Adaptive Scheduling Algorithm Based on Dynamic Core-Resource Partitions for Many-Core Processor Systems

With the aim to address the increasing difficulty of efficiently using large number of cores in many-core processors,a core-partitioned adaptive scheduling algorithm,named CASM(core-partitioned adaptive scheduling for many-core systems),is proposed.CASM dynamically aggregates cores into different partitions by splitting or merging task-clusters,which ensures the efficiency of isolated accessing in these core partitions.To improve the scheduling efficiency of CASM,equi-partitioning scheduling algorithm is adopted to reallocate the cores among task-clusters,and the feedback-driven adaptive scheduling algorithm is implemented within the task-clusters.Online competitive analysis shows that CASM achieves 2-competitiveness ratio with respect to the execution time of parallel jobs,which indicates that CASM has better performance and scalability.The experimental results demonstrate that compared with WS(work-stealing),AGDEQ(adaptive greedy dynamic equi-partitioning) and EQUI?EQUI,CASM reduces the execution time of the same workload by nearly 46%,32% and 15% respectively.Under the same power consumption,CASM greatly enhances the system throughput.

[1]  Chita R. Das,et al.  Aérgia: A Network-on-Chip Exploiting Packet Latency Slack , 2011, IEEE Micro.

[2]  Martín Abadi,et al.  Semantics of transactional memory and automatic mutual exclusion , 2011, TOPL.

[3]  Long Guo Architectural Support and Evaluation of Cilk Language on Many-Core Architectures , 2008 .

[4]  Onur Mutlu,et al.  Fairness via source throttling: a configurable and high-performance fairness substrate for multi-core memory systems , 2010, ASPLOS 2010.

[5]  Alexandra Fedorova,et al.  Towards better performance per watt in virtual environments on asymmetric single-ISA multi-core systems , 2009, OPSR.

[6]  Yun Zhang,et al.  Revisiting the Sequential Programming Model for the Multicore Era , 2008, IEEE Micro.

[7]  Eugene Loh The ideal HPC programming language , 2010, Commun. ACM.

[8]  D. Geer,et al.  Chip makers turn to multicore processors , 2005, Computer.

[9]  Matteo Frigo,et al.  The implementation of the Cilk-5 multithreaded language , 1998, PLDI.

[10]  Andrew A. Chien,et al.  The future of microprocessors , 2011, Commun. ACM.

[11]  Julien Robert,et al.  Non-clairvoyant Batch Sets Scheduling: Fairness Is Fair Enough , 2006, ESA.

[12]  Yuxiong He,et al.  Provably Efficient Online Nonclairvoyant Adaptive Scheduling , 2008, IEEE Trans. Parallel Distributed Syst..

[13]  Kirk Pruhs Competitive online scheduling for server systems , 2007, PERV.

[14]  Samuel Thibault,et al.  Scheduling Dynamic OpenMP Applications over Multicore Architectures , 2008, IWOMP.

[15]  Stefan Marr,et al.  Virtual Machine Support for Many-Core Architectures: Decoupling Abstract from Concrete Concurrency Models , 2010, PLACES.

[16]  Margaret Martonosi,et al.  Parallelization libraries: Characterizing and reducing overheads , 2011, TACO.