Analysis of SSD Utilization by Graph Processing Systems

Abstract

Graph Processing Systems are highly productive when it comes to graph data. While using data parallel approach, it could not exploit common characteristics of a graph computation workload. To address all these challenges, distributed graph processing frameworks were introduced which inherited both the properties of graph parallel systems and data parallel system. Usually, the standard operators which were being used by data parallel systems were filter, join, reduce and etc. while graph parallel system introduced operators such as sub-graph, mrTriplets and etc. In comparison with graph framework operators, the standard relational operators were too slow. Traditionally, all the frameworks and their benchmarks were executed over hard disk drive but modern storage technology has evolved which lead us to use Solid State Drives. Solid state drives are known for their lightning speed as it manages to retrieve and populate data using pulse. This paper presents an analysis of SSD by utilizing graph processing systems. It also discuss the pros and cons faced by the Graph Processing Frameworks and by using TRIM support how the issue of wear leveling can be resolved.