Achieving Multi-Level Parallelism in the Filter-Labeled Stream Programming Model

Abstract

New architectural trends in chip design resulted in machines with multiple processing units as well as efficient communication networks, leading to the wide availability of systems that provide multiple levels of parallelism, both inter- and intra-machine. Developing applications that efficiently make use of such systems is a challenge, specially for application-domain programmers. In this paper we present a new version of the Anthill programming environment that efficiently exploits multi-level parallelism and experimental results that demonstrate such efficiency. Anthill is based on the filter-stream model; in this model, applications are decomposed into a set of filters communicating through streams, which has already been shown to be efficient for expressing inter-machine parallelism. We replaced the filter run-time environment, originally process-oriented, with an event-oriented version. This new version allow programmers to efficiently express opportunities for parallelism within each compute node through a higher-level programming abstraction. We evaluated our solution on dual- and quad-core machines with two data mining applications: Eclat and KNN. Both had drops in execution time nearly proportional to the number of cores on a single machine. When using a cluster of dual-core machines, speed-ups were close to linear on the number of available cores for both applications, confirming event-oriented Anthill performs well both on the inter- and intra-machine parallelism levels.