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distributed computing for macromolecular crystallography

ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2018年第2期74卷 143-151页

作者： Krissinel, Evgeny Uski, Ville Lebedev, Andrey Winn, Martyn Ballard, Charles Rutherford Appleton Lab STFC Sci Comp Dept Didcot OX11 0FA Oxon England

Modern crystallographic computing is characterized by the growing role of automated structure-solution pipelines, which represent complex expert systems utilizing a number of program components, decision makers and databases. They also require considerable computational resources and regular database maintenance, which is increasingly more difficult to provide at the level of individual desktop-based CCP4 setups. On the other hand, there is a significant growth in data processed in the field, which brings up the issue of centralized facilities for keeping both the data collected and structure-solution projects. The paradigm of distributed computing and data management offers a convenient approach to tackling these problems, which has become more attractive in recent years owing to the popularity of mobile devices such as tablets and ultra-portable laptops. In this article, an overview is given of developments by CCP4 aimed at bringing distributed crystallographic computations to a wide crystallographic community.

关键词： crystallographic computing distributed computing data and project management web services computational cloud

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On Scalability of Association-rule-based Recommendation: A Unified distributed-computing Framework

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ACM TRANSACTIONS ON THE WEB 2020年第3期14卷 13-13页

作者： Wu, Zhiang Li, Changsheng Cao, Jie Ge, Yong Nanjing Audit Univ 86 West Yushan Rd Nanjing 211815 Peoples R China Nanjing Univ Finance & Econ 128 North Railway St Nanjing 210003 Peoples R China Univ Arizona McClelland Hall 430V11130 E Helen St Tucson AZ USA

The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users' actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.

关键词： Recommender system association rule frequent pattern distributed computing load balanced partitioning

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Coding for distributed computing: Reed-Solomon Codes for Private, distributed computing

Coding for Distributed Computing: Reed-Solomon Codes for Pri...

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作者： REENT SCHLEGEL CHALMERS UNIVERSITY OF TECHNOLOGY

学位级别：硕士

distributed cluster systems have emerged as a useful way to perform large scale computations. These clusters can be rented at service providers, which imposes constraints regarding the privacy of the data the computations are done on. Fur- thermore, straggling servers that take an excessive amount of time to? nish their computation slow down the overall process gravely. In this thesis, a coding scheme that mitigates the e? ect of straggling servers and assures privacy is introduced. This is made possible by a concatenation of two Reed-Solomon codes, one for straggler mitigation directly at the service providers and one in combination with random data to assure privacy which is used on the data itself, before it is distributed to multiple service providers. The impact of di? erent network structures on the overall runtime as well as the impact of privacy on the optimal code rate for minimizing the overall runtime is investigated and set in relation with existing theory, such as studies from Lee et al. on the optimal code rate of maximum distance separable codes for distributed computing.

关键词： Coding Theory distributed computing Information-Theoretic Privacy Maximum Distance Separable Codes Private computing Reed-Solomon Codes

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Teaching Parallel and distributed computing with MPI on Raspberry Pi Clusters 18

Teaching Parallel and Distributed Computing with MPI on Rasp...

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49th ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE)

作者： Brown, Richard Adams, Joel Matthews, Suzanne Shoop, Elizabeth St Olaf Coll Northfield MN 55057 USA Calvin Coll Grand Rapids MI 49506 USA USMA West Point NY USA Macalester Coll St Paul MN 55105 USA

ISBN: (纸本)9781450351034

CS2013 brings parallel and distributed computing (PDC) into the CS curricular mainstream. The Message Passing Interface (MPI) is a platform independent, industry-standard library for parallel and distributed computing. The MPI standard includes support for C, C++, and Fortran; third parties have created implementations for Python and Java. This hands-on workshop introduces MPI basics and applications in C/C++ using Raspberry Pi single-board computers, as an inexpensive and engaging hardware platform for studying PDC. The workshop includes: (i) personal experience with the Raspberry Pi (units provided) accessed via participant laptops (Windows, Mac, or Linux); (ii) assembly of Beowulf clusters of Raspberry Pis quickly in the classroom; (iii) self-paced hands-on experimentation with the working MPI programs; and (iv) a discussion of how such clusters can be used to engage students in and out of the classroom. Participants will experience how to teach distributed computing essentials with MPI by means of reusable, effective "parallel programming patterns," including single program multiple data (SPMD) execution, send-receive message passing, the master-worker, parallel loop, and other common patterns. Participants will then explore more in-depth "exemplar" applications, such as drug design and epidemiology. All materials including the Raspberry Pi software system setup from this workshop will be freely available from ***. No prior experience with MPI, PDC, or the Raspberry Pi is required. Windows, Mac, or Linux laptop required.

关键词： CSinParallel MPI distributed computing exemplars message passing parallel and distributed computing parallel design patterns

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Performance Prediction for distributed Graph computing

Performance Prediction for Distributed Graph Computing

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International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Ji, Shuo Zhao, Yinliang Li, Yuxiang Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian Shaanxi Peoples R China Henan Univ Sci & Technol Sch Informat Engn Luoyang Peoples R China

ISBN: (纸本)9781728104669

Performance estimation for executing graph algorithms on the distributed systems, especially with sacrificing the accuracy of results to improve the runtime performance, is a pre-requisite for optimizing system parameters to achieve an applicable tradeoff between the runtime and the inaccuracy of results. This paper presents an experimental approach that predicts the runtime and the inaccuracy of conducting graph algorithms on the BSP-based distributed graph computing systems to optimize system parameters by using an artificial neural network (ANN) model. It samples different scales of subgraphs from the complete input graphs and executes the underlying algorithm on each subgraph to capture its characteristics. Then it essentially predicts the performance of executing the underlying algorithm on the complete graph by learning the scalability that how the runtime and the inaccuracy of results vary with different scales of graphs with an ANN network, which is trained off-line based on the captured characteristics of the subgraphs. We conducted all experimental studies on the Amazon EC2 Cloud. The experimental results demonstrate that the prediction approach can effectively predict the runtime with a relative error rate under 8% averagely and the inaccuracy of results with a relative error rate under 25% averagely compared to the actual performance results.

关键词： distributed computing graph computing performance prediction

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Integrating Brain Implants With Local and distributed computing Devices: A Next Generation Epilepsy Management System

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2018年 6卷 2500112-2500112页

作者： Kremen, Vaclav Brinkmann, Benjamin H. Kim, Inyong Guragain, Hari Nasseri, Mona Magee, Abigail L. Attia, Tal Pal Nejedly, Petr Sladky, Vladimir Nelson, Nathanial Chang, Su-Youne Herron, Jeffrey A. Adamski, Tom Baldassano, Steven Cimbalnik, Jan Vasoli, Vince Fehrmann, Elizabeth Chouinard, Tom Patterson, Edward E. Litt, Brian Stead, Matt Van Gompel, Jamie Sturges, Beverly K. Jo, Hang Joon Crowe, Chelsea M. Denison, Timothy Worrell, Gregory A. Mayo Clin Mayo Syst Electrophysiol Lab Dept Neurol Rochester MN 55905 USA Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague 16000 Czech Republic Mayo Clin Dept Physiol & Biomed Engn Rochester MN 55905 USA St Annes Univ Hosp Int Clin Res Ctr Brno 65691 Czech Republic Mayo Clin Dept Neurosurg Rochester MN 55905 USA Medtronic Restorat Therapy Grp Res & Core Technol Minneapolis MN 55432 USA Univ Penn Ctr Neuroengn & Therapeut Dept Bioengn Philadelphia PA 19104 USA Univ Minnesota Dept Vet Clin Sci Coll Vet Med St Paul MN 55108 USA Univ Calif Davis Dept Surg & Radiol Sci Davis CA 95616 USA Mayo Clin Dept Neurol Rochester MN 55905 USA Univ Calif Davis Vet Med Teaching Hosp Davis CA 95616 USA

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in off the body local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

关键词： Epilepsy deep brain stimulation implantable devices seizure detection seizure prediction distributed computing

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Solving large batches of traveling salesman problems with parallel and distributed computing

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COMPUTERS & OPERATIONS RESEARCH 2017年 85卷 87-96页

作者： Ozden, S. G. Smith, A. E. Gue, K. R. Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Univ Louisville Dept Ind Engn Louisville KY 40292 USA

In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin-Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150- and 200-city TSPs can increase the overall utilization of computer resources up to 25% compared to single thread computing. The resulting speed-up/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC3 in the literature. For variable TSP sizes, a longest processing time first heuristic performs better than an equal distribution rule. We illustrate our approach with an application in the design of order picking warehouses. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： TSP Parallel computing Routing distributed computing Facilities design

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Adaptive fault detection and diagnosis using parsimonious Gaussian mixture models trained with distributed computing techniques

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2017年第6期354卷 2543-2572页

作者： Nakamura, Thiago A. Palhares, Reinaldo M. Caminhas, Walmir M. Menezes, Benjamin R. de Campos, Mario Cesar M. M. Fumega, Ubirajara Bomfim, Carlos H. de M. Lemos, Andre P. Univ Fed Minas Gerais Dept Elect Engn Av Antonio Calos 6627 BR-31270901 Belo Horizonte MG Brazil Petrobras SA CENPES Av Horacio de Macedo950 Cidade Univ BR-21941915 Rio De Janeiro RJ Brazil Petrobras SA REGAP R Jose Dias Diniz 690 BR-32689898 Betim MG Brazil

After a great advance by the industry on processes automation, an important challenge still remains: the automation under abnormal situations. The first step towards solving this challenge is the Fault Detection and Diagnosis (FDD). This work proposes a batch-incremental adaptive methodology for fault detection and diagnosis based on mixture models trained on a distributed computing environment. The models used are from a family of Parsimonious Gaussian Mixture Models (PGMM), in which the reduced number of parameters of the model brings important advantages when there are few data available, an expected scenario of faulty conditions. On the other hand, a large number of different models rises another challenge, the best model selection for a given behaviour. For that, it is proposed to train a large number of models, using distributed computing techniques, for only then select the best model. This work proposes the usage of the Spark framework, ideal for iterative computations. The proposed methodology was validated in a simulated process, the Tennessee Eastman Process (TEP), showing good results for both the detection and the diagnosis of faults. Furthermore, numeric experiments show the viability of training a large number of models for the best model selection a posteriori. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： PARSIMONY (Statistics) GAUSSIAN elimination GAUSSIAN processes distributed computing FAULT diagnosis (Chemical engineering)

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distributed computing in a Cloud of Mobile Phones

Distributed Computing in a Cloud of Mobile Phones

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作者： Sanches, Pedro Miguel Castanheira Universidade Nova de Lisboa

学位级别：硕士

For the past few years we have seen an exponential growth in the number of mobiledevices and in their computation, storage and communication capabilities. We also have seen an increase in the amount of data generated by mobile devices while performing common tasks. Additionally, the ubiquity associated with these mobile devices, makes it more reasonable to start thinking in a different use for them, where they will begin to act as an important part in the computation of more demanding applications, rather than relying exclusively on external servers to do *** is also possible to observe an increase in the number of resource demanding applications, whereas these resort to the use of services, offered by infrastructure ***, with the use of these Cloud services, many problems arise, such as: the considerable use of energy and bandwidth, high latency values, unavailability of connectivity infrastructures, due to the congestion or the non existence of it. Considering all the above, for some applications it starts to make sense to do part or all the computation locally in the mobile devices *** propose a distributed computing framework, able to process a batch or a streamof data, which is being generated by a cloud composed of mobile devices, that doesnot require Internet services. Differently from the current state-of-the-art, where bothcomputation and data are offloaded to mobile devices, our system intends to move thecomputation to where the data is, reducing significantly the amount of data exchangedbetween mobile *** on the evaluation performed, both on a real and simulated environment, ourframework has proved to support scalability, by benefiting significantly from the usageof several devices to handle computation, and by supporting multiple devices submitting computation requests while not having a significant increase in the latency of a request. It also proved that is able to deal with churn without being highly penalized by it.

关键词： Mobile Edge Cloud computing Stream Processing distributed computing Android

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Scalable and efficient data distribution for distributed computing of all-to-all comparison problems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 67卷 152-162页

作者： Zhang, Yi-Fan Tian, Yu-Chu Kelly, Wayne Fidge, Colin Queensland Univ Technol Sch Elect Engn & Comp Sci GPO Box 2434 Brisbane Qld 4001 Australia Taiyuan Univ Technol Coll Informat Engn Taiyuan 030024 Shanxi Peoples R China

All-to-all comparison problems represent a class of big data processing problems widely found in many application domains. To achieve high performance for distributed computing of such problems, storage usage, data locality and load balancing should be considered during the data distribution phase in the distributed environment. Existing data distribution strategies, such as the Hadoop one, are designed for problems with MapReduce pattern and do not consider comparison tasks at all. As a result, a huge amount of data must be re-arranged at runtime when the comparison tasks are executed, degrading the overall computing performance significantly. Addressing this problem, a scalable and efficient data distribution strategy is presented in this paper with comparison tasks in mind for distributed computing of all-to-all comparison problems. Specifically designed for problems with all-to-all comparison pattern, it not only saves storage space and data distribution time but also achieves load balancing and good data locality for all comparison tasks of the all-to-all comparison problems. Experiments are conducted to demonstrate the presented approaches. It is shown that about 90% of the ideal performance capacity of the multiple machines can be achieved through using the approach presented in this paper. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed computing Big data All-to-all comparison Data distribution

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