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作者： Dylan A Goodin University of Louisville

学位级别：硕士

Mathematical modeling aims to provide a theoretical framework for understanding tissue dynamics and for establishing treatment response for diseased tissues, such as tumors. Previously published continuum models have successfully represented idealized two-dimensional and three-dimensional tissue for short periods of time. A recently published continuum model of cancer increases model complexity and describes three-dimensional tissue that, due to the required complexity of the geometric multigrid solver, can only be feasibly applied to millimeter-scale simulations. Furthermore, the computational cost for such models has hindered their application in the laboratory and in the clinic. With computational demands greatly outpacing current openMP-based approaches on single-CPU-socket machines, higher performance solvers for large-scale tissue models remain a critical need. In this thesis, preliminary results of a CUDA and CUDA-MPI based parallelization applied to a tissue model are presented, with significant speedups seen in solution calculation for an initial time step. With further access to larger distributed computing, these parallel frameworks could potentially scale to simulate large-scale tissues.

关键词： Parallel Processing Mathematical Modeling distributed computing Cancer Modeling Other Biomedical Engineering and Bioengineering

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Performance prediction for distributed graph computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第12期34卷 e5961-e5961页

作者： Ji, Shuo Zhao, Yinliang Li, Yuxiang Northwest Univ Sch Informat Sci & Technol Xian Peoples R China Northwest Univ Sch Informat Sci & Technol State Prov Joint Engn & Res Ctr Adv Networking In Xian Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Henan Univ Sci & Technol Sch Informat Engn Luoyang Peoples R China

Performance prediction for executing graph applications on distributed systems is a prerequisite to improve system performance. Especially for distributed systems optimized by sacrificing the accuracy of results to improve runtime performance, performance prediction can be used to determine accuracy-related system parameters to achieve a tradeoff between the runtime and inaccuracy of results. This article presents an approach that predicts the runtime and inaccuracy of executing graph algorithms on bulk synchronous parallel (BSP)-based systems to optimize system parameters by using an artificial neural network (ANN). The proposed approach samples different scales of subgraphs from the input graph by maintaining that the features of subgraphs are similar to the features of the input graph. Then it executes graph algorithm on each subgraph and extracts their runtime features. An ANN-based performance prediction model is trained off-line based on the extracted features and is used to predict the performance of executing graph algorithm on the complete input graph. We have validated the proposed approach by conducting single-source shortest path, connected component, and PageRank on the BSP-based distributed systems. The experimental results demonstrate that the prediction method can effectively predict the runtime and the inaccuracy with a relative error rate under 14% and under 25%, respectively, compared with the actual performance results.

关键词： distributed computing graph computing performance prediction

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A decision support system for scientists by processing large-scale satellite images on a distributed computing environment

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第11期77卷 14305-14326页

作者： Lee, Sang-hwan Choi, Yun-soo Lee, Ryong Park, Gyong-seok Hong, Eui Kyeong Korea Inst Sci & Technol Informat Daejeon South Korea Univ Seoul Seoul South Korea

Data-driven research strategy has been widely accepted in many scientific areas. In the strategy, which consists of several processing steps including data collection, processing, analysis and discovery, data processing step is so time-consuming that it delays the overall time of scientific discovery. This also happens in the field of satellite remote sensing. As SeaDAS, a popular software package used for processing satellite remote sensing images, has been initially devised to work on a single site;it is inevitable to take unendurable time for processing massive remote sensing images. To rectify this issue, this article introduces a data-driven analysis system that rapidly processes a massive volume satellite remote sensing images. Unlike the conventional program for remote sensing images, our system is developed by virtue of both a distributed array-based DBMS and a high-throughput computing facility in order to process massive remote sensing images in a parallel and distributed manner. Consequently, it allows scientists to perform their analysis workloads more quickly and efficiently. Through extensive experiments performed with real-world dataset on 10 computing nodes, we show that our system is up to 27.5 times faster than the conventional SeaDAS package.

关键词： BigData Remote sensing images distributed computing Decision Support System

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Sunburn-using excess energy of small-scale production for distributed computing

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ENERGY EFFICIENCY 2018年第1期11卷 97-119页

作者： Nurminen, J. K. Niemi, T. Strandman, J. Ruokosuo, K. Helsinki Inst Phys Helsinki Finland Aalto Univ Helsinki Finland CERN Helsinki Inst Phys Meyrin Switzerland

The Sunburn solar computer system is based on the idea of consuming the excess electricity of photovoltaic energy systems for useful computing. In this way, the proposed approach allows us to move a part of computing capacity from dedicated data centers to individual solar energy systems and thereby reduce the energy consumption of the data centers. In principle, the proposed mechanism converts the excess electrical energy into valuable computation. The results of computation or data processing are easier to store and transfer than produced electricity. In small-scale systems, the benefit from storing or selling the excess electricity is small, whereas the benefit from selling the generated data is potentially larger, as we demonstrate in this study. Finally, the technical feasibility of the solution is illustrated by constructing and evaluating a prototype implementation using excess solar energy for distributed BOINC computing.

关键词： distributed computing Solar energy Green computing Energy storage Scientific computing distributed data center

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Wrangling distributed computing for high- throughput environmental science: An introduction to HTCondor

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PLOS COMPUTATIONAL BIOLOGY 2018年第10期14卷 1-8页

作者： Erickson, Richard A. Fienen, Michael N. McCalla, S. Grace Weiser, Emily L. Bower, Melvin L. Knudson, Jonathan M. Thain, Greg US Geol Survey Upper Midwest Environm Sci Ctr La Crosse WI 54601 USA US Geol Survey Wisconsin Water Sci Ctr Middelton WI USA Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Wisconsin Inst Discovery Madison WI USA

Biologists and environmental scientists now routinely solve computational problems that were unimaginable a generation ago. Examples include processing geospatial data, analyzing -omics data, and running large-scale simulations. Conventional desktop computing cannot handle these tasks when they are large, and high-performance computing is not always available nor the most appropriate solution for all computationally intense problems. High-throughput computing (HTC) is one method for handling computationally intense research. In contrast to high-performance computing, which uses a single "supercomputer," HTC can distribute tasks over many computers (e.g., idle desktop computers, dedicated servers, or cloud-based resources). HTC facilities exist at many academic and government institutes and are relatively easy to create from commodity hardware. Additionally, consortia such as Open Science Grid facilitate HTC, and commercial entities sell cloud-based solutions for researchers who lack HTC at their institution. We provide an introduction to HTC for biologists and environmental scientists. Our examples from biology and the environmental sciences use HTCondor, an open source HTC system.

关键词： distributed computing BIOLOGISTS CLOUD computing OPEN source software COMPUTATIONAL biology

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The Network As a Computer: A Framework for distributed computing Over IoT Mesh Networks

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IEEE INTERNET OF THINGS JOURNAL 2018年第3期5卷 2107-2119页

作者： Di Pascale, Emanuele Macaluso, Irene Nag, Avishek Kelly, Mark Doyle, Linda Trinity Coll Dublin CONNECT Dublin 2 Ireland

Ultradense Internet of Things (IoT) mesh networks and machine-to-machine communications herald an enormous opportunity for new computing paradigms and are serving as a catalyst for profound change in the evolution of the Internet. The collective computation capability of these IoT devices is typically neglected in favor of cloud or edge processing. We propose a framework to tap into this resource pool by coupling data communication and processing. Raw data captured by sensing devices can be aggregated and transformed into appropriate actions as it travels along the network toward actuating nodes. This paper presents an element of this vision, whereby we map the operations of an artificial neural network onto the communication of a multihop IoT network for simultaneous data transfer and processing. By exploiting the principle of locality inherent to many IoT applications, the proposed approach can reduce the latency in delivering processed information. Furthermore, it improves the distribution of energy consumption across the IoT network compared to a centralized processing scenario, thus mitigating the "energy hole" effect and extending the overall lifetime of the system.

关键词： Artificial neural networks (ANNs) distributed computing Internet of Things (IoT) wireless mesh networks wireless sensor networks (WSNs)

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WarpEngine, a Flexible Platform for distributed computing Implemented in the VEGA Program and Specially Targeted for Virtual Screening Studies

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2018年第6期58卷 1154-1160页

作者： Pedretti, Alessandro Mazzolari, Angelica Vistoli, Giulio Univ Milan Dipartimento Sci Farmaceut Fac Farm Via Luigi Mangiagalli 25 I-20133 Milan Italy

The manuscript describes WarpEngine, a novel platform implemented within the VEGA ZZ suite of software for performing distributed simulations both in local and wide area networks. Despite being tailored for structure-based virtual screening campaigns, WarpEngine possesses the required flexibility to carry out distributed calculations utilizing various pieces of software, which can be easily encapsulated within this platform without changing their source codes. WarpEngine takes advantages of all cheminformatics features implemented in the VEGA ZZ program as well as of its largely customizable scripting architecture thus allowing an efficient distribution of various time-demanding simulations. To offer an example of the WarpEngine potentials, the manuscript includes a set of virtual screening campaigns based on the ACE data set of the DUD-E collections using PLANTS as the docking application. Benchmarking analyses revealed a satisfactory linearity of the WarpEngine performances, the speed-up values being roughly equal to the number of utilized cores. Again, the computed scalability values emphasized that a vast majority (i.e., >90%) of the performed simulations benefit from the distributed platform presented here. WarpEngine can be freely downloaded along with the VEGA ZZ program at ***.

关键词： computer software PLANTS Manuscripts Platform distributed computing cheminformatics Wide area networks Virtual plants VEGA

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Water molecules migration at the oil-paper interface during heating: a large-scale distributed computing study

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MOLECULAR SIMULATION 2018年第15期44卷 1200-1209页

作者： Wang, Wei Dong, Wenyan Jiang, Da Ning, Zhongzheng North China Elect Power Univ Beijing Key Lab High Voltage & Electromagnet Comp Beijing Peoples R China

In view of the small molecular model established in the field of high voltage insulation, the actual operation of transformer cannot be fully reflected at micro-level. Therefore, this paper aims to improve the performance of computing environment and expand molecular scale. Firstly, two servers were connected through a high-speed communication network as the initial cluster architecture. Secondly, spatial decomposition and load balancing algorithms were used to improve the operation efficiency of cluster. Meanwhile, the oil-paper composite media model of 10(5) atoms could be established based on this cluster, but it consumed a lot of time. Therefore, we analysed the relationship between operation efficiency and four characteristic quantities such as central processing unit performance, number of cores, simulation time and number of nodes. Then the highest point of cluster operating efficiency was found through continuous optimisation. It can be summarised that the calculating speed of cluster is nearly 10 times faster than that of the large server. Meanwhile, according to the results based on this cluster, it can be concluded that water molecules would migrate towards the oil during heating. When the initial moisture content in paper is high, the high water region would appear at the oil-paper interface.

关键词： Cluster distributed computing molecular dynamics oil-paper insulation

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distributed Fog computing Architecture for Real-Time Anomaly Detection in Smart Meter Data 6

Distributed Fog Computing Architecture for Real-Time Anomaly...

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6th IEEE International Conference on Big Data computing Service and Applications (IEEE BigDataService)

作者： Jaiswal, Rituka Chakravorty, Antorweep Rong, Chunming Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway

ISBN: (纸本)9781728170220

The use of Fog computing for real-time Big Data monitoring of power consumption is gaining popularity. In traditional systems, Cloud servers receive sensor Big Data, perform predictions and detect anomalies or any threat patterns and then raise the alarms. With exponentially increasing sensor data, Cloud servers are becoming impractical to process this data because of the issues of volume, velocity, variety, network bandwidth, real-time support and security issues. Fog computing is introduced as a distributed computing paradigm that uses intermediate computing infrastructure for processing to overcome the limitations of Cloud computing. In this paper, we propose a hierarchically distributed Fog computing architecture to deploy machine learning based anomaly detection models for generating insights from the collected Smart meter sensor data from the household. The anomaly detection is divided into two steps: model training and anomaly detection. We perform detailed analysis and evaluation of the models using standard open datasets obtained from UCI machine learning repository. The results confirm the efficacy of our proposed architecture. We used open source framework and software for our experiments.

关键词： Fog computing Real-time Anomaly detection Big Data Analytics Machine Learning Ensemble Learning Power Consumption Prediction distributed computing

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A general-purpose distributed computing Java middleware

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第7期31卷 e4967-e4967页

作者： Barroso Almeida, Andre Luis Cimino, Leonardo de Souza Eugenio de Resende, Jose Estevao Moreira Silva, Lucas Henrique Souza Rocha, Samuel Queiroz Gregorio, Guilherme Aparecido Paiva, Gustavo Silva Delabrida, Saul Santos, Haroldo Gambini Moreira de Carvalho, Marco Antonio Lins Aquino, Andre Luiz Lima, Joubert de Castro Univ Fed Ouro Preto DECOM Ouro Preto Brazil Inst Fed Minas Gerais Control & Automat Dept CODAAUT Ouro Preto Brazil Univ Fed Alagoas IC Maceio Brazil

The middleware solutions for General-Purpose distributed computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi-cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Ca&La (JCL), a distributed-shared-memory and task-oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single-board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

关键词： distributed computing distributed shared memory Java middleware parallel computing task-oriented

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