Microbenchmark results demonstrate the RMA performance of GASNet-EX is competitive with several MPI-3 implementations on current HPC systems. We describe and evaluate several features and enhancements that have been introduced to address the needs of modern client systems. The library is an evolution of the popular GASNet communication system, more » building upon over 15 years of lessons learned. Figure 2: Illustration of a 3-d upc memput strided (Design B). GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in future exascale machines. Installing Berkeley UPC++, UPC, and GASNet. Architectural trends in supercomputing make such programming models increasingly attractive, and newer, more sophisticated models such as UPC++, Legion and Chapel that rely upon similar communication paradigms are gaining popularity. Partitioned Global Address Space (PGAS) models, typified by such languages as Unified Parallel C (UPC) and Co-Array Fortran, expose one-sided communication as a key building block for High Performance Computing (HPC) applications. Micro-benchmark Performance upcmemput UPC memput operation latency and bandwidth micro benchmark Latency reduced by 80 compared to single endpoint multi-thread design. Liu, Y.Y., Wang, S.: A scalable parallel genetic algorithm for the generalized Assignment Problem. doi: 10.1109/41.538609Ĭhu, P.C., Beasley, J.E.: A genetic algorithm for the generalised assignment problem. Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: Concepts and applications. In: 4th Conference on Partitioned Global Address Space Programming Model (PGAS 2010), pp. IEEE Computer Society, Washington (2011). In: 17th International Conference on Parallel and Distributed Systems (ICPADS 2011), pp. Teijeiro, C., Taboada, G.L., Tourino, J., Doallo, R.: Design and implementation of Mapreduce using the PGAS programming model with UPC. (Neither thread needs to be the calling thread.) 5 Questions from Previous Lecture Q2: What memory model is assumed for UPC non-collective library functions A: Relaxed. Technical report, IDA Center for Computing Sciences (1999) The function upcmemcpy( dst, src, n) copies n bytes from a shared object (src) with affinity to one thread, to a shared object (dst) with affinity to the same or another thread. USENIX Association, San Francisco (2004)Ĭarlson, W.W., Draper, J.M., Culler, D.E., Yelick, K., Brooks, E., Warren, K.: Introduction to UPC and language specification. In: Sixth Symposium on Operating System Design and Implementation (OSDI2004), p. Results of evaluation of Mapreduce on UPC framework based on WordCount benchmark application are presented and compared to Apache Hadoop implementation.ĭean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. The framework also allows to express data parallel applications using simple sequential code.Īdditionally, we present a heuristic approach based on genetic algorithm that could efficiently perform load balancing optimization to distribute key/values among threads such that we minimize data movement operations and evenly distribute computational workload. Shared hashmap is used in to perform exchange of key/values between parallel UPC threads during shuffle phase of Mapreduce framework. 1 shows the difference in the amount of memory usage of NPB3.3 programs when MPI, OpenMP, and UPC versions of the code are used. MEMORY USAGE OF DIFFERENT PROGRAMMING MODELS Fig. In this paper we introduce a novel UPC implementation of Mapreduce technology based on idea of using purely UPC based implementation of shared hashmap data structure as an intermediate key/value store. For UPC, the berkeley UPC compiler and runtime system are used. These implementations present a new viewpoint when Mapreduce application developers can benefit from using global address space model while writing data parallel tasks. Out of other notable implementations one should mention recent PGAS (partitioned global address space) – based X10, UPC (Unified Parallel C) versions. One of the most prevalent implementations of Mapreduce is Hadoop framework and Google proprietary Mapreduce system. UPC collective prim-itives, which are part of the UPC standard, increase pro-gramming productivity while reducing the communication overhead. Over the years from its introduction Mapreduce technology proved to be very effective parallel programming technique to process large volumes of data. Unied Parallel C (UPC) is an extension of ANSI C designed for parallel programming.
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