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首页> 外文期刊>IEEE Transactions on Software Engineering >Clustering algorithm for parallelizing software systems in multiprocessors environment
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Clustering algorithm for parallelizing software systems in multiprocessors environment

机译：多处理器环境中用于并行化软件系统的聚类算法

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摘要

A variety of techniques and tools exist to parallelize software systems on different parallel architectures (SIMD, MIMD). With the advances in high-speed networks, there has been a dramatic increase in the number of client/server applications. A variety of client/server applications are deployed today, ranging from simple telnet sessions to complex electronic commerce transactions. Industry standard protocols, like Secure Socket Layer (SSL), Secure Electronic Transaction (SET), etc., are in use for ensuring privacy and integrity of data, as well as for authenticating the sender and the receiver during message passing. Consequently, a majority of applications using parallel processing techniques are becoming synchronization-centric, i.e., for every message transfer, the sender and receiver must synchronize. However, more effective techniques and tools are needed to automate the clustering of such synchronization-centric applications to extract parallelism. The authors present a new clustering algorithm to facilitate the parallelization of software systems in a multiprocessor environment. The new clustering algorithm achieves traditional clustering objectives (reduction in parallel execution time, communication cost, etc.). Additionally, our approach: 1) reduces the performance degradation caused by synchronizations, and 2) avoids deadlocks during clustering. The effectiveness of our approach is depicted with the help of simulation results.
机译：存在多种技术和工具来并行化不同并行体系结构（SIMD，MIMD）上的软件系统。随着高速网络的进步，客户端/服务器应用程序的数量急剧增加。今天，已经部署了各种客户端/服务器应用程序，从简单的telnet会话到复杂的电子商务交易。使用行业标准协议，例如安全套接字层（SSL），安全电子交易（SET）等，以确保数据的私密性和完整性，并在消息传递过程中对发送方和接收方进行身份验证。因此，大多数使用并行处理技术的应用程序正变得以同步为中心，即，对于每个消息传输，发送方和接收方必须同步。但是，需要更有效的技术和工具来自动化此类以同步为中心的应用程序的聚类以提取并行性。作者提出了一种新的聚类算法，以促进多处理器环境中软件系统的并行化。新的聚类算法实现了传统的聚类目标（减少了并行执行时间，通信成本等）。此外，我们的方法：1）减少了由于同步而导致的性能下降，以及2）避免了集群期间的死锁。仿真结果显示了我们方法的有效性。

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