Conquering big data with high performance computing /
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| Imprint: | Switzerland : Springer, ©2016. ©2016 |
|---|---|
| Description: | 1 online resource |
| Language: | English |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11266754 |
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| 245 | 0 | 0 | |a Conquering big data with high performance computing / |c Ritu Arora, editor. |
| 260 | |a Switzerland : |b Springer, |c ©2016. | ||
| 264 | 4 | |c ©2016 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed October 26, 2016). | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Preface; Contents; 1 An Introduction to Big Data, High Performance Computing, High-Throughput Computing, and Hadoop; 1.1 Big Data; 1.2 High Performance Computing (HPC); 1.2.1 HPC Platform; 1.2.2 Serial and Parallel Processing on HPC Platform; 1.3 High-Throughput Computing (HTC); 1.4 Hadoop; 1.4.1 Hadoop-Related Technologies; 1.4.2 Some Limitations of Hadoop and Hadoop-Related Technologies; 1.5 Convergence of Big Data, HPC, HTC, and Hadoop; 1.6 HPC and Big Data Processing in Cloud and at Open-Science Data Centers; 1.7 Conclusion; References. | |
| 505 | 8 | |a 2 Using High Performance Computing for Conquering Big Data2.1 Introduction; 2.2 The Big Data Life Cycle; 2.3 Technologies and Hardware Platforms for Managing the Big Data Life Cycle; 2.4 Managing Big Data Life Cycle on HPC Platforms at Open-Science Data Centers; 2.4.1 TACC Resources and Usage Policies; 2.4.2 End-to-End Big Data Life Cycle on TACC Resources; 2.5 Use Case: Optimization of Nuclear Fusion Devices; 2.5.1 Optimization; 2.5.2 Computation on HPC; 2.5.3 Visualization Using GPUs; 2.5.4 Permanent Storage of Valuable Data; 2.6 Conclusions; References. | |
| 505 | 8 | |a 3 Data Movement in Data-Intensive High Performance Computing3.1 Introduction; 3.2 Node-Level Data Movement; 3.2.1 Case Study: ADAMANT; 3.2.2 Case Study: Energy Cost of Data Movement; 3.3 System-Level Data Movement; 3.3.1 Case Study: Graphs; 3.3.2 Case Study: Map Reduce; 3.4 Center-Level Data Movement; 3.4.1 Case Study: Spider; 3.4.2 Case Study: Gordon and Oasis; 3.5 About the Authors; References; 4 Using Managed High Performance Computing Systems for High-Throughput Computing; 4.1 Introduction; 4.2 What Are We Trying to Do?; 4.2.1 Deductive Computation; 4.2.2 Inductive Computation. | |
| 505 | 8 | |a 4.2.2.1 High-Throughput Computing4.3 Hurdles to Using HPC Systems for HTC; 4.3.1 Runtime Limits; 4.3.2 Jobs-in-Queue Limits; 4.3.3 Dynamic Job Submission Restrictions; 4.3.4 Solutions from Resource Managers and Big Data Research; 4.3.5 A Better Solution for Managed HPC Systems; 4.4 Launcher; 4.4.1 How Launcher Works; 4.4.2 Guided Example: A Simple Launcher Bundle; 4.4.2.1 Step 1: Create a Job File; 4.4.2.2 Step 2: Build a SLURM Batch Script; 4.4.3 Using Various Scheduling Methods; 4.4.3.1 Dynamic Scheduling; 4.4.3.2 Static Scheduling; 4.4.4 Launcher with Intel®Xeon Phi Coprocessors. | |
| 505 | 8 | |a 4.4.4.1 Offload4.4.4.2 Independent Workloads for Host and Coprocessor; 4.4.4.3 Symmetric Execution on Host and Phi; 4.4.5 Use Case: Molecular Docking and Virtual Screening; 4.5 Conclusion; References; 5 Accelerating Big Data Processing on Modern HPC Clusters; 5.1 Introduction; 5.2 Overview of Apache Hadoop and Spark; 5.2.1 Overview of Apache Hadoop Distributed File System; 5.2.2 Overview of Apache Hadoop MapReduce; 5.2.3 Overview of Apache Spark; 5.3 Overview of High-Performance Interconnects and Storage Architecture on Modern HPC Clusters. | |
| 520 | |a This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing. Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop. Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable. | ||
| 650 | 0 | |a High performance computing. |0 http://id.loc.gov/authorities/subjects/sh95008935 | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 7 | |a Systems analysis & design. |2 bicssc | |
| 650 | 7 | |a Algorithms & data structures. |2 bicssc | |
| 650 | 7 | |a Databases. |2 bicssc | |
| 650 | 7 | |a COMPUTERS |x Computer Literacy. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Computer Science. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Hardware |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Information Technology. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a High performance computing. |2 fast |0 (OCoLC)fst00956032 | |
| 655 | 0 | |a Electronic books. | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Arora, Ritu, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 9783319337401 |z 3319337408 |w (OCoLC)945949333 |
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