Parallel and distributed kmeans to identify the translation initiation site of proteins

Rodrigues, Laerte M; Zárate, Luis Enrique; Nobre, Cristiane Neri; Freitas, Henrique C.

doi:10.1109/icsmc.2012.6377972

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…The distributed algorithm described in this section is widely used in practice [39,10,34] and a scalability analysis for this algorithm can be seen in the work by Rodrigues et al [35]. Compared to the multi-threaded algorithm, the distributed K-Means algorithm takes an additional parameter p that specifies the number of distributed peers to be used.…”

Section: Distributed Algorithmmentioning

confidence: 98%

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

Francesquini

Castro

Penna

et al. 2015

Journal of Parallel and Distributed Computing

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h i g h l i g h t s• Programming for a manycore is challenging.• Limited memory and NoC are among the most important constraints of manycores. • For CPU-bound and mixed workloads, MPPA-256 achieves better performance than Xeon. • MPPA-256 consumes up to 13× less energy than embedded and general-purpose multicores. a b s t r a c t Until the last decade, performance of HPC architectures has been almost exclusively quantified by their processing power. However, energy efficiency is being recently considered as important as raw performance and has become a critical aspect to the development of scalable systems. These strict energy constraints guided the development of a new class of so-called light-weight manycore processors. This study evaluates the computing and energy performance of two well-known irregular NP-hard problems -the Traveling-Salesman Problem (TSP) and K-Means clustering -and a numerical seismic wave propagation simulation kernel -Ondes3D -on multicore, NUMA, and manycore platforms. First, we concentrate on the nontrivial task of adapting these applications to a manycore, specifically the novel MPPA-256 manycore processor. Then, we analyze their performance and energy consumption on those different machines. Our results show that applications able to fully use the resources of a manycore can have better performance and may consume from 3.8× to 13× less energy when compared to low-power and general-purpose multicore processors, respectively.

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Section: Distributed Algorithmmentioning

confidence: 98%

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

Francesquini

Castro

Penna

et al. 2015

Journal of Parallel and Distributed Computing

Self Cite

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“…A parallel version of Lloyd's algorithm for distributed systems implemented using MPI was described in [31]. A hybrid parallelization of Lloyd's method based on MPI and OpenMP was proposed in [32]. Contrary to our method, that approach employed a reduction algorithm with a linear computational complexity depending on the number of cores.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

Kwedlo

Czochanski

2019

IEEE Access

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The standard formulation of the K -means clustering (Lloyd's method) performs many unnecessary distance calculations. In this paper, we focus on four approaches that use the triangle inequality to avoid unnecessary distance calculations. These approaches are Drake's, Elkan's, Annulus, and Yinyang algorithms. We propose a hybrid MPI/OpenMP parallelization of these algorithms in which the dataset and the corresponding data structures storing bounds on distances are evenly divided among MPI processes. Then, in the assignment step of a K -means iteration, each MPI process computes the assignment of its portion of data using OpenMP threads. In the update step of the iteration, the cluster centroids are computed using a hierarchical all-reduce operation. In the computational experiments, we compared the strong scalability of these four algorithms with the scalability of Lloyd's algorithm, parallelized using the same approach. The results indicate that all four algorithms maintain an advantage in computing time over Lloyd's algorithm. A comparison with two software packages, whose sources are publicly available, in the same computing environment, shows that our implementations are more efficient.

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“…Distributed versions of clustering algorithms related to kernel k-Means, like classic k-Means [86] and k-Medians [87] have already been proposed. However, to the best of our knowledge, a distributed approach to kernel k-Means has not been proposed yet.…”

Section: B Distributed Trimmed Kernel K-means Clusteringmentioning

confidence: 99%

Big Data Analysis for Media Production

et al. 2016

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Abstract-A typical high-end film production generates several terabytes of data per day, either as footage from multiple cameras or as background information regarding the set (laser scans, spherical captures, etc). This paper presents solutions to improve the integration of the multiple data sources, and understand their quality and content, which are useful both to support creative decisions on-set (or near it) and enhance the post-production process. The main cinema specific contributions, tested on a multisource production dataset made publicly available for research purposes, are the monitoring and quality assurance of multi-camera set-ups, multisource registration and acceleration of 3D reconstruction, anthropocentric visual analysis techniques for semantic content annotation, and integrated 2D-3D web visualization tools. We discuss as well improvements carried out in basic techniques for acceleration, clustering and visualization, which were necessary to deal with the very large multisource data, and can be applied to other big data problems in diverse application fields.

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Parallel and distributed kmeans to identify the translation initiation site of proteins

Cited by 12 publications

References 14 publications

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

Big Data Analysis for Media Production

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