Lifting sequential graph algorithms for distributed-memory parallel computation

Abstract

This paper describes the process used to extend the Boost Graph Library (BGL) for parallel operation with distributed memory. The BGL consists of a rich set of generic graph algorithms and supporting data structures, but it was not originally designed with parallelism in mind. In this paper, we revisit the abstractions comprising the BGL in the context of distributed-memory parallelism, lifting away the implicit requirements of sequential execution and a single shared address space. We illustrate our approach by describing the process as applied to one of the core algorithms in the BGL, breadth-first search. The result is a generic algorithm that is unchanged from the sequential algorithm, requiring only the introduction of external (distributed) data structures for parallel execution. More importantly, the generic implementation retains its interface and semantics, such that other distributed algorithms can be built upon it, just as algorithms are layered in the sequential case. By characterizing these extensions as well as the extension process, we develop general principles and patterns for using (and reusing) generic, object-oriented parallel software libraries. We demonstrate that the resulting algorithm implementations are both efficient and scalable with performance results for several algorithms.