On Overcoming HPC Challenges of Trillion-Scale Real-World Graph Datasets.

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