In today's data-driven world and heterogeneous computing environments, processing large-scale graphs in an architecture agnostic manner has become more crucial than ever before. In terms of graph analytics frameworks, on the one side, there has been a significant interest in developing hand-optimized high-performance computing solutions. On the systems side, following the big data movement and to bring parallel computing to the masses, researchers have proposed several graph processing and management systems to handle large-scale graphs. Hand optimized HPC approaches require high expertise and are expensive to maintain and develop, and graph processing frameworks suffer from limited expressibility and performance. We propose Parallel Graph Algorithms by Blocks (PGAbB), a block-based graph algorithms framework for shared-memory, multi-core, multi-GPU machines. PGAbB offers a sweet spot between efficient parallelism and architecture agnostic algorithm design for a wide class of graph problems while performing close to hand-optimized HPC implementations.

Modern graphs are not only large, but rapidly changing. The rate of change can vary significantly along with the computational cost. Existing distributed graph analysis systems have largely been designed to operate on static graphs. Infrastructure changes in these systems need to occur when the system is idle, which can result in significant wasted resources or the inability to cope with changes.

Finding dense regions of graphs is fundamental in graph mining. We focus on the computation of dense hierarchies and regions with graph nuclei---a generalization of k-cores and trusses. Static computation of nuclei, namely through variants of 'peeling', are easy to understand and implement. However, many practically important graphs undergo continuous change. Dynamic algorithms, maintaining nucleus computations on dynamic graph streams, are nuanced and require significant effort to port between nuclei, e.g., from k-cores to trusses.