Job Scheduling Strategies for Parallel Processing

Memory bandwidth is a major resource which is shared among all CPU cores. The development speed of memory bandwidth cannot catch up with the increasing number of CPU cores. Thus, the contention for occupying more memory bandwidth among concurrently executing tasks occurs. In this paper, we have presented Bubble Task method which mitigates memory contention via throttling technique. We made a memory contention modeling for dynamically deciding throttling ratio and implemented both software and hardware versions to present trade-off between fine-grained adjustment and stable fairness. Bubble Task can lead to performance improvement in STREAM benchmark suite which is one of the most memory hungry benchmark by 21 % and fairness in memory bandwidth sharing among SPEC CPU 2006 applications which have different memory access patterns.

[1]  Judy Kay,et al.  A fair share scheduler , 1988, CACM.

[2]  Erik Elmroth,et al.  Integration and Evaluation of Decentralized Fairshare Prioritization (Aequus) , 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

[3]  Mark J. Clement,et al.  Core Algorithms of the Maui Scheduler , 2001, JSSPP.

[4]  John P. Lehoczky,et al.  The rate monotonic scheduling algorithm: exact characterization and average case behavior , 1989, [1989] Proceedings. Real-Time Systems Symposium.

[5]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[6]  Erik Elmroth,et al.  Decentralized Prioritization-Based Management Systems for Distributed Computing , 2013, 2013 IEEE 9th International Conference on e-Science.

[7]  Gonzalo P. Rodrigo.  Proof of compliance for the relative operator on the proportional distribution of unused share in an ordering fairshare system , 2014 .

[8]  Šimon Tóth,et al.  Tools and Methods for Detailed Analysis of Complex JobSchedules in the Czech National Grid , 2013 .

[9]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[10]  Erik Elmroth,et al.  Decentralized scalable fairshare scheduling , 2013, Future Gener. Comput. Syst..

[11]  Erik Elmroth,et al.  Design and evaluation of a decentralized system for grid-wide fairshare scheduling , 2005, First International Conference on e-Science and Grid Computing (e-Science'05).

[12]  Andy B. Yoo,et al.  Approved for Public Release; Further Dissemination Unlimited X-ray Pulse Compression Using Strained Crystals X-ray Pulse Compression Using Strained Crystals , 2002 .

[13]  P. Sadayappan,et al.  Selective Reservation Strategies for Backfill Job Scheduling , 2002, JSSPP.

[14]  Rajesh Raman,et al.  Matchmaking: distributed resource management for high throughput computing , 1998, Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244).

[15]  Petr Holub,et al.  Virtual Clusters as a New Service of MetaCentrum, the Czech NGI , 2010 .

[16]  Miroslav Ruda,et al.  Practical Experiences with torque Meta-Scheduling in the Czech National Grid , 2012, Comput. Sci..

[17]  P. Sadayappan,et al.  Job fairness in non-preemptive job scheduling , 2004 .

[18]  John P. Lehoczky,et al.  Task Scheduling In Distributed Real-Time Systems , 1987, Other Conferences.