Workload Balancing via Graph Reordering on Multicore Systems

In a shared-memory multicore system, the intrinsic irregular data structure of graphs leads to poor cache utilization, and therefore deteriorates the performance of graph analytics. To address the problem, prior works have proposed a variety of lightweight reordering methods with focus on the optimization of cache locality. However, there is a compromise between cache locality and workload balance. Little insight has been devoted into the issue of workload imbalance for the underlying multicore system, which degrades the effectiveness of parallel graph processing. In this work, a measurement approach is proposed to quantify the imbalance incurred by the concentration of vertices. Inspired by it, we present
Cache-aware Reorder (Corder)
, a lightweight reordering method exploiting the cache hierarchy of multicore systems. At the shared-memory level, Corder promotes even distribution of computation loads amongst multicores. At the private-cache level, Corder facilitates cache efficiency by applying further refinement to local vertex order. Comprehensive performance evaluation of Corder is conducted on various graph applications and datasets. Experimental results show that Corder yields speedup of up to
$2.59\times$
and on average
$1.45\times$
, which significantly outperforms existing lightweight reordering methods. To identify the root causes of performance boost delivered by Corder, multicore activities are investigated in terms of thread behavior, cache efficiency, and memory utilization. Statistical analysis demonstrates that the issue of imbalanced thread execution time dominates other factors in determining the overall graph processing time. Moreover, Corder achieves remarkable advantages in cross-platform scalability and reordering overhead.