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Past decade has seen the development of many shared-memory graph processingframeworks, intended to reduce the effort of developing high performanceparallel applications. However many of these frameworks, based onVertex-centric or Edge-centric paradigms suffer from several issues, such aspoor cache utilization, irregular memory accesses, heavy use of synchronizationprimitives and theoretical inefficiency, that deteriorate overall performanceand scalability.  Recently, we proposed a cache and memory efficient partition-centric paradigmfor computing PageRank. In this paper, we generalize this approach to develop anovel Graph Processing Over Partitions (GPOP) framework that iscache-efficient, scalable and work-efficient. GPOP induces locality in memoryaccesses by increasing granularity of execution to vertex subsets called'partitions', thereby dramatically improving the cache performance of a varietyof graph algorithms. It achieves high scalability by enabling completely lockand atomic free computation. GPOP's built-in analytical performance modelenables it to use a hybrid of source and partitioncentric communication modesin a way that ensures work-efficiency each iteration, while simultaneouslyboosting high bandwidth sequential memory accesses.  We extensively evaluate the performance of GPOP for a variety of graphalgorithms, using several large datasets. We observe that GPOP incurs up to 9x,6.8x and 5.5x less L2 cache misses compared to Ligra, GraphMat and Galois,respectively. In terms of execution time, GPOP is upto 19x, 9.3x and 3.6xfaster than Ligra, GraphMat and Galois respectively.

GPOP: A cache- and work-efficient framework for Graph Processing Over Partitions