Whereas the literature describes an increasing number of graph algorithms, loading graphs remains a time-consuming component of the end-to-end execution time. Graph frameworks often rely on custom graph storage formats, that are not optimized for efficient loading of large-scale graph datasets. Furthermore, graph loading is often not optimized as it is time-consuming to implement. This shows a demand for high-performance libraries capable of efficiently loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions across a wide range of graph datasets, and (iii) to facilitate easy and fast comparisons across different graph frameworks. We present ParaGrapher, a library for loading large-scale compressed graphs in parallel and distributed graph frameworks. ParaGrapher supports (a) loading the graph while the caller is blocked and (b) interleaving graph loading with graph processing. ParaGrapher is designed to support loading graphs in shared-memory, distributed-memory, and out-of-core graph processing. We explain the design of ParaGrapher and present a performance model of graph decompression. Our evaluation shows that ParaGrapher delivers up to 3.2 times speedup in loading and up to 5.2 times speedup in end-to-end execution (i.e., through interleaved loading and execution).

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.