Tiled-MapReduce

Abstract

The prevalence of chip multiprocessors opens opportunities of running data-parallel applications originally in clusters on a single machine with many cores. MapReduce, a simple and elegant programming model to program large-scale clusters, has recently been shown a promising alternative to harness the multicore platform. The differences such as memory hierarchy and communication patterns between clusters and multicore platforms raise new challenges to design and implement an efficient MapReduce system on multicore. This article argues that it is more efficient for MapReduce to iteratively process small chunks of data in turn than processing a large chunk of data at a time on shared memory multicore platforms. Based on the argument, we extend the general MapReduce programming model with a “tiling strategy”, called Tiled - MapReduce (TMR). TMR partitions a large MapReduce job into a number of small subjobs and iteratively processes one subjob at a time with efficient use of resources; TMR finally merges the results of all subjobs for output. Based on Tiled-MapReduce, we design and implement several optimizing techniques targeting multicore, including the reuse of the input buffer among subjobs, a NUCA/NUMA-aware scheduler, and pipelining a subjob’s reduce phase with the successive subjob’s map phase, to optimize the memory, cache, and CPU resources accordingly. Further, we demonstrate that Tiled-MapReduce supports fine-grained fault tolerance and enables several usage scenarios such as online and incremental computing on multicore machines. Performance evaluation with our prototype system called Ostrich on a 48-core machine shows that Ostrich saves up to 87.6% memory, causes less cache misses, and makes more efficient use of CPU cores, resulting in a speedup ranging from 1.86x to 3.07x over Phoenix. Ostrich also efficiently supports fine-grained fault tolerance, online, and incremental computing with small performance penalty.