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Spark-based Solution of Large Linear...

Al-Farsiyah, Salha Mohammed Issa,

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Spark-based Solution of Large Linear Systems for Big Data Processing = = حل قائم على Spark للأنظمة الخطية الكبيرة لمعالجة البيانات الضخمة /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Spark-based Solution of Large Linear Systems for Big Data Processing =/ Salha Mohammed Issa Al-Farsiyah.
Reminder of title:	حل قائم على Spark للأنظمة الخطية الكبيرة لمعالجة البيانات الضخمة /
Author:	Al-Farsiyah, Salha Mohammed Issa,
Description:	1 electronic resource (68 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-10.
Contained By:	Masters Abstracts International86-10.
Subject:	Iterative methods. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31962259
ISBN:	9798311984614

Spark-based Solution of Large Linear Systems for Big Data Processing = = حل قائم على Spark للأنظمة الخطية الكبيرة لمعالجة البيانات الضخمة /
Al-Farsiyah, Salha Mohammed Issa,

Spark-based Solution of Large Linear Systems for Big Data Processing =حل قائم على Spark للأنظمة الخطية الكبيرة لمعالجة البيانات الضخمة /Salha Mohammed Issa Al-Farsiyah. - 1 electronic resource (68 pages)

Source: Masters Abstracts International, Volume: 86-10.

Big data is a collection of large datasets that cannot be processed using conventional data management methods. In order to process these large amounts of data in an efficient way, parallelism is used. The scale, diversity, and complexity of the big data require new parallel computing architectures and techniques. Among the parallel computing frameworks, Spark enables parallel processing of data on collections of commodity computing nodes without the need to handle the complexity of designing and implementing parallel programs. Spark is an efficient in-memory cluster computing technology which offers high scalability and fault-tolerance capabilities. Furthermore, it enables to better handle iterative tasks as a result of reading and writing the data from memory (using the caching feature of Spark through the iterations) rather than reading and writing from disk.Solving very large systems of linear equations is needed in many applications. There are several well-known methods for solving such large systems of linear equations including the Jacobi method. This iterative method has been used to solve systems of linear equations with thousands of unknown variables in different areas such as the area of machine learning and climate science. As compared to other frameworks such as Hadoop, Spark offers the promise of being able to solve large-scale linear systems efficiently using iterative methods (such as Jacobi) due to its data persistence/caching feature.In this project we put this hypothesis to the test by implementing the Jacobi method for solving large systems of linear equations using Spark on a commodity cluster of computers and evaluate its performance. The aim is to show the effectiveness of the Spark in solving important big data problems in a distributed system environment. The performance evaluation results show that the Jacobi method on Spark can achieve super-linear speedup due to the ability of the Spark cluster to cache large amounts of data across the cluster nodes. The results also reveal that Jacobi on Spark achieves substantially higher speedup and efficiency for very large matrices (of size 4000x4000 or higher). Our results compare favourably to the results obtained in other projects which implemented iterative methods on Hadoop. The Jacobi method on Spark v achieved much higher (7 times higher) efficiency than the Jacobi method on Hadoop MapReduce for large matrix size due to the caching feature offered by Spark which is not available on Hadoop MapReduce.

English

ISBN: 9798311984614Subjects--Topical Terms:

3686120
Iterative methods.

Spark-based Solution of Large Linear Systems for Big Data Processing = = حل قائم على Spark للأنظمة الخطية الكبيرة لمعالجة البيانات الضخمة /
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264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 1 electronic resource (68 pages)
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500 $a Source: Masters Abstracts International, Volume: 86-10.
500 $a Advisors: Day, Khaled.
502 $b M.S. $c Sultan Qaboos University (Oman) $d 2020.
520 $a Big data is a collection of large datasets that cannot be processed using conventional data management methods. In order to process these large amounts of data in an efficient way, parallelism is used. The scale, diversity, and complexity of the big data require new parallel computing architectures and techniques. Among the parallel computing frameworks, Spark enables parallel processing of data on collections of commodity computing nodes without the need to handle the complexity of designing and implementing parallel programs. Spark is an efficient in-memory cluster computing technology which offers high scalability and fault-tolerance capabilities. Furthermore, it enables to better handle iterative tasks as a result of reading and writing the data from memory (using the caching feature of Spark through the iterations) rather than reading and writing from disk.Solving very large systems of linear equations is needed in many applications. There are several well-known methods for solving such large systems of linear equations including the Jacobi method. This iterative method has been used to solve systems of linear equations with thousands of unknown variables in different areas such as the area of machine learning and climate science. As compared to other frameworks such as Hadoop, Spark offers the promise of being able to solve large-scale linear systems efficiently using iterative methods (such as Jacobi) due to its data persistence/caching feature.In this project we put this hypothesis to the test by implementing the Jacobi method for solving large systems of linear equations using Spark on a commodity cluster of computers and evaluate its performance. The aim is to show the effectiveness of the Spark in solving important big data problems in a distributed system environment. The performance evaluation results show that the Jacobi method on Spark can achieve super-linear speedup due to the ability of the Spark cluster to cache large amounts of data across the cluster nodes. The results also reveal that Jacobi on Spark achieves substantially higher speedup and efficiency for very large matrices (of size 4000x4000 or higher). Our results compare favourably to the results obtained in other projects which implemented iterative methods on Hadoop. The Jacobi method on Spark v achieved much higher (7 times higher) efficiency than the Jacobi method on Hadoop MapReduce for large matrix size due to the caching feature offered by Spark which is not available on Hadoop MapReduce.
520 $a اللخصس:الضخمة عبارة عن مجموعة من البيانات الكبيرة التي لا تمكن مهالتبا باستخدام طرق إدارة البيانات التقليدية. من أجل ممالحة هذه الكميات الكبية من البيانات بطريقة فمالة ؛ يتم استخدام التوازي. يتطلب جم البيانات الضخمة وتنوعها وتمقيدها بنى وتقنيات حوسية متوازية جديدة. من بين أطر الحوسبة المتوازية ؛ يقيح عاددم8 المعالجة التوازية للبيانات على عبموعات من أجهزة الحاسوب دون الحاجة إلى التعامل مع تصميم مسقد لتنفيذ البراج الموازية. عاددر5 هي تقنية حوسبة جماعية فعالة في الذاكرة توفر قابلية تطوير عالية وقدرات تحمل الأخطاء. علاوة على ذلك ؛ فإنه جمكن من التمامل بشكل أفضل مح المهام التكرارية نتيجة لقراءة البيانات وكتابتها من الذاكرة «باستخدام ميزة التخزين المؤقت ل ماسر من خلال التكراراتع بدلاً من القراءة والكتابة من القرص.هناك حاجة إلى حل أنظمة كبيرة جدًا من المعادلات الخطية فى العديد من التطبيقات. هثاله العديد من الطرق المعروفة لحل مثل هذه الأنظمة الكبيرة من المعادلات الخطية بما في ذلك علريقة آنادمها. . تم استخدام هذه الطريقة التكرارية لحل أنظمة العادلات اللخطية بآلاف التغيمات في مجالات مختلفة مثل مال التعلم الآبي وعلوم الناخ. بالقارنة مع الألر الأخرى مثل «إدداءداط : تقدم عانددرة وعدًا بالقدرة على حل الأنظمة الخطية واسعة النطاق يكفاءة باستخدام الأساليب التكرارية «مثل أدادمها. > نظرًا ميزة استمرار البيانات / التخزين المؤقت.
546 $a English
590 $a School code: 2545
650 4 $a Iterative methods. $3 3686120
650 4 $a Data processing. $3 680224
650 4 $a Design. $3 518875
650 4 $a Scientific imaging. $3 3560377
650 4 $a Fault tolerance. $3 3561030
650 4 $a Climate science. $3 3828389
650 4 $a Linear equations. $3 3683501
650 4 $a Distributed processing. $3 3680534
690 $a 0389
710 2 $a Sultan Qaboos University (Oman). $e degree granting institution. $3 3824592
720 1 $a Day, Khaled $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-10.
790 $a 2545
791 $a M.S.
792 $a 2020
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31962259