茅台2.0:面向视图并行编程中的数据竞争预防

2009 International Conference on Parallel and Distributed Computing, Applications and Technologies Pub Date : 2009-12-08 DOI:10.1109/PDCAT.2009.12

K. Leung, Zhiyi Huang, Qihang Huang, P. Werstein

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引用次数: 7

摘要

针对面向视图并行编程(VOPP)模型中的数据竞争问题，提出了一种数据竞争预防方案。VOPP是一种新颖的以共享内存数据为中心的并行编程模型，它使用视图将互斥与数据访问捆绑在一起。我们用内存保护机制实现了数据竞争预防方案。实验结果表明，在我们的应用程序中，内存保护的额外开销是微不足道的。我们还提出了一个新的VOPP实现——茅台2.0，它具有高级功能，如死锁避免、生产者/消费者视图和系统队列，以及数据竞争预防方案。对茅台2.0的性能进行了评价，并与OpenMP、Cilk等现代编程模型进行了比较。

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

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.

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2009 International Conference on Parallel and Distributed Computing, Applications and Technologies

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