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"Progress in High-Performance Computing in general, and High-Performance Graph\nProcessing in particular, is highly dependent on the availability of\npublicly-accessible, relevant, and realistic data sets.\n  To ensure continuation of this progress, we (i) investigate and optimize the\nprocess of generating large sequence similarity graphs as an HPC challenge and\n(ii) demonstrate this process in creating MS-BioGraphs, a new family of\npublicly available real-world edge-weighted graph datasets with up to 2.5\ntrillion edges, that is, 6.6 times greater than the largest graph published\nrecently. The largest graph is created by matching (i.e., all-to-all similarity\naligning) 1.7 billion protein sequences. The MS-BioGraphs family includes\nalso seven subgraphs with different sizes and direction types.\n  We describe two main challenges we faced in generating large graph datasets\nand our solutions, that are, (i) optimizing data structures and algorithms for\nthis multi-step process and (ii) WebGraph parallel compression technique. We\npresent a comparative study of structural characteristics of MS-BioGraphs.\n  The datasets are available online on\nhttps://blogs.qub.ac.uk/DIPSA/MS-BioGraphs ."
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