DFS: A Simple to Write Yet Difficult to Execute Benchmark

Abstract

Many emerging applications are built upon large, unstructured datasets that exhibit highly irregular (or even nearly random) memory access patterns. Examples include informatics applications, and other problems that are often represented by unstructured graph-based data structures. It is well known that these applications are challenging for conventional architectures to execute (either serially or in parallel). The depth first search (DFS) benchmark proposed in this work uses the boost graph library to perform a depth-first search on a large power-law graph, representing "small world" phenomena. The graph in question exhibits a small average distance between any two vertices, a small diameter, and has a few high-degree vertices with a large number of low-degree vertices. Graphs such as this appear in many fields, including networking, biology, social networks, and data mining. Many of these applications are of critical importance to researchers, and the challenge of executing them on conventional machines increases as the graph size grows. The benchmark proposed in this work is used as the basis for many fundamental algorithms in graph theory, is critical to several emerging applications, is memory intensive, and exhibits poor performance on conventional machines. Section 2 quantitatively demonstrates the memory characteristics of the benchmark in an architecture independent fashion, showing that it is extremely memory intensive. Section 3 describes the execution phases of the benchmark. And section 4 presents the conclusions