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decryst: an efficient software suite for structure determination from powder dirtraction

JOURNAL OF APPLIED CRYSTALLOGRAPHY 2018年第4期51卷 1237-1243页

作者： Liu, Yu Chinese Acad Sci Inst Phys Natl Lab Superconduct Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100149 Peoples R China

Presented here is decryst, a software suite for structure determination from powder diffraction, which uses the direct-space method, and is able to apply anti-bump constraints automatically and efficiently during the procedure of global optimization using the crystallographic collision detection algorithm by Liu [Acta Cryst. (2017), A73, 414-422]. decryst employs incremental computation in its global-optimization cycles, which results in dramatic performance enhancement. It is also designed with parallel and distributed computing in mind, allowing for even better performance by simultaneous use of multiple processors. Owing to the parallelized usage of the equivalent position combination method [Deng & Dong (2009). J. Appl. Cryst. 42, 953-958] in decryst, it is particularly suitable for determination of structures with mostly unknown bonding relations, and offers some unprecedented opportunities for these structures. decryst is free and open-source software, and can be obtained at https://***/CasperVector/decryst/;it strives to be simple yet flexible, in the hope that the underlying techniques could be adopted in more crystallographic applications.

关键词： incremental computation parallel and distributed computing powder diffraction structure determination computer programs decryst

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Dynamic slack allocation algorithms for energy minimization on parallel machines

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JOURNAL OF parallel and distributed computing 2010年第5期70卷 417-430页

作者： Kang, Jaeyeon Ranka, Sanjay Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

We explore novel algorithms for DVS (Dynamic Voltage Scaling) based energy minimization of DAG (Directed Acyclic Graph) based applications on parallel and distributed machines in dynamic environments. Static DVS algorithms for DAG execution use the estimated execution time. The estimated time in practice is overestimated or underestimated. Therefore, many tasks may be completed earlier or later than expected during the actual execution. For overestimation, the extra available slack can be added to future tasks so that energy requirements can be reduced. For underestimation, the increased time may cause the application to miss the deadline. Slack can be reduced for future tasks to reduce the possibility of not missing the deadline. In this paper, we present novel dynamic scheduling algorithms for reallocating the slack for future tasks to reduce energy and/or satisfy deadline constraints. Experimental results show that our algorithms are comparable to static algorithms applied at runtime in terms of energy minimization and deadline satisfaction, but require considerably smaller computational overhead. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Energy-aware dynamic scheduling Dynamic voltage scaling Real time and embedded systems parallel and distributed computing Multiprocessor systems

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SeeMore: A kinetic parallel computer sculpture for educating broad audiences on parallel computation

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JOURNAL OF parallel and distributed computing 2017年 105卷 183-199页

作者： Li, Bo Mooring, John Blanchard, Sam Johri, Aditya Leko, Melinda Cameron, Kirk W. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Microsoft Seattle WA USA Virginia Tech Sch Visual Arts Blacksburg VA USA George Mason Univ Dept Informat Sci & Technol Fairfax VA 22030 USA Univ Kansas Dept Special Educ Lawrence KS 66045 USA

We discuss the design, implementation, and evaluation of a 256-node Raspberry-Pi cluster with kinetic properties. Each compute node is attached to a servo mechanism such that movement results from local computation. The result is SeeMore, a kinetic parallel computer sculpture designed to enable visualization of parallel algorithms in an effort to educate broad audiences as to the beauty, complexity, and importance of parallel computation. The algorithms and interfaces were implemented by students from various related courses at VA Tech. We describe these designs in sufficient detail to enable others to build their own kinetic computing sculptures to augment their experiential learning programs. Our evaluations at exhibitions indicate 63% and 84% of visitors enjoyed interacting with SeeMore while 69% and 87% believed SeeMore has educational value. Published by Elsevier Inc.

关键词： parallel and distributed computing Kinetic art Computer science education

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Advanced environments for parallel and distributed applications: a view of current status

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parallel computing 2002年第12期28卷 1637-1662页

作者： D'Ambra, P Danelutto, M di Serafino, D Lapegna, M CNR ICAR I-80126 Naples Italy Univ Pisa Dept Comp Sci I-56127 Pisa Italy Univ Naples 2 Dept Math I-81100 Caserta Italy Univ Naples Federico II Dept Math & Applicat I-80126 Naples Italy

In this paper we provide a view of the design and development activity concerning advanced environments for parallel and distributed computing. We start from assessing the main issues driving this research track, in the areas of hardware and software technology and of applications. Then, we identify some key concepts, that can be considered as common guidelines and goals in the development of modem advanced environments, and we come up with a "classification" of these environments into two main classes: programming environments and problems solving environments. Both classes are widely discussed, in light of the key concepts previously outlined, and several examples are provided, in order to give a picture of the current status and trends. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： parallel and distributed computing programming environments problem solving environments

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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

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INFORMATION SCIENCES 2014年 275卷 314-347页

作者： Chen, C. L. Philip Zhang, Chun-Yang Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China

It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore's Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as data-intensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Big Data Data-intensive computing e-Science parallel and distributed computing Cloud computing

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Leveraging teaching on demand: Approaching HPC to undergrads

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JOURNAL OF parallel and distributed computing 2021年 156卷 148-162页

作者： Catalan, Sandra Carratala-Saez, Rocio Iserte, Sergio Univ Complutense Madrid UCM Madrid Spain Univ Jaume I UJI Castellon de La Plana Spain

High Performance computing (HPC) is a highly demanded discipline in companies and institutions. However, as students and also afterwards as professors, we observed a lack of HPC related content in the engineering degrees at our university, including Computer Science. Thus, we designed and offered the engineering students a non-mandatory course entitled "Build your owncluster employing Raspberry Pi" to provide the students with HPC skills. With this course, we covered the basics of supercomputing (hardware, networking, software tools, performance evaluation, cluster management, etc.). This was possible thanks to leveraging the flexibility and versatility of Raspberry Pi devices, and the students' motivation that arose from the hands-on experience. Moreover, the course included a "Teaching on demand" component to let the attendees choose a field to explore, based on their own interests. In this paper, we offer all the details to let anyone fully reproduce the course. Besides, we analyze and evaluate the methodology that let us fulfill our objectives: increase the students' HPC skills and knowledge in such a way that they feel capable of utilizing it in their mid-term professional career. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Computational cluster Undergrad teaching System administration parallel and distributed computing Raspberry Pi

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Experience using an intermediate compiler target language for parallel machines

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MICROPROCESSORS AND MICROSYSTEMS 1997年第9期20卷 511-520页

作者： Papadopoulos, GA Department of Computer Science University of Cyprus 75 Kallipoleos Str. P.O. Box 537 CY-1678 Nicosia Cyprus

The generalised computational model of term graph rewriting systems (TGRSs) has been used extensively as an implementation vehicle for a number of, often divergent, programming paradigms ranging from the traditional functional programming ones to the (concurrent) logic programming ones and various amalgamations of them, to (concurrent) object-oriented ones. More recently, the relationship between TGRSs and process calculi (such as the pi-calculus) as well as linear logic has also been explored. In this paper we describe our experience in using the intermediate compiler target language Dactl based on TGRSs for mapping a variety of programming paradigms of the aforementioned types onto it. In particular, we concentrate on some of the issues that we feel have played an important role in our work (in, say, affecting performance, etc.), the aim being to derive a list of features that we feel every language model which intends to be used as an intermediate representation between (concurrent) high-level languages and (parallel) computer architectures must have. (C) 1997 Elsevier Science B.V.

关键词： compiler target languages intermediate representations parallel and distributed computing language embeddings term graph rewriting systems

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On message sequences for incremental network computing

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2008年第4期24卷 1023-1040页

作者： Lin, Cho-Chin Natl Ilan Univ Dept Elect Engn Yilan 260 Taiwan

In a network computing platform, tasks compete with others for shared resources to communicate messages. Incremental computing masks communication latency by overlapping computation with communication. However, a sequence of messages with a large latency variance still makes computations proceed intermittently. In this paper, the impact of the message sequence on computation efficiency is studied and a framework which employs a well organized message sequence to maximize the efficiency of computations is introduced. Firstly, a network computing model for performing incremental computations is proposed. Based on the model, theorems are developed as the groundwork based on which algorithms for finding a well organized message sequence are derived. Finally, algorithms which find a well organized message sequence in O((r/k)(k+1)) P) and O(r!/(k!)(r/k) ) comparison steps are given for sending r input data items using r/k messages of a given size k.

关键词： network computing high performance computing incremental computing parallel and distributed computing message sequence

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A novel hybrid resampling algorithm for parallel/distributed particle filters

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JOURNAL OF parallel and distributed computing 2021年 151卷 24-37页

作者： Zhang, Xudong Zhao, Liang Zhong, Wei Gu, Feng CUNY Grad Ctr Dept Comp Sci New York NY USA CUNY Lehman Coll Dept Comp Sci Bronx NY USA Univ South Carolina Upstate Div Math & Comp Sci Spartanburg SC USA CUNY Coll Staten Isl Dept Comp Sci Staten Isl NY 10314 USA

parallel/distributed particle filters have been widely used in the estimation of states of dynamic systems by using multiple processing units (PUs). In parallel/distributed particle filters, the centralized resampling needs a central unit (CU) to serve as a hub to execute the global resampling. The centralized scheme is the main obstacle for the improved performance due to its global nature. To reduce the communication cost, the decentralized resampling was proposed, which only conducted the resampling on each PU. Although the decentralized resampling can improve the performance, it suffers from the low accuracy due to the local nature. Therefore, we propose a novel hybrid resampling algorithm to dynamically adjust the intervals between the centralized resampling steps and the decentralized resampling steps based on the measured system convergence. We formulate the proposed algorithm and prove it to be uniformly convergent. Since the proposed algorithm is a generalization of various versions of the hybrid resampling, its proof provides the solid theoretical foundation for their wide adoptions in parallel/distributed particle filters. In the experiments, we evaluate and compare different resampling algorithms including the centralized resampling algorithm, the decentralized resampling algorithm, and different types of existing hybrid resampling algorithms to show the effectiveness and the improved performance of the proposed hybrid resampling algorithm. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Particle filters Sequential Monte Carlo methods Convergence parallel and distributed computing Resampling

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Bag of little bootstraps for massive and distributed longitudinal data

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STATISTICAL ANALYSIS AND DATA MINING 2022年第3期15卷 314-321页

作者： Zhou, Xinkai Zhou, Jin J. Zhou, Hua Univ Calif Los Angeles Dept Biostat Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Med Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Computat Med Los Angeles CA USA

Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package ***. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers.

关键词： bags of little bootstraps big data EMR linear mixed models longitudinal data parallel and distributed computing

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