GPOP: A cache- and work-efficient framework for Graph Processing Over Partitions
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The past decade has seen development of many shared-memory graph processing frameworks intended to reduce the effort for creating high performance parallel applications. However, their programming models, based on Vertex-centric or Edge-centric paradigms suffer from several issues, such as poor cache utilization, irregular memory accesses, heavy use of synchronization primitives or theoretical inefficiency, that deteriorate the performance and scalability of applications. Recently, a cache-efficient partition-centric paradigm was proposed for computing PageRank. In this paper, we generalize this approach to develop a novel Partition-centric Programming Model(PPM) that is cache-efficient, scalable and work-efficient. We implement PPM as part of Graph Processing over Partitions(GPOP) framework that can efficiently execute a variety of algorithms. GPOP dramatically improves the cache performance by exploiting locality of partitioning. It achieves high scalability by enabling completely lock and atomic free computation. Its built-in analytical performance models enable it to use a hybrid of source and destination centric communication modes in a way that ensures work-efficiency of each iteration and simultaneously boosts high bandwidth sequential memory accesses. GPOP framework completely abstracts away underlying parallelism and programming model details from the user. It provides an easy to program set of APIs with the ability to selectively continue the active vertex set across iterations, which is not intrinsically supported by the current frameworks. We extensively evaluate the performance of GPOP for a variety of graph algorithms, using several large datasets. We observe that GPOP incurs upto 8.6x and 5.2x less L2 cache misses compared to Ligra and GraphMat, respectively. In terms of execution time, GPOP is upto 19x and 6.1x faster than Ligra and GraphMat, respectively.
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