A Study of Adaptive Co-scheduling Approach for an Opportunistic Software Environment to Execute in Multi-core and Multi-Processor Configurations

Multi-cores and multi-processors loosely coupled are being considered interesting architectures to increase computational power in many organizations. However, these environments brought a new challenge for the scientific community that can be understood as the limited number of applications developed to take advantage of these new configurations. Opportunistic software environments also need to adapt to these new configurations, otherwise it is not expected that these software environments be able to use efficient idle capacity from these architectures. In this paper, it is presented an empirical study of an adaptive coscheduling approach, using an opportunistic package to gather resources from an environment compound by multi-core and multi-processor loosely coupled elements. The proposal approach adopts multiple threads to use idle processing from multi-core and multi-processor resources. Empirical tests proved that the proposal was successful in gathering idle resources from these configurations, enhancing the performance of a demonstration application.

[1]  Peter C. J. Graham,et al.  On the Programming Impact ofMulti-Core,Multi-Processor Nodes inMPI Clusters , 2007, 21st International Symposium on High Performance Computing Systems and Applications (HPCS'07).

[2]  Ronald L. Rivest,et al.  The RC5 Encryption Algorithm , 1994, FSE.

[3]  David P. Anderson,et al.  Local Scheduling for Volunteer Computing , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[4]  Gage Js,et al.  The great Internet Mersenne prime search. , 1998 .

[5]  H. C. Anderson Frank Jenkins Elected Division VII Director , 1982 .

[6]  Volker Strumpen Parallel Molecular Sequence Analysis on Workstations in the Internet , 1993 .

[7]  Zvi M. Kedem,et al.  Charlotte: Metacomputing on the Web , 1999, Future Gener. Comput. Syst..

[8]  José A. B. Fortes HCW panel: programming heterogeneous systems - Less pain! Better performance! , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[9]  Hesham El-Rewini,et al.  Distributed and Parallel Computing , 1998 .

[10]  Hossein Pourreza,et al.  On the Programming Impact of Multi-Core, Multi-Processor Nodes in MPI Clusters , 2007 .

[11]  Sandhya Dwarkadas,et al.  Compatible phase co-scheduling on a CMP of multi-threaded processors , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[12]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[13]  Amjad Umar Distributed And Parallel Computing , 1998, IEEE Concurrency.

[14]  N. Nisan,et al.  Globally distributed computation over the Internet-the POPCORN project , 1998, Proceedings. 18th International Conference on Distributed Computing Systems (Cat. No.98CB36183).

[15]  Mario A. R. Dantas,et al.  The ATHA Environment: Experience with a User Friendly Environment for Opportunistic Computing , 2004, HPCS.

[16]  Zarka Cvetanovic,et al.  The Effects of Problem Partitioning, Allocation, and Granularity on the Performance of Multiple-Processor Systems , 1987, IEEE Transactions on Computers.