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