High-Performance Graph Processing

Graph Processing for Precision Medicine [2021 – ongoing]

– Graph Isomorphism [2021 – ongoing]
(Syed)

Graptor [2019 – ongoing] enhances GraphGrind and VEBO by providing Single Instruction Multiple Data (SIMD) vectorization for graph analytics using the SSE, AVX2 and AVX-512 instruction sets families.

LaganLighter [2019 – 2022]: Having the experience of GraphGrind and VEBO, we understood the requirement of re-studying graph algorithms based on the implications imposed by the structure of datasets into the execution of graph analytics. LaganLighter introduces structure-aware high-performance graph algorithms.

VEBO [2017 – 2018] provides load balancing and better CPU utilization by applying a new graph relabeling and partitioning algorithm. The assumption behind VEBO is that load balance is a more important consideration for shared-memory graph partitioning than memory locality. VEBO has been implemented for GraphGrind and also for Polymer.

GraphGrind [2014 – 2017] optimizes Ligra and Polymer for systems with Non-Uniform Memory-Architecture (NUMA) by adapting the parallelisation approach (vertices and edges partitioning and scheduling algorithms) to the graph algorithm, by compressing of topology data, and by introducing a new NUMA-aware work-stealing algorithm that extends Cilk.