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3rd International Conference on Software and Data Technologies

作者： Vladoiu, Monica Constantinescu, Zoran PG Univ Ploiesti Romania Dept Informat Bd Bucuresti 39 Ploiesti Romania ZealSoft Ltd Bucharest Romania

ISBN: (纸本)9789898111517

In this paper we first present briefly QADPZ, an open source platform for heterogeneous desktop grid computing, which enables users from a local network (organization-wide) or Internet (volunteer computing) to share their resources. Users of the system can submit compute-intensive applications to the system, which are then automatically scheduled for execution. The scheduling is made based on the hardware and software requirements of the application. Users can later monitor and control the execution of the applications. Each application consists of one or more tasks. Applications can be independent, when the composing tasks do not require any interaction, or parallel, when the tasks communicate with each other during the computation. QADPZ uses a master worker-model that is improved with some refined capabilities: push of work units, pipelining, sending more work-units at a time, adaptive number of workers, adaptive timeout interval for work units, and use of multithreading, to be presented further in this paper. These improvements are meant to increase the performance and efficiency of such applications.

关键词： grid computing desktop grid computing distributed and parallel computing master worker paradigm

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Implementation of Message Passing Interface in MANETs in Processing of Big Data

Implementation of Message Passing Interface in MANETs in Pro...

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International Conference on Computers, Communications and Systems (ICCCS)

作者： Sinha, Vishal Raj, Tilak Guru Tegh Bahadur Inst Technol Comp Sci & Engn Dept New Delhi India

ISBN: (纸本)9781467397544

For many years, data mining has been one of the most important tools in the field of research. The mere reason for the rapid growth in availability of raw data is due to cheap information-sensing devices that are readily available. Big data is a term used to define data sets that are comparatively very big and complex like user database of Facebook, and require a lot of resources to process. MANETs stands for Mobile Ad-hoc Networks and is a comparatively new technology and studies have been in progress to make it a more reliable technology. Message passing interface is a basic portable message-passing interface designed to function on several computers at once. It is regularly integrated where there is the need of high speed computing. This paper describes the use of Message Passing Interface which is in preference to MapReduce in Mobile Ad-Hoc Networks to process large and complex data. Here each node in the given network can use partially reserved CPU cycles and the other nodes in the network are used to carry out processing of data in an affordable manner using various architectures of distributed computing. This allows smaller organizations to gather refined information in a much easier way and that too without the need of huge amounts of server to do the same.

关键词： Mobile Ad-hoc Networks (MANETs) Message Passing Interface (MPI) MapReduce Big Data distributed and parallel computing

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On the Design of a Blockchain Platform for Clinical Trial and Precision Medicine 37

On the Design of a Blockchain Platform for Clinical Trial an...

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37th IEEE International Conference on distributed computing Systems (ICDCS)

作者： Shae, Zonyin Tsai, Jeffrey K. Q. Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung Taiwan

ISBN: (纸本)9781538617915

This paper proposes a blockchain platform architecture for clinical trial and precision medicine and discusses various design aspects and provides some insights in the technology requirements and challenges. We identify 44 new system architecture components that are required to be built on top of traditional blockchain and discuss their technology challenges in our blockchain platform: (a) a new blockchain based general distributed and parallel computing paradigm component to devise and study parallel computing methodology for big data analytics, (b) blockchain application data management component for data integrity, big data integration, and integrating disparity of medical related data, (c) verifiable anonymous identity management component for identity privacy for both person and Internet of Things (IoT) devices and secure data access to make possible of the patient centric medicine, and (d) trust data sharing management component to enable a trust medical data ecosystem for collaborative research.

关键词： blockchain clinical trial precision medicine data integrity data integration identity privacy big data analytics distributed and parallel computing patient centric medicine IoT

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A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023年 229卷 107270-107270页

作者： Zhou, Yin Luo, Binghui Sang, Jiugao Li, Cheng Zhu, Meng Zhu, Zhengfei Dai, Jianrong Wang, Jianhua Chen, Haibo Zhai, Shuwei Lu, Lina Liu, Hui Yu, Genhua Ye, Jin Zhang, Zhen Huan, Jian Evidance Med Technol Inc Suzhou Peoples R China Nanjing Med Univ Affiliated Suzhou Sci & Technol Town Hosp Dept Radiat Oncol Suzhou Peoples R China Li Huili Hosp Ningbo Med Ctr Ningbo Peoples R China Homol Med Technol Inc Ningbo Peoples R China Fudan Univ Shanghai Canc Ctr Dept Radiat Oncol Shanghai Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Canc Hosp Natl Canc Ctr Dept Radiat Oncol Beijing Peoples R China Rudong Cty Peoples Hosp Dept Radiat Oncol Nantong Rudong Peoples R China Sun Yat sen Univ Dept Radiat Oncol State Key Lab Oncol South China Canc Ctr Guangzhou Peoples R China Zhebei Mingzhou Hosp Dept Radiat Oncol Huzhou Peoples R China

Purpose: This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the *** and Materials: A cloud-based consultation and collaboration system for radiotherapy, OncoEvid-ance (R), was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was de-ployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiother-apy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated ***: All the passing rates of per-beam dose verification are > 94% and all the passing rates of com-posite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification. Conclusion: The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.(c) 2022 Published by

关键词： Cloud-based system Radiotherapy Radiation treatment planning Remote services distributed and parallel computing Consultation Collaboration Decision support Community radiotherapy centers OncoEvidance?

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parallel and scalable Dunn Index for the validation of big data clusters

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parallel computing 2021年 102卷 102751-102751页

作者： Ben Ncir, Chiheb-Eddine Hamza, Abdallah Bouaguel, Waad Univ Jeddah Coll Business Jeddah Saudi Arabia Univ Tunis LARODEC Lab ISG Tunis Tunisia

parallelizing data clustering algorithms has attracted the interest of many researchers over the past few years. Many efficient parallel algorithms were proposed to build partitioning over a huge volume of data. The effectiveness of these algorithms is attributed to the distribution of data among a cluster of nodes and to the parallel computation models. Although the effectiveness of parallel models to deal with increasing volume of data little work is done on the validation of big clusters. To deal with this issue, we propose a parallel and scalable model, referred to as S-DI (Scalable Dunn Index), to compute the Dunn Index measure for an internal validation of clustering results. Rather than computing the Dunn Index on a single machine in the clustering validation process, the new proposed measure is computed by distributing the partitioning among a cluster of nodes using a customized parallel model under Apache Spark framework. The proposed S-DI is also enhanced by a Sketch and Validate sampling technique which aims to approximate the Dunn Index value by using a small representative data-sample. Different experiments on simulated and real datasets showed a good scalability of our proposed measure and a reliable validation compared to other existing measures when handling large scale data.

关键词： distributed and parallel computing Spark framework Sampling Big data analysis Clustering validity Dunn Index

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distributed Model Checking: From Abstract Algorithms to Concrete Implementations

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Electronic Notes in Theoretical Computer Science 2003年第1期89卷 114-127页

作者： Christophe Joubert

distributed Model Checking ( DMC ) is based on several distributed algorithms, which are often complex and error prone. In this paper, we consider one fundamental aspect of DMC design: message passing communication, the implementation of which presents hidden tradeoffs often dismissed in DMC related literature. We show that, due to such communication models, high level abstract DMC algorithms might face implicit pitfalls when implemented concretely. We illustrate our discussion with a generic distributed state space generation algorithm.

关键词： distributed and parallel computing communication paradigms message passing state space generation model checking

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A distributed parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

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SENSORS 2018年第11期18卷 3627-3627页

作者： Zhang, Yi Wu, Zebin Sun, Jin Zhang, Yan Zhu, Yaoqin Liu, Jun Zang, Qitao Plaza, Antonio Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Lianyungang E Port Informat Dev Co Ltd Lianyungang 222042 Peoples R China Univ Extremadura Dept Technol Computers & Commun Hyperspectral Comp Lab Caceres 10003 Spain

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA's efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

关键词： hyperspectral images anomaly detection distributed and parallel computing apache spark clouds

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Fast American Basket Option Pricing on a multi-GPU Cluster 14

Fast American Basket Option Pricing on a multi-GPU Cluster

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Simulation Multi-Conference

作者： Michael Benguigui Francoise Baude INRIA Sophia-Antipolis Méditerranée University of Nice Sophia-Antipolis

ISBN: (纸本)9781632662163

This article presents a multi-GPU adaptation of a specific Monte Carlo and classification based method for pricing American basket options, due to Picazo. The first part relates how to combine fine and coarse-grained parallelization to price American basket options. A dynamic strategy of kernel calibration is proposed. Doing so, our implementation on a reasonable size (18) GPU cluster achieves the pricing of a high dimensional (40) option in less than one hour against almost 8 as observed for runs we conducted in the past, using a 64-core cluster (composed of quad-core AMD Opteron 2356). In order to benefit from different GPU device types, we detail the dynamic strategy we have used to load balance GPU calculus which greatly improves the overall pricing time we obtained. An analysis of possible bottleneck effects demonstrates that there is a sequential bottleneck due to the training phase that relies upon the AdaBoost classification method, which prevents the implementation to be fully scalable, and so prevents to envision further decreasing pricing time down to handful of minutes. For this we propose to consider using Random Forests classification method: it is naturally dividable over a cluster, and available like AdaBoost as a black box from the popular Weka machine learning library. However our experimental tests will show that its use is costly.

关键词： distributed and parallel computing Cluster GPU OpenCL Machine learning Mathematical finance Option pricing

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