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A survey of genome sequence assembly techniques and algorithms using high-performance computing

JOURNAL OF SUPERcomputing 2015年第1期71卷 293-339页

作者： Ahmed, Munib Ahmad, Ishfaq Ahmad, Mohammad Saad Univ Texas Arlington Arlington TX 76019 USA

Genome assembly has been an area of active research since the DNA structure was discovered and has gathered more steam after the Human Genome project was launched. A large number of genomes have been assembled and many more are in the pipeline. A number of full-scale assemblers and other special-purpose modules have been reported. Since the volume of data involved in the genome assembly process is extraordinarily large and requires significantly large computational power and processing time, many assemblers have utilized parallel computing to achieve faster and more efficient reconstruction of the DNA. A genome assembler is a multi-step process including different components that may be partly or fully parallelized. Although several assemblers and individual modules that perform various tasks, such as pairwise alignment, multiple sequence alignment, and repeat finding, have been analyzed and documented before, this paper provides a holistic view of the assembly process in the realm of parallel and distributed computing, streamlining all the individual tasks related, but not limited to, the whole genome shotgun sequencing into a sequence of loosely coupled stages where one stage consumes the output of the preceding stage and passes its results to the next one. Many of these tasks are essential to the current and next-generation sequence assemblers. The paper walks through the entire streamlined process while describing, analyzing, and commenting on the algorithms and techniques that have been designed and implemented for each of the stages. Where applicable, the paper suggests improvements that may form the basis of potentially new research work.

关键词： parallel and distributed computing Whole genome sequence assembly Sequence alignment

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Extended computational kernels in a massively parallel implementation of the Trotter-Suzuki approximation

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COMPUTER PHYSICS COMMUNICATIONS 2015年 197卷 339-340页

作者： Wittek, Peter Calderaro, Luca ICFO Castelldefels 08860 Barcelona Spain Univ Boras S-50190 Boras Sweden Univ Padua I-35123 Padua Italy

We extended a parallel and distributed implementation of the Trotter Suzuki algorithm for simulating quantum systems to study a wider range of physical problems and to make the library easier to use. The new release allows periodic boundary conditions, many-body simulations of non-interacting particles, arbitrary stationary potential functions, and imaginary time evolution to approximate the ground state energy. The new release is more resilient to the computational environment: a wider range of compiler chains and more platforms are supported. To ease development, we provide a more extensive command-line interface, an application programming interface, and wrappers from high-level languages.

关键词： Quantum evolution Trotter-Suzuki algorithm parallel and distributed computing GPU computing

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Energy-Aware Scheduling for Precedence-Constrained parallel Virtual Machines in Virtualized Data Centers

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JOURNAL OF GRID computing 2015年第2期13卷 233-253页

作者： Ebrahimirad, Vahid Goudarzi, Maziar Rajabi, Aboozar Sharif Univ Technol Dept Comp Engn Tehran Iran Univ Tehran Sch Elect & Comp Engn Tehran Iran

Large scale Internet services are expected to only increase in complexity and popularity. Their energy consumption is also a major concern in data centers. Smart scheduling of their sub-services on data center Physical Machines (PM) can effectively improve their energy as well as performance. Since today servers are not energy-proportional yet, a major and traditionally neglected source of inefficiency in them is the utilization level of PMs. We present two scheduling algorithms for precedence-constrained parallel Virtual Machines (VM) in a virtualized data center where each VM represents a sub-service of the Internet-scale service. Our algorithms use virtualization technology to increase utilization of the PMs, and hence reduce total number of active PMs, to improve energy with minimal effect on makespan. Both proposed algorithms have a polynomial time complexity which make them suitable options for scheduling of large services. Simulation results using real-world services demonstrate that the algorithms are capable of increasing utilization level of PMs on average by 52 % and improving energy consumption by 18 % while the makespan of services is degraded less than 2 %.

关键词： Energy-aware scheduling List-based scheduling Precedence-constrained parallel virtual machines Virtualized data centers parallel and distributed computing

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A scalable parallel genetic algorithm for the Generalized Assignment Problem

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parallel computing 2015年 46卷 98-119页

作者： Liu, Yan Y. Wang, Shaowen Univ Illinois CyberGIS Ctr Adv Digital & Spatial Studies Urbana IL 61801 USA Univ Illinois CyberInfrastruct & Geospatial Informat Lab CIGI Urbana IL 61801 USA Univ Illinois Dept Geog & Geog Informat Sci Urbana IL 61801 USA Univ Illinois Natl Ctr Supercomp Applicat Urbana IL 61801 USA Univ Illinois Dept Comp Sci Urbana IL 61801 USA Univ Illinois Dept Urban & Reg Planning Urbana IL 61801 USA

Known as an effective heuristic for finding optimal or near-optimal solutions to difficult optimization problems, a genetic algorithm (GA) is inherently parallel for exploiting high performance and parallel computing resources for randomized iterative evolutionary computation. It remains to be a significant challenge, however, to devise parallel genetic algorithms (PGAs) that can scale to massively parallel computer architecture (also known as the mainstream supercomputer architecture) primarily because: (1) a common PGA design adopts synchronized migration, which becomes increasingly costly as more processor cores are involved in global synchronization; and (2) asynchronous PGA design and associated performance evaluation are intricate due to the fact that PGA is a type of stochastic algorithm and the amount of computation work needed to solve a problem is not simply dependent on the problem size. To address the challenge, this paper describes a scalable coarse-grained PGA–PGAP, for a well-known NP-hard optimization problem: Generalized Assignment Problem (GAP). Specifically, an asynchronous migration strategy is developed to enable efficient deme interactions and significantly improve the overlapping of computation and communication. Buffer overflow and its relationship with migration parameters were investigated to resolve the issues of observed message buffer overflow and the loss of good solutions obtained from migration. Two algorithmic conditions were then established to detect these issues caused by communication delays and improper configuration of migration parameters and, thus, guide the dynamic tuning of PGA parameters to detect and avoid these issues. A set of computational experiments is designed to evaluate the scalability and numerical performance of PGAP. These experiments were conducted for large GAP instances on multiple supercomputers as part of the National Science Foundation Extreme Science and Engineering Discovery Environment (XSEDE). Results

关键词： Generalized Assignment Problem Genetic algorithm Heuristics parallel and distributed computing Scalability

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A job profile oriented scheduling architecture for improving the throughput of industrial printing environments

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 88卷 191-205页

作者： do Carmo, Andriele Busatto Raeder, Mateus Nunes, Thiago Kolberg, Mariana Fernandes, Luiz Gustavo GMAP PPGCC PUCRS BR-90619900 Porto Alegre RS Brazil INF PPGC UFRGS BR-91501970 Porto Alegre RS Brazil

The Digital Printing industry has become extremely specialized in the past few years. The use of personalized documents has emerged as a consolidated trend in this field. In order to meet this demand, languages to describe templates for personalized documents were proposed along with procedures which allow the correct printing of such documents. One of these procedures, which demands a high computational effort, is the ripping phase performed over a queue of documents in order to convert them into a printable format. An alternative to decrease the ripping phase computational time is to use high performance computing techniques to allow parallel ripping of different documents. However, such strategies present several unsolved issues. One of the most severe issues is the impossibility to assure a fair load balancing for any job queue. In this scenario, this work proposes a job profile oriented scheduling architecture for improving the throughput of industrial printing environments through a more efficient use of the available resources. Our results show a performance gain of up to 10% in average over the previous existing strategies applied on different job queue scenarios. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Scheduling High performance printing Documents ripping Load balancing Documents profiling parallel and distributed computing

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High-performance computing: to boldly go where no human has gone before

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2015年第13期27卷 3145-3165页

作者： Limet, Sebastien Smari, Waleed W. Spalazzi, Luca Univ Orleans INSA Ctr Val Loire Orleans Orleans France Ball Aerosp & Technol Corp Fairborn OH USA Univ Politecn Marche Ancona Italy

High-performance computing (HPC) is now present in many spheres and domains of modern society. The special issue of Concurrency Computation contains research papers addressing the state-of-the-art in HPC and simulation and reflects some of the trends reported earlier. P. Trunfio deals with a peer-to-peer file-sharing model that takes into account energy efficiency. This is achieved by means of a sleep-and-wake approach. The second manuscript falling within this category is the work by Yeh and colleagues. Their work consists of prioritizing both memory management and disk scheduling processes. The approach, which was applied to the Linux OS, allows the authors to improve process performances. Alexandru and colleagues deal with parallel computing and computation time prediction as applied to the area of computational electromagnetic modeling and simulation. The prediction model allows for an analytical estimation of the required number of computing resources in order to optimize their use while delivering the performance sought. The work by Ortega and researchers present a novel and compact form to storage of large sparse matrices coupled with the adoption of a hybrid MPI-GPU parallelization. This combined approach allows systems to extend the dimension of the Helmholtz problem they are able to solve. Neves and Araujo also deal with large sparse matrices, so common in many scientific problems. They focus on binary matrix-vector multiplication in particular. Guo and Wang also propose a prediction model. But in this case, the model assists researchers in evaluating the performances of a GPU architecture when a matrix-vector multiplication is required.

关键词： high-performance computing HPC supercomputing parallel and distributed computing algorithms clusters grid computing clouds big data reconfigurable computing modeling simulation history

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CSinparallel: Using WebMapReduce to Teach parallel computing Concepts, Hands-on (Abstract Only) 16

CSinParallel: Using WebMapReduce to Teach Parallel Computing...

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Proceedings of the 47th ACM Technical Symposium on computing Science Education

作者： Richard A. Brown Elizabeth Shoop Joel C. Adams St. Olaf College Northfield MN USA Macalester College St. Paul MN USA Calvin College Grand Rapids MI USA

ISBN: (纸本)9781450336857

Map-reduce computation is the on-ramp to data-intensive cloud computing, and arguably the most widely deployed form of parallel/distributed computing. Participants will carry out exercises designed for students at CS1, intermediate, and advanced levels that introduce data-intensive scalable computing concepts using WebMapReduce (WMR), a simplified open-source interface to the dominant Hadoop map-reduce programming environment. WMR supports programming in a choice of languages including Python, Java, C++, and C#. Besides a hands-on experience with introductory teaching materials, the workshop includes an overview of teaching advanced map-reduce programming using WMR, and a comparison of WMR to direct Hadoop programming. All materials will reside on ***, and the demonstration WMR system is reservable for participants' courses. Intended audience: CS instructors. Web-enabled laptop required.

关键词： design patterns csinparallel parallel and distributed computing cs1 exemplars wmr webmapreduce map-reduce education advanced courses distributed computing curriculum parallel design patterns parallel computing parallelism

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Bourbaki's structure theory in the problem of complex systems simulation models synthesis and model-oriented programming

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2015年第1期55卷 148-159页

作者： Brodsky, Yu. I. Russian Acad Sci Dorodnicyn Comp Ctr Moscow 119333 Russia

The work is devoted to the application of Bourbaki's structure theory to substantiate the synthesis of simulation models of complex multicomponent systems, where every component may be a complex system itself. An application of the Bourbaki's structure theory offers a new approach to the design and computer implementation of simulation models of complex multicomponent systems-model synthesis and model-oriented programming. It differs from the traditional object-oriented approach. The central concept of this new approach and at the same time, the basic building block for the construction of more complex structures is the concept of models-components. A model-component endowed with a more complicated structure than, for example, the object in the object-oriented analysis. This structure provides to the model-component an independent behavior-the ability of standard responds to standard requests of its internal and external environment. At the same time, the computer implementation of model-component's behavior is invariant under the integration of models-components into complexes. This fact allows one firstly to construct fractal models of any complexity, and secondly to implement a computational process of such constructions uniformly-by a single universal program. In addition, the proposed paradigm allows one to exclude imperative programming and to generate computer code with a high degree of parallelism.

关键词： simulation complex multi-component systems model synthesis model-oriented programming parallel and distributed computing

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A practical introduction to butterfly and adaptive resampling in Sequential Monte Carlo

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IFAC-PapersOnLine 2015年第28期48卷 787-792页

作者： Guldas, Hakan Cemgil, A. Taylan Whiteley, Nick Heine, Kari Department of Computer Engineering Bogazici University Istanbul Turkey School of Mathematics University of Bristol United Kingdom Department of Statistics UCL United Kingdom

parallel and distributed computing technologies offer great potential for speed-up of Monte Carlo algorithms. However, in the development of most existing algorithms it has been implicitly assumed that implementation would be on a serial machine, so algorithm structure is often not well-suited to parallel architectures. In recent work the authors have studied the theoretical properties of sequential Monte Carlo algorithms involving a \buttery” resampling method, whose conditional independence structure is intended to better match parallel and distributed architectures, with resampling broken down into stages, allowing sampling tasks for subsets of the particles to be handled concurrently. This paper provides a more practical overview of these methods, including consideration of adaptive resampling schemes, numerical results and an accessible account of theoretical properties. © 2015

关键词： Monte Carlo methods Numerical methods parallel architectures Conditional independences distributed architecture parallel and distributed computing parallelizations Particle filter Practical introduction Sequential Monte Carlo Sequential Monte Carlo algorithms

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NBC: An Efficient Hierarchical Clustering Algorithm for Large Datasets

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INTERNATIONAL JOURNAL OF SEMANTIC computing 2015年第3期9卷 307-331页

作者： Zhang, Wei Zhang, Gongxuan Wang, Yongli Zhu, Zhaomeng Li, Tao Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 XiaoLingWei Rd Nanjing 210094 Jiangsu Peoples R China

Nearest neighbor search is a key technique used in hierarchical clustering and its computing complexity decides the performance of the hierarchical clustering algorithm. The time complexity of standard agglomerative hierarchical clustering is O(n(3)), while the time complexity of more advanced hierarchical clustering algorithms (such as nearest neighbor chain, SLINK and CLINK) is O(n(2)). This paper presents a new nearest neighbor search method called nearest neighbor boundary (NNB), which first divides a large dataset into independent subset and then finds nearest neighbor of each point in subset. When NNB is used, the time complexity of hierarchical clustering can be reduced to O(n log(2)n). Based on NNB, we propose a fast hierarchical clustering algorithm called nearest-neighbor boundary clustering (NBC), and the proposed algorithm can be adapted to the parallel and distributed computing framework. The experimental results demonstrate that our algorithm is practical for large datasets.

关键词： Hierarchical clustering nearest neighbor boundary parallel and distributed computing MapReduce

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