An improvement of OpenMP pipeline parallelism with the BatchQueue algorithm

In the context of multicore programming, pipeline parallelism is a solution to easily transform a sequential program into a parallel one without requiring a whole rewriting of the code. The OpenMP stream-computing extension presented by Pop and Cohen proposes an extension of OpenMP to handle pipeline parallelism. However, their communication algorithm relies on Multiple-producer-Multiple- Consumer queues, while pipelined applications mostly deal with linear chains of communication, i.e., with only a single producer and a single consumer. To improve the performance of the OpenMP streamextension, we propose to add a more specialized Single- Producer-Single-Consumer communication algorithm called BatchQueue and to select it for one-to-one communication. Our evaluation shows that BatchQueue is then able to improve the throughput up to a factor 2 on an 8-core machine both for example application and real applications. Our study shows therefore that using specialized and efficient communication algorithms can have a significant impact on the overall performance of pipelined applications.