Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

Citations Over TimeTop 1% of 2011 papers

Abstract

Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental property of randomly shaped real-world graph instances. By utilizing memory bandwidth more efficiently, our method shows improved performance over the current state-of-the-art implementation and increases its advantage as the size of the graph increases. We then propose a hybrid method which, for each level of the BFS algorithm, dynamically chooses the best implementation from: a sequential execution, two different methods of multicore execution, and a GPU execution. Such a hybrid approach provides the best performance for each graph size while avoiding poor worst-case performance on high-diameter graphs. Finally, we study the effects of the underlying architecture on BFS performance by comparing multiple CPU and GPU systems, a high-end GPU system performed as well as a quad-socket high-end CPU system.

Related Papers

• → Parallelization Strategies for Mixed Regular-Irregular Applications on Multicore-Systems(2009)6 cited
• → Parallelization of the Fast Multipole Method for Molecular Dynamics Simulations on Multicore Computers(2013)1 cited
• → Comparison of serial and parallel searching in multicore systems(2014)1 cited
• → Accelerating the Production of Synthetic Seismograms by a Multicore Processor Cluster with Multiple GPUs(2012)1 cited
• → Parallel Large-Scale Image Processing for Orthorectification(2018)1 cited