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• A parallel scalable DBSCAN algorithm which outperforms other implementations. • Optimizations for data partitioning, spatial indexing, and cluster merging. • Exploiting hybrid parallelization to take advantage of modern HPC architectures. • Demonstrating accuracy, performance and scalability of our algorithm. Dbscan is a density-based clustering algorithm which is well known for its ability to discover clusters of arbitrary shape as well as to distinguish noise. As it is computationally expensive for large datasets, research studies on the parallelization of Dbscan have been received a considerable amount of attention. In this paper we present an exact, efficient and scalable parallel Dbscan algorithm which we call Hy-Dbscan. It employs three major techniques to enable scalable data clustering on distributed-memory computers i) a modified kd-tree for domain decomposition, ii) a spatial indexing approach based on grid and inference, and iii) a cluster merging scheme based on distributed Rem's Union-Find algorithm. Moreover, Hy-Dbscan exploits process level and thread level parallelization. In experiments, we have demonstrated performance and scalability using two scientific datasets on up to 2048 cores of a distributed-memory computer. Through extensive evaluation, we show that Hy-Dbscan significantly outperforms previous state-of-the-art Dbscan implementations. [ABSTRACT FROM AUTHOR]

This paper examines a novel parallel computation model called bulk synchronous farm (BSF) that focuses on estimating the scalability of compute-intensive iterative algorithms aimed at cluster computing systems. The main advantage of the proposed model is that it allows to estimate the scalability of a parallel algorithm before its implementation. Another important feature of the BSF model is the representation of problem data in the form of lists that greatly simplifies the logic of building applications. In the BSF model, a computer is a set of processor nodes connected by a network and organized according to the master/slave paradigm. A cost metric of the BSF model is presented. This cost metric requires the algorithm to be represented in the form of operations on lists. This allows us to derive an equation that predicts the scalability boundary of a parallel program: the maximum number of processor nodes after which the speedup begins to decrease. The paper includes examples of applying the BSF model to designing and analyzing parallel numerical algorithms. The large-scale computational experiments conducted on a cluster computing system confirm the adequacy of the analytical estimations obtained using the BSF model. • Scalability is important property of parallel algorithm. • The main measure for evaluating scalability is speedup. • Algorithm is scalable if speedup increases linearly with number of processors. • Scalability boundary is number of processors at which speedup peak is reached. • Parallel computation model can predict scalability boundary of parallel algorithm. [ABSTRACT FROM AUTHOR]