
MapReduce is widely used for BigData processing. It was originally designed to overcome the I/O bottleneck of commodity servers. However, several high speed storage and network devices have recently emerged, and speeds continue to increase. Employing such brand new devices will solve the I/O bottleneck, making the CPU the next serious bottleneck in the MapReduce framework. In this paper, we conduct a performance study of Hadoop MapReduce by using a server cluster built on state-of-the-art devices. We show the CPU is the bottleneck in such an environment. To overcome the CPU bottleneck, we propose hardware acceleration for MapReduce. We implement a prototype using a many core processor board developed by Tilera and show the feasibility of our proposal.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 20 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
