DWS: Demand-aware Work-Stealing in Multi-programmed Multi-core Architectures
Proceedings of Programming Models and Applications on Multicores and Manycores
Competitive online adaptive scheduling for sets of parallel jobs with fairness and efficiency
Journal of Parallel and Distributed Computing
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With proliferation of multicore computers and multiprocessor systems, an imminent challenge is to efficiently schedule parallel applications on these resources. In contrast to conventional static scheduling, adaptive schedulers that dynamically allocate processors to jobs possess good potential for improving processor utilization and speeding up job's execution. In this paper, we focus on adaptive scheduling of malleable jobs with periodic processor reallocations based on parallelism feedback of the jobs and allocation policy of the system. We present an efficient adaptive scheduler Acdeq that provides parallelism feedback using an adaptive controller A-Control and allocates processors based on the well-known Dynamic Equipartitioning algorithm (Deq). Compared to A-Greedy, an existing adaptive scheduler that experiences feedback instability thus incurs unnecessary scheduling overheads, we show that A-Control achieves much more stable feedback among other desirable control-theoretic properties. Furthermore, we analyze algorithmically the performances of Acdeq in terms of its response time and processor waste for an individual job as well as makespan and total response time for a set of jobs. To the best of our knowledge, Acdeq is the first multiprocessor scheduling algorithm that offers both control-theoretic and algorithmic guarantees. We further evaluate Acdeq via simulations by using Downey's parallel job model augmented with internal parallelism variations. The results confirm its improved performances over Agdeq, and they show that Acdeq excels especially when the scheduling overhead becomes high.