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DCM: A Python-based middleware for parallel processing applications on small scale devices

机译:DCM:基于Python的中间件,用于在小型设备上并行处理应用程序

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Parallel programming has been an active area of research in computer science and software engineering for many years. Parallel programming should ideally provide a linear speedup to computational problems. In reality, this is rarely the case. While there are some algorithms that cannot be parallelized, many that can, still fail to provide the ideal linear speedup. For algorithms that can benefit from parallelization, it is often much more difficult to develop the parallel code than it is to write a sequential, single-threaded program. The existence of this gap between ideal parallel computing and parallel computing on real hardware and software has caused many developers to create new solutions in an attempt to move real parallel computing closer to its idealized model. While many of these solutions provide a great performance benefit on large-scale systems, they often lag behind when deployed on small-scale systems. In this paper, we introduce the design and implementation of DCM (Distributed Computing Middleware) - a Python-based middleware for writing parallel processing applications for execution on clusters of small-scale devices. Evaluation results show the feasibility of DCM. Our middleware and its test cases are publicly available on GitHub.
机译:多年来,并行编程一直是计算机科学和软件工程研究的活跃领域。理想情况下,并行编程应提供线性加速以解决计算问题。实际上,这种情况很少发生。尽管有些算法无法并行化,但许多算法仍无法提供理想的线性加速。对于可以受益于并行化的算法,开发并行代码通常比编写顺序单线程程序要困难得多。理想的并行计算与实际的硬件和软件上的并行计算之间存在这种差距,导致许多开发人员创建了新的解决方案,试图使实际的并行计算更接近其理想化模型。尽管这些解决方案中的许多解决方案在大型系统上提供了巨大的性能优势,但是在小型系统上部署时,它们通常会落后。在本文中,我们介绍了DCM(分布式计算中间件)的设计和实现-DCM是一种基于Python的中间件,用于编写并行处理应用程序以在小型设备的集群上执行。评估结果表明了DCM的可行性。我们的中间件及其测试用例可在GitHub上公开获得。

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