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Subject: search.filters.subject.multiprocessor systems

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    Scalable Global Power Management Policy Based on Combinatorial Optimization for Multiprocessors
    (2015-12-01)
    Pan, Gung-Yu
    ;
    Yang, Jed
    ;
    Jou, Jing-Yang
    ;
    Lai, Bo-Cheng Charles
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    交大名義發表
    Multiprocessors have become the main architecture trend in modern systems due to the superior performance; nevertheless, the power consumption remains a critical challenge. Global power management (GPM) aims at dynamically finding the power state combination that satisfies the power budget constraint while maximizing the overall performance (or vice versa). Due to the increasing number of cores in a multiprocessor system, the scalability of GPM policies has become critical when searching satisfactory state combinations within acceptable time. This article proposes a highly scalable policy based on combinatorial optimization with theoretical proofs, whereas previous works take exhaustive search or heuristic methods. The proposed policy first applies an optimum algorithm to construct a state combination table in pseudo polynomial time using dynamic programming. Then, the state combination is assigned to cores with minimum transition cost in linear time by mapping to the network flow problem. Simulation results show that the proposed policy achieves better system performance for any given power budget when compared to the state-of-the-art heuristic. Furthermore, the proposed policy demonstrates its prominent scalability with 125 times faster policy runtime for 512 cores.
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    An intelligent parallel loop scheduling for parallelizing compilers
    (2000-03-01)
    Fann, YW
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    Yang, CT
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    Tseng, SS
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    Tsai, CJ
    ;
    資訊工程學系
    In this paper we propose a knowledge-based approach to solving loop-scheduling problems. A rule-based system, called IPLS, is developed by combining a repertory grid and an attribute ordering table to construct a knowledge base. IPLS chooses an appropriate scheduling algorithm by inferring some features of loops and assigning parallel loops to multiprocessors to achieve significant speedup. Because more attributes are proposed, the accuracy of selection of an appropriate scheduling method is improved. In addition, the refined IPLS system can automatically adjust the attributes in the knowledge base according to profile information; therefore, IPLS has the capability of feedback learning. The experimental results show that our approach can achieve greater speedup on multiprocessor systems than can others.