high performance computing for big data

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Conquering Big Data With High Performance Computing

Author : Ritu Arora
ISBN : 9783319337425
Genre : Computers
File Size : 24. 75 MB
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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.

High Performance Computing For Big Data

Author : Chao Wang
ISBN : 9781351651578
Genre : Computers
File Size : 58. 99 MB
Format : PDF
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High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.

New Frontiers In High Performance Computing And Big Data

Author : G. Fox
ISBN : 9781614998167
Genre : Computers
File Size : 88. 66 MB
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For the last four decades, parallel computing platforms have increasingly formed the basis for the development of high performance systems primarily aimed at the solution of intensive computing problems, and the application of parallel computing systems has also become a major factor in furthering scientific research. But such systems also offer the possibility of solving the problems encountered in the processing of large-scale scientific data sets, as well as in the analysis of Big Data in the fields of medicine, social media, marketing, economics etc. This book presents papers from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2016. The workshop covered a wide range of topics and new developments related to the solution of intensive and large-scale computing problems, and the contributions included in this volume cover aspects of the evolution of parallel platforms and highlight some of the problems encountered with the development of ever more powerful computing systems. The importance of future large-scale data science applications is also discussed. The book will be of particular interest to all those involved in the development or application of parallel computing systems.

Big Data And High Performance Computing

Author : L. Grandinetti
ISBN : 9781614995838
Genre : Computers
File Size : 38. 38 MB
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Big Data has been much in the news in recent years, and the advantages conferred by the collection and analysis of large datasets in fields such as marketing, medicine and finance have led to claims that almost any real world problem could be solved if sufficient data were available. This is of course a very simplistic view, and the usefulness of collecting, processing and storing large datasets must always be seen in terms of the communication, processing and storage capabilities of the computing platforms available. This book presents papers from the International Research Workshop, Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2014. The papers selected for publication here discuss fundamental aspects of the definition of Big Data, as well as considerations from practice where complex datasets are collected, processed and stored. The concepts, problems, methodologies and solutions presented are of much more general applicability than may be suggested by the particular application areas considered. As a result the book will be of interest to all those whose work involves the processing of very large data sets, exascale computing and the emerging fields of data science

Big Data And Hpc Ecosystem And Convergence

Author : L. Grandinetti
ISBN : 9781614998822
Genre : Computers
File Size : 51. 95 MB
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Due to the increasing need to solve complex problems, high-performance computing (HPC) is now one of the most fundamental infrastructures for scientific development in all disciplines, and it has progressed massively in recent years as a result. HPC facilitates the processing of big data, but the tremendous research challenges faced in recent years include: the scalability of computing performance for high velocity, high variety and high volume big data; deep learning with massive-scale datasets; big data programming paradigms on multi-core; GPU and hybrid distributed environments; and unstructured data processing with high-performance computing. This book presents 19 selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale era, held in Tehran, Iran, in April 2017. The book is divided into 3 sections: State of the Art and Future Scenarios, Big Data Challenges, and HPC Challenges, and will be of interest to all those whose work involves the processing of Big Data and the use of HPC.

Cloud Computing And Big Data

Author : C. Catlett
ISBN : 9781614993223
Genre : Computers
File Size : 39. 39 MB
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Cloud computing offers many advantages to researchers and engineers who need access to high performance computing facilities for solving particular compute-intensive and/or large-scale problems, but whose overall high performance computing (HPC) needs do not justify the acquisition and operation of dedicated HPC facilities. There are, however, a number of fundamental problems which must be addressed, such as the limitations imposed by accessibility, security and communication speed, before these advantages can be exploited to the full. This book presents 14 contributions selected from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in June 2012. The papers are arranged in three chapters. Chapter 1 includes five papers on cloud infrastructures, while Chapter 2 discusses cloud applications. The third chapter in the book deals with big data, which is nothing new – large scientific organizations have been collecting large amounts of data for decades – but what is new is that the focus has now broadened to include sectors such as business analytics, financial analyses, Internet service providers, oil and gas, medicine, automotive and a host of others. This book will be of interest to all those whose work involves them with aspects of cloud computing and big data applications.

High Performance Computing With Remotely Sensed Spatial Big Data Using The Many Core Graphics Processing Unit Gpu

Author : Feng Ni (Ph.D.)
ISBN : OCLC:1038717021
Genre : Big data
File Size : 30. 7 MB
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The advances in remote sensing technologies have led to the dramatic enlargement of the spatial data repositories and the formation of remotely sensed Spatial Big Data, which include hyperspectral imagery with hundreds of spectral bands, hyper-spatial imagery with sub-meter pixel sizes, and LiDAR (Light Detection And Ranging) data with dense 3D point cloud, and massive waveform curves. The traditional serial computing paradigm became extremely or prohibitively time-consuming when involving complex spatial analysis algorithms and large volumes of remotely sensed Spatial Big Data. High-performance computing (HPC) based on parallel processors, especially the many-core Graphics Processing Unit (GPU), is beginning to be utilized in many research areas involving data-intensive and computationally complex tasks. However, the GPU-based parallel processing of remotely sensed Spatial Big Data is still at its infantry. Most of the current research is mostly based on ad hoc parallelization solutions, and the comprehensive parallelization strategy for different kinds of spatial analysis is still missing. The size of the data that can be processed in these studies was still relatively small, not considered to be real Spatial Big Data. To overcome these limitations, a comprehensive parallelization strategy for different kinds of spatial analysis with remotely sensed spatial data of different sizes and types was proposed in this research. Specifically, data partitioning strategies inside GPU for the three major types of spatial analysis including local, focal and zonal analysis were designed to utilize the parallel computing power of the many-core GPU; data partitioning strategies outside GPU were designed to break the resource limitation of the many-core GPU; data referencing approaches were provided to efficiently partition regularly spaced and irregularly spaced spatial data. Case studies involving different types of spatial analysis and different types of remotely sensed data were then conducted to demonstrate the algorithm implementation with the proposed parallelization strategy. By running the developed parallel algorithms on a many-core GPU, the computational efficiency of each case study was greatly improved with a computing speedup ranging from 10X to 40X over the serial algorithms. The parallel programs are able to handle data with a size much bigger than the size of the GPU memory, as long as it can be stored on the local hard disks. While bigger data were used, the computing speedup of parallel algorithms was increased up to 72X, which suggest that large datasets like Spatial Big Data should be used for parallel processing to take full advantage of the computing power of the many-core GPU.

High Performance Computing In Finance

Author : M. A. H. Dempster
ISBN : 9781482299670
Genre : Computers
File Size : 44. 57 MB
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High-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computing– that can be used without much expertise and expense – to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Wave’s quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems.

Introduction To Hpc With Mpi For Data Science

Author : Frank Nielsen
ISBN : 9783319219035
Genre : Computers
File Size : 30. 76 MB
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This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions. Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters. In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics. Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. This part closes with a concise introduction to data core-sets that let big data problems be amenable to tiny data problems. Exercises are included at the end of each chapter in order for students to practice the concepts learned, and a final section contains an overall exam which allows them to evaluate how well they have assimilated the material covered in the book.

High Performance Big Data Analytics

Author : Pethuru Raj
ISBN : 9783319207445
Genre : Computers
File Size : 58. 57 MB
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This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.

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