Increasingly larger amounts of data are produced from different sources and in different fields of research, science, humanities and industry: telescopes and interferometers in radio astronomy, cameras and sensors of smart phones and security and healthcare systems, interactions between proteins in biology, purchases and ratings of products in e-commerce websites, posts and connections between users in social networks, … . However, these raw data are only beneficial after being analyzed by high performance software systems to extract special information and patterns. Our group studies algorithms and techniques to enhance high performance graph processing systems:

  • 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.
  • 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.
  • 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.
  • Transprecision [2020 – ongoing] adapts the precision (detail) in the computation in order to increase processing speed and/or reduce energy consumption. One simple way to reduce precision is to use reduced-width floating-point number representations. This allows more data to be retained in on-chip caches and reduced memory pressure. Care must be taken to retain accuracy of the computation.
  • LaganLighter [2019 – ongoing]: 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 investigates high performance graph processing from the graph datasets locality perspective.