IEEE Transactions on Parallel and Distributed Systems

TPDS Seeks Editor-in-Chief for 2018-2020 Term

The IEEE Computer Society seeks applicants for the position of editor-in-chief of the IEEE Transactions on Parallel and Distributed Systems, serving a three-year term starting 1 January 2018. Prospective candidates are asked to provide a complete curriculum vitae, a brief plan for the publication's future, and a letter of support from their institution or employer (as PDF files) by 1 March 2017. For more information on the search process and to submit application materials, click here or please contact: Kimberly Sperka,

IEEE Transactions on Parallel and Distributed Systems (TPDS) is a scholarly archival journal published monthly. Parallelism and distributed computing are foundational research and technology to rapidly advance computer systems and their applications. Read the full scope of TPDS

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From the March 2017 Issue

Model-Based Optimization of EULAG Kernel on Intel Xeon Phi Through Load Imbalancing

By Alexey Lastovetsky, Lukasz Szustak, and Roman Wyrzykowski

Free Featured ArticleLoad balancing is a widely accepted technique for performance optimization of scientific applications on parallel architectures. Indeed, balanced applications do not waste processor cycles on waiting at points of synchronization and data exchange, maximizing this way the utilization of processors. In this paper, we challenge the universality of the load-balancing approach to optimization of the performance of parallel applications. First, we formulate conditions that should be satisfied by the performance profile of an application in order for the application to achieve its best performance via load balancing. Then we use a real-life scientific application, EULAG MPDATA kernel, to demonstrate that its performance profile on a modern parallel architecture, Intel Xeon Phi, significantly deviates from these conditions. Based on this observation, we propose a method of performance optimization of scientific applications through load imbalancing. In the case of data parallel application, the method uses functional performance models of the application to find partitioning that minimizes its computation time but not necessarily balances the load of processors. We apply this method to optimization of MPDATA on Intel Xeon Phi. Experimental results demonstrate that the performance of this carefully optimized load-balanced application can be further improved by 15percent using the proposed load-imbalancing technique.

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Editorials and Announcements


  • According to Thomson Reuters' 2013 Journal Citation Report, TPDS has an impact factor of 2.173.

  • TPDS celebrates its 25th Anniversary. Editor-in-Chief David A. Bader says, "Congratulations to TPDS on its Silver Jubilee! For 25 years, TPDS has been the parallel and distributed computing community's flagship journal for research breakthroughs!"

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