The paper aims to propose a distributed clustering method for High performance computing (HPC) models and, its application for medical image processing. The communication cost is one of the great challenges, which minimizes the scalability of parallel and distributed computing models. Indeed, it reduces significantly the performance of HPC systems where these models are assigned to be implemented. In this paper, we present a new distributed k-means method which integrates virtual parallel distributed computing model with a low communication cost mechanism. The k-means method is performed as a distributed service within a cooperative micro-services team which uses asynchronous communication mechanism based on AMQP protocol. We design and implement a parallel and distributed HPC application for MRI image segmentation assigned to be deployed on cloud. Experimental results show that the proposed method (DSCM) and its assigned model reach high degree of scalability. We expect this clustering approach to provide scalable HPC applications for big data clustering.

本文旨在为高性能计算（HPC）模型提出一种分布式聚类方法及其在医学图像处理中的应用。通信成本是最大的挑战之一，它使并行和分布式计算模型的可伸缩性最小化。实际上，它会大大降低指定要实现这些模型的HPC系统的性能。在本文中，我们提出了一种新的分布式k-means方法，该方法将虚拟并行分布式计算模型与低通信成本机制集成在一起。在使用基于AMQP协议的异步通信机制的协作微服务团队中，k-means方法是作为分布式服务执行的。我们设计并实现了并行和分布式HPC应用程序，用于分配给云的MRI图像分割。实验结果表明，所提出的方法（DSCM）及其分配的模型具有很高的可扩展性。我们希望这种群集方法能够为大数据群集提供可扩展的HPC应用程序。