Graph Analytics on Jellyfish topology

Because large unstructured datasets are important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the "best" network topologies for irregular communication, unstructured graph-based interconnects can be more suitable, due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.In addition to two common stencil-based mini-applications (LULESH and Sweep3D), we analyze three popular graph workloads – clustering, pattern enumeration (triangle counting), and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, considering relevant network routing schemes. Using packet-level simulations, we report average improvements of about 4–20% and 3–26% between equivalent Jellyfish vs. Dragonfly and Jellyfish vs. Fat tree topologies across diverse input graphs and applications.