Supporting MPI-distributed stream parallel patterns in GrPPI

Abstract

In the recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GrPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports the distributed and hybrid (distributed and shared-memory) parallel execution of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GrPPI shared-memory one. A detailed analysis of the GrPPI MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation on a streaming application with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GrPPI overheads.