Evaluating the suitability of mapreduce for surface temperature analysis codes
Proceedings of the second international workshop on Data intensive computing in the clouds
Poster: SSS: a mapreduce framework based on distributed key-value store
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
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MapReduce has been very successful in implementing large-scale data-intensive applications. Because of its simple programming model, MapReduce has also begun being utilized as a programming tool for more general distributed and parallel applications, e.g., HPC applications. However, its applicability is limited due to relatively inefficient runtime performance and hence insufficient support for flexible workflow. In particular, the performance problem is not negligible in iterative MapReduce applications. On the other hand, today, HPC community is going to be able to utilize very fast and energy-efficient Solid State Drives (SSDs) with 10 Gbit/sec-class read/write performance. This fact leads us to the possibility to develop ``High-Performance MapReduce'', so called. From this perspective, we have been developing a new MapReduce framework called ``SSS'' based on distributed key-value store (KVS). In this paper, we first discuss the limitations of existing MapReduce implementations and present the design and implementation of SSS. Although our implementation of SSS is still in a prototype stage, we conduct two benchmarks for comparing the performance of SSS and Hadoop. The results indicate that SSS performs 1-10 times faster than Hadoop.