reduce both communication latency and overall application running time under thermal constraint. In essence, this algorithm first selects a 3D cuboid core region of a specific shape for each incoming application by setting the region's number of occupied vertical layers and its distance to the heat sink, in order to optimize its communication performance and peak temperature. Next, the exact locations of the core regions in the chip are determined, followed by a task-to-core mapping. A defragmentation algorithm is also proposed to keep free core regions contiguous. The experimental results have confirmed that, compared to two recently proposed runtime mapping algorithms, our proposed approach can reduce the total running time by up to 48% and communication cost by up to 44%, with a low runtime overhead.

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