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Evolution of the High Performance Database by Informix Software, Inc Pdf
The database market is exploding and heres the guide to the newest and most exciting trends in the industry. This book is a collection of essays about RDBMS technology from industry gurus, such as Marc Andressen of Netscape, who are in a unique position to know the real deal.
High-Performance Web Databases by Sanjiv Purba Pdf
As Web-based systems and e-commerce carry businesses into the 21st century, databases are becoming workhorses that shoulder each and every online transaction. For organizations to have effective 24/7 Web operations, they need powerhouse databases that deliver at peak performance-all the time. High Performance Web Databases: Design, Development, and
High-Performance Big Data Computing by Dhabaleswar K. Panda,Xiaoyi Lu,Dipti Shankar Pdf
An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.
High-Performance Modelling and Simulation for Big Data Applications by Joanna Kołodziej,Horacio González-Vélez Pdf
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.
High Performance Data Mining by Yike Guo,R.L. Grossman Pdf
High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
High-Performance Web Databases by Sanjiv Purba Pdf
In the last few decades, data management has had to support an ever-expanding range of solutions and technology architectures. Many end-to-end Information Technology solutions in the current environment involve access to the Web and integration with one or more Web sites. This guide gives the best practices in building an end-to-end approach that includes traditional data management considerations and Internet support.
IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences by Dino Quintero,Frank N. Lee,IBM Redbooks Pdf
This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support.
National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Committee on Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science in 2017-2020
Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Committee on Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science in 2017-2020 Publisher : National Academies Press Page : 49 pages File Size : 46,5 Mb Release : 2018-07-31 Category : Computers ISBN : 9780309481892
Opportunities from the Integration of Simulation Science and Data Science by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Committee on Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science in 2017-2020 Pdf
Convergence has been a key topic of discussion about the future of cyberinfrastructure for science and engineering research. Convergence refers both to the combined use of simulation and data-centric techniques in science and engineering research and the possibilities for a single type of cyberinfrastructure to support both techniques. The National Academies of Science, Engineering, and Medicine convened a Workshop on Converging Simulation and Data-Driven Science on May 10, 2018, in Washington, D.C. The workshop featured speakers from universities, national laboratories, technology companies, and federal agencies who addressed the potential benefits and limitations of convergence as they relate to scientific needs, technological capabilities, funding structures, and system design requirements. This publication summarizes the presentations and discussions from the workshop.
High Performance Computing and Communications by Federal Coordinating Council for Science, Engineering, and Technology. Committee on Physical, Mathematical, and Engineering Sciences Pdf
Sustained Simulation Performance 2016 by Michael M. Resch,Wolfgang Bez,Erich Focht,Nisarg Patel,Hiroaki Kobayashi Pdf
The book presents the state of the art in high-performance computing and simulation on modern supercomputer architectures. It explores general trends in hardware and software development, and then focuses specifically on the future of high-performance systems and heterogeneous architectures. It also covers applications such as computational fluid dynamics, material science, medical applications and climate research and discusses innovative fields like coupled multi-physics or multi-scale simulations. The papers included were selected from the presentations given at the 20th Workshop on Sustained Simulation Performance at the HLRS, University of Stuttgart, Germany in December 2015, and the subsequent Workshop on Sustained Simulation Performance at Tohoku University in February 2016.
Development of a Regional Pavement Performance Database for the AASHTO Mechanistic-empiricle [sic] Pavement Design Guide: Validation and local calibration by Swetha Kesiraju Pdf
New Frontiers in High Performance Computing and Big Data by G. Fox,V. Getov,L. Grandinetti Pdf
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.
High-Performance Big-Data Analytics by Pethuru Raj,Anupama Raman,Dhivya Nagaraj,Siddhartha Duggirala Pdf
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.
United States. Congress. House. Committee on Appropriations. Subcommittee on Energy and Water Development
Author : United States. Congress. House. Committee on Appropriations. Subcommittee on Energy and Water Development Publisher : Unknown Page : 1440 pages File Size : 44,8 Mb Release : 2002 Category : Energy development ISBN : LOC:00101900940
Energy and Water Development Appropriations for 2003 by United States. Congress. House. Committee on Appropriations. Subcommittee on Energy and Water Development Pdf
Big Data and High Performance Computing by L. Grandinetti,G.R. Joubert,M. Kunze Pdf
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
