Maotai 2.0: Data Race Prevention in View-Oriented Parallel Programming

Abstract

This paper proposes a data race prevention scheme, which can prevent data races in the View-Oriented Parallel Programming (VOPP) model. VOPP is a novel shared-memory data-centric parallel programming model, which uses views to bundle mutual exclusion with data access. We have implemented the data race prevention scheme with a memory protection mechanism. Experimental results show that the extra overhead of memory protection is trivial in our applications. We also present a new VOPP implementation--Maotai 2.0, which has advanced features such as deadlock avoidance, producer/consumer view and system queues, in addition to the data race prevention scheme. The performance of Maotai 2.0 is evaluated and compared with modern programming models such as OpenMP and Cilk.