4/29/2023 0 Comments Berkeley upc benchmark![]() ![]() MPI is the standard interface for message-passing libraries and there is a wide range of MPI implementations, both from HPC vendors and the free software community, optimized for high-speed networks, such as InfiniBand or Myrinet. Therefore, as the programmer has to manage explicitly data placement through point-to-point or collective operations, the programming of these architectures is difficult. It is the preferred choice for parallel programming distributed memory systems, such as multicore clusters. The message-passing is the most widely used parallel programming model as it is portable, scalable and provides good performance for a wide variety of computing platforms and codes. These approaches are the message-passing paradigm, shared memory programming, and the PGAS programming model. This section presents three of the main options for parallel programming multicore architectures. Parallel Programming for Multicore Architectures. Finally, Section 5 presents the main conclusions of this evaluation. Benchmarking results obtained from two multicore systems are shown and analyzed in Section 4. Section 3 introduces the benchmarks used for the comparative evaluation and related work. This paper is structured as follows: Section 2 presents current trends in multicore parallel programming (MPI, OpenMP and PGAS). A matrix multiplication kernel and the Sobel edge detection kernel, have also been used to assess the scalability of the evaluated parallel programming solutions. This evaluation uses the standard parallel benchmarking suite, the NAS Parallel Benchmarks (NPB). validity of this assessment, this work presents an up-to-date comparative performance evaluation of MPI, UPC and OpenMP on two multicore scenarios: a cluster composed of 16-core nodes interconnected via InfiniBand, and a 128-core shared memory system. These alternatives have been dismissed and MPI is still the preferred platform for HPC developers due to its higher performance and portability. In the past other alternatives, such as High Performance Fortran, aimed to replace MPI as the primary choice for HPC programming. Additionally, the Partitioned Global Address Space (PGAS) languages, such as Unified Parallel C (UPC), are an emerging alternative that allows shared memory-like programming on distributed memory systems, taking advantage of the data locality, being of special interest for hybrid architectures. ![]() These systems are usually programmed using MPI on distributed memory, OpenMP on shared memory, and MPI+OpenMP on hybrid shared/distributed memory architectures. Currently, multicore clusters are the most popular option for the deployment of High Performance Computing (HPC) infrastructures, due to their scalability and performance/cost ratio. Key words: MPI, UPC, OpenMP, Multicore Architectures, Performance Evaluation, NAS Parallel Benchmarks (NPB). ![]() 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. 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. This paper evaluates MPI performance against Unified Parallel C (UPC) and OpenMP on multicore architectures. Therefore, up-to-date performance evaluations of current options for programming multicore systems are needed. While new languages and alternatives for supporting more efficiently these systems are proposed, MPI faces this new challenge. The current trend to multicore architectures underscores the need of parallelism. Galicia Supercomputing Center (CESGA), Santiago de Compostela, Spain Computer Architecture Group, University of A Coruña, A Coruña, Spain Abstract. Fraguela2, Andrés Gómez1, Ramón Doallo2, and J. Taboada2, Carlos Teijeiro2, Juan Touriño2, Basilio B. (1)Performance Evaluation of MPI, UPC and OpenMP on Multicore Architectures Damián A. ![]()
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