PhD Thesis: View-Oriented Parallel Programming and its Performance Evaluation on Multicore Architectures

Shared-memory multicore architectures have become pervasive, and there is a pressing need for parallel programming models to facilitate both performance and convenience. However, most existing shared-memory programming models are tedious for programming and are prone to errors such as data race, which are difficult to debug. To solve this problem, this thesis proposes a data race prevention scheme in the View-Oriented Parallel Programming (VOPP) paradigm. VOPP was proposed for distributed shared memory systems. It is adapted to shared-memory multicore architectures in this thesis. VOPP is a sharedmemory data-centric parallel programming model, which uses views to bundle mutual exclusion with data access. In VOPP, programmers partition the shared memory into “views”, which are non-overlapping sets of shared data objects. The data race prevention scheme proposed for VOPP can prevent data race through the memory protection mechanism while keeping the extra overhead low. To improve the programmability of VOPP, this thesis proposes an automatic view access management scheme where a view is automatically acquired upon its first access, and automatically released when no longer needed, thus relieving programmers from arranging locks to protect critical sections. To further improve performance and programmability, this thesis proposes the View-Oriented Transactional Memory (VOTM) system, which uses Restricted Admission Control (RAC) to manage the number of processes holding each view according to its contention. In VOTM, RAC can restrict the number of processes holding the view when its contention is high, and in