Performance Evaluation of Unified Parallel C Collective Communications

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/HPCC.2009.88[Abstract] Unified Parallel C (UPC) is an extension of ANSI C designed for parallel programming. UPC collective primitives, which are part of the UPC standard, increase programming productivity while reducing the communication overhead. This paper presents an up-to-date performance evaluation of two publicly available UPC collective implementations on three scenarios: shared, distributed, and hybrid shared/distributed memory architectures. The characterization of the throughput of collective primitives is useful for increasing performance through the runtime selection of the appropriate primitive implementation, which depends on the message size and the memory architecture, as well as to detect inefficient implementations. In fact, based on the analysis of the UPC collectives performance, we proposed some optimizations for the current UPC collective libraries. We have also compared the performance of the UPC collective primitives and their MPI counterparts, showing that there is room for improvement. Finally, this paper concludes with an analysis of the influence of the performance of the UPC collectives on a representative communication-intensive application, showing that their optimization is highly important for UPC scalability.Ministerio de Ciencia e Innovación; TIN2007-67537-C03-02Xunta de Galicia; 3/2006 DOGA 13/12/200

DART-MPI: An MPI-based Implementation of a PGAS Runtime System

A Partitioned Global Address Space (PGAS) approach treats a distributed system as if the memory were shared on a global level. Given such a global view on memory, the user may program applications very much like shared memory systems. This greatly simplifies the tasks of developing parallel applications, because no explicit communication has to be specified in the program for data exchange between different computing nodes. In this paper we present DART, a runtime environment, which implements the PGAS paradigm on large-scale high-performance computing clusters. A specific feature of our implementation is the use of one-sided communication of the Message Passing Interface (MPI) version 3 (i.e. MPI-3) as the underlying communication substrate. We evaluated the performance of the implementation with several low-level kernels in order to determine overheads and limitations in comparison to the underlying MPI-3.Comment: 11 pages, International Conference on Partitioned Global Address Space Programming Models (PGAS14