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Graph construction from a given set of edges is a data-intensive operatorthat appears in social network analysis, ontology enabled databases, and, otheranalytics processing. The operator represents an edge list to compressed sparserow (CSR) representation (or sometimes in adjacency list, or as clusteredB-Tree storage). In this work, we show how to scale CSR construction to massivescale on SSD-enabled supercomputers such as Gordon using pipelined processing.We develop several abstraction and operations for external memory and paralleledge list and integer array processing that are utilized towards building ascalable algorithm for creating CSR representation.  Our experiments demonstrate that this scheme is four to six times faster thancurrently available implementation. Moreover, our scheme can handle up to 8billion edges (128GB) by using external memory as compared to prior schemeswhere performance degrades considerably for edge list size 26 million andbeyond.

Pipelined Workflow in Hybrid MPI/Pthread runtime for External Memory Graph Construction