Graph analytics frameworks, typically based on Vertex-centric or Edge-centric paradigms suffer from poor cache utilization, irregular memory accesses, heavy use of synchronization primitives or theoretical inefficiency, that deteriorate over-all performance and scalability.

In this paper, we generalize a recent partition-centric PageRank computation approach to develop a novel Graph Processing Over Partitions (GPOP) framework that enables cache-efficient, work-efficient and scalable implementations of several graph algorithms. For large graphs, we observe that GPOP is upto 19x and 6.1x faster than Ligra and GraphMat, respectively.