International Journal of Innovative Technology and Research
Abstract
A well-balanced graph partition with small edge cut ratio is usually preferred because it cuts down on the costly network communication cost. However, based on an empirical study Graph, the performance over well partitioned graph may be even two occasions worse than simple random partitions. Graph partition quality affects the general performance of parallel graph computation systems. PAGE, means Partition Aware Graph computation Engine, is made to support different graph partition characteristics and keep high end by an adaptively tuning mechanism and new cooperation techniques. The computing graph is partitioned and distributive stored among workers’ memory. The caliber of a graph partition is measured through the balance factor and edge cut ratio. It is because scalping strategies only optimize for that simple partition methods and can't efficiently handle the growing workload of local message processing when a top quality graph partition can be used. Within this paper, we advise a manuscript partition aware graph computation engine named PAGE, which equips a brand new message processor along with a dynamic concurrency control model. The dynamic model adaptively changes the concurrency from the processor in line with the online statistics. The experimental evaluation demonstrates the brilliance of PAGE within the graph partitions with assorted characteristics. The brand new message processor concurrently processes local and remote messages inside a unified way
