  Graph analytics power a range of applications in areas as diverse as finance,
networking and business logistics. A common property of graphs used in the
domain of graph analytics is a power-law distribution of vertex connectivity,
wherein a small number of vertices are responsible for a high fraction of all
connections in the graph. These richly-connected (hot) vertices inherently
exhibit high reuse. However, their sparse distribution in memory leads to a
severe underutilization of on-chip cache capacity. Prior works have proposed
lightweight skew-aware vertex reordering that places hot vertices adjacent to
each other in memory, reducing the cache footprint of hot vertices. However, in
doing so, they may inadvertently destroy the inherent community structure
within the graph, which may negate the performance gains achieved from the
reduced footprint of hot vertices.
  In this work, we study existing reordering techniques and demonstrate the
inherent tension between reducing the cache footprint of hot vertices and
preserving original graph structure. We quantify the potential performance loss
due to disruption in graph structure for different graph datasets. We further
show that reordering techniques that employ fine-grain reordering significantly
increase misses in the higher level caches, even when they reduce misses in the
last-level cache.
  To overcome the limitations of existing reordering techniques, we propose
Degree-Based Grouping (DBG), a novel lightweight reordering technique that
employs a coarse-grain reordering to largely preserve graph structure while
reducing the cache footprint of hot vertices. Our evaluation on 40 combinations
of various graph applications and datasets shows that, compared to a baseline
with no reordering, DBG yields an average application speed-up of 16.8% vs
11.6% for the best-performing existing lightweight technique.
