Understanding parallelism in graph traversal on multi-core clusters

  • Authors:
  • Huiwei Lv;Guangming Tan;Mingyu Chen;Ninghui Sun

  • Affiliations:
  • State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate School of Chinese Academy of Sciences, Beijing, China;State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Computer Science - Research and Development
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

There is an ever-increasing need for exploring large-scale graph data sets in computational sciences, social networks, and business analytics. However, due to irregular and memory-intensive nature, graph applications are notoriously known for their poor performance on parallel computer systems. In this paper we propose a new hybrid MPI/Pthreads breadth-first search (BFS) algorithm featuring with (i) overlapping computation and communication by separating them into multiple threads, (ii) maximizing multi-threading parallelism on multi-cores with massive threads to improve throughputs, and (iii) exploiting pipeline parallelism using lock-free queues for asynchronous communication. By comparing it with traditional MPI-only BFS algorithm, we learned several valuable lessons that would help to understand and exploit parallelism in graph traversal applications. Experiments show our algorithm is 1.9脳 faster than the MPI-only version, capable of processing 1.45 billion edges per second on a 32-node SMP cluster. At a large scale, our algorithm is 1.49脳 than the MPI-only BFS algorithm in Combinatorial BLAS Library with 6,144 cores.