The current trend to multicore architectures underscores the need of parallelism. While new languages and alternatives for supporting more efficiently these systems are proposed, MPI faces this new challenge. Therefore, up-to-date performance evaluations of current options for programming multicore systems are needed. This paper evaluates MPI performance against Unified Parallel C (UPC) and OpenMP on multicore architectures. From the analysis of the results, it can be concluded that MPI is generally the best choice on multicore systems with both shared and hybrid shared/distributed memory, as it takes the highest advantage of data locality, the key factor for performance in these systems. Regarding UPC, although it exploits efficiently the data layout in memory, it suffers from remote shared memory accesses, whereas OpenMP usually lacks efficient data locality support and is restricted to shared memory systems, which limits its scalability.
Abstract-The study of a language in terms of programmability is a very interesting issue in parallel programming. Traditional approaches in this field have studied different methods, such as the number of Lines of Code or the analysis of programs, in order to prove the benefits of using a paradigm compared to another. Nevertheless, these methods usually focus only on code analysis, without giving much importance to the conditions of the development process and even to the learning stage, or the benefits and disadvantages of the language reported by the programmers. In this paper we present a methodology to accomplish a programmability study with UPC (Unified Parallel C) through the use of classroom studies with a group of novice UPC programmers. This work will show the design of these sessions and the analysis of the results obtained (code analysis and survey responses). Thus, it is possible to characterize the current benefits and disadvantages of UPC, as well as to report some desirable features that could be included in this language standard.
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