Servet: A Benchmark Suite for Autotuning on Multicore Clusters

This is a post-peer-review, pre-copyedit version of an article published in 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS). Proceedings. The final authenticated version is available online at: http://dx.doi.org/10.1109/IPDPS.2010.5470358.[Abstract] MapReduce is a powerful tool for processing large data sets used by many applications running in distributed environments. However, despite the increasing number of computationally intensive problems that require low-latency communications, the adoption of MapReduce in High Performance Computing (HPC) is still emerging. Here languages based on the Partitioned Global Address Space (PGAS) programming model have shown to be a good choice for implementing parallel applications, in order to take advantage of the increasing number of cores per node and the programmability benefits achieved by their global memory view, such as the transparent access to remote data. This paper presents the first PGAS-based MapReduce implementation that uses the Unified Parallel C (UPC) language, which (1) obtains programmability benefits in parallel programming, (2) offers advanced configuration options to define a customized load distribution for different codes, and (3) overcomes performance penalties and bottlenecks that have traditionally prevented the deployment of MapReduce applications in HPC. The performance evaluation of representative applications on shared and distributed memory environments assesses the scalability of the presented MapReduce framework, confirming its suitability.Xunta de Galicia; INCITE08PXIB105161PRMinisterio de Ciencia e Innovación; TIN2007-67537-C03-02Ministerio de Educación; FPU; AP2008-0157