Energy and communication aware task mapping for MPSoCs

• Published in: Journal of parallel and distributed computing Vol. 121; pp. 71 - 89
• Main Authors: Maqsood, Tahir, Tziritas, Nikos, Loukopoulos, Thanasis, Madani, Sajjad A., Khan, Samee U., Xu, Cheng-Zhong, Zomaya, Albert Y.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2018
• Subjects: Core mapping; Energy and communication aware mapping; Multi-processor system-on-chip (MPSoC); Network-on-chip (NoC); Task mapping; Core mapping; Task mapping; Energy and communication aware mapping; Multi-processor system-on-chip (MPSoC); Network-on-chip (NoC)
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2018.03.010

Minimizing energy consumption and network load is a major challenge for network-on-chip (NoC) based multi-processor systems-on-chip (MPSoCs). Efficient task and core mapping can greatly reduce the overall energy consumption and communication overhead among the interdependent tasks. In this paper, we propose a novel Knapsack based bin packing algorithm for workload consolidation that places tasks in such a manner that utilization of available processing elements is maximized, while network overhead, regarding the communication among the tasks, is minimized. We also propose a task swapping algorithm that attempts to further optimize the task placement produced by the bin packing algorithms. Moreover, several core mapping techniques are implemented and the performance of each technique is evaluated under varying configurations. In addition, we also apply a Pareto-efficient algorithm, on top of the bin packing algorithms, attempting to explore the solution in two dimensions, i.e., energy consumption and network load. The experimental results show that the proposed Knapsack based bin packing algorithm coupled with the Pareto-efficient algorithm achieves significant energy savings and reduction in network load as compared to state-of-the-art algorithms, as well as the greedy algorithm. Particularly, the Pareto-efficient algorithm when applied on top of the Knapsack algorithm shows on average 50% and 55% reduction in energy consumption and network load as compared to the greedy algorithm, respectively. While the proposed Pareto-efficient algorithm applied with Knapsack algorithm also demonstrate superior performance compared to three other state-of-the-art heuristics. •Introduce a new class of bin packing problem, i.e., variable benefit bin packing.•A Knapsack based bin packing algorithm is proposed for workload consolidation.•Proposed Knapsack algorithm reduces both energy consumption and network load.•Various mapping heuristics are proposed to map the bins onto cores of MPSoC.•Pareto-efficient algorithm to explore solutions in two dimensions simultaneously.