As computers continue to remain essential tools for the pursuit of physics, medicine, economics, social sciences, and more, supercomputers are proving that they can further extend and greatly enhance as-of-yet undiscovered knowledge and solve the world’s most complex problems. As these instruments continue to lead to groundbreaking discoveries and breakthroughs, it is imperative that research remains up to date with the latest findings and uses. The Handbook of Research on Methodologies and Applications of Supercomputing is a comprehensive and critical reference book that provides research on the latest advances of control flow and dataflow supercomputing and highlights selected emerging big data applications needing high acceleration and/or low power. Consequently, this book advocates the need for hybrid computing, where the control flow part represents the host architecture and dataflow part represents the acceleration architecture. These issues cover the initial eight chapters. The remaining eight chapters cover selected modern applications that are best implemented on a hybrid computer, in which the transactional parts (serial code) are implemented on the control flow part and the loops (parallel code) on the dataflow part. These final eight chapters cover two major application domains: scientific computing and computing for digital economy. This book offers applications in marketing, medicine, energy systems, and library science, among others, and is an essential source for scientists, programmers, engineers, practitioners, researchers, academicians, and students interested in the latest findings and advancements in supercomputing.

Supercomputing is a technology for high-performance computing that makes use of supercomputers. These computers are made up of interconnects, input-output systems, memory and processor cores. They have more than one CPU which consists of circuits for deciphering program instructions and executing the arithmetic and logic operations in a proper order. Another characteristic feature of supercomputers is their use of vector arithmetic. This feature provides them with the ability to operate on pairs of lists of numbers rather than only on pairs of numbers. Supercomputers are primarily used for scientific and engineering work that requires exceedingly high-speed computations. Some applications of supercomputers include weather forecasting, climate research, oil and gas exploration, molecular modeling, physical simulation, and cryptology. This book is compiled in such a manner, that it will provide in-depth knowledge about the theory and practice of supercomputing. The readers would gain knowledge that would broaden their perspective about this area of study.

This book constitutes the refereed proceedings of the 4th Russian Supercomputing Days, RuSCDays 2018, held in Moscow, Russia, in September 2018. The 59 revised full papers and one revised short paper presented were carefully reviewed and selected from 136 submissions. The papers are organized in topical sections on parallel algorithms; supercomputer simulation; high performance architectures, tools and technologies.

This guide provides a comprehensive overview of High Performance Computing (HPC) to equip students with a full skill set including cluster setup, network selection, and a background of supercomputing competitions. It covers the system, architecture, evaluating approaches, and other practical supercomputing techniques. As the world’s largest supercomputing hackathon, the ASC Student Supercomputer Challenge has attracted a growing number of new talent to supercomputing and has greatly promoted communications in the global HPC community. Enclosed in this book, readers will also find how to analyze and optimize supercomputing systems and applications in real science and engineering cases.

Data Intensive Computing refers to capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies. The challenge of data intensive computing is to provide the hardware architectures and related software systems and techniques which are capable of transforming ultra-large data into valuable knowledge. Handbook of Data Intensive Computing is written by leading international experts in the field. Experts from academia, research laboratories and private industry address both theory and application. Data intensive computing demands a fundamentally different set of principles than mainstream computing. Data-intensive applications typically are well suited for large-scale parallelism over the data and also require an extremely high degree of fault-tolerance, reliability, and availability. Real-world examples are provided throughout the book. Handbook of Data Intensive Computing is designed as a reference for practitioners and researchers, including programmers, computer and system infrastructure designers, and developers. This book can also be beneficial for business managers, entrepreneurs, and investors.

Grid technologies are rising with the next generation of Internet by defining a powerful computing paradigm. Grid could be used as a technology ‘glue’ providing users with a uniform way to access resources by means of several devices. These technologies can provide a support for Technology Enhanced Learning (TEL) by enabling new learning environments based on collaboration, real direct experience, personalization, ubiquity, accessibility and contextualization. Nevertheless, to be effectively used in TEL, Grid must be complemented with other elements like semantics and educational modelling; leading to the concept of ‘Learning Grid’ as defined in the homonymous Special Interest Group (SIG) of the European Network of Excellence ‘Kaleidoscope: Shaping the Scientific Evolution of Technology Enhanced Learning’. The key challenge that Kaleidoscope is facing is the scientific and structural integration of the European TEL research. In this context, the Learning Grid SIG aims at contributing to the achievement of an improvement in TEL practices through the definition of open, distributed and pervasive environments for effective human learning taking into account that effective learning requires an active attitude of learners and that learning is a social and collaborative activity so requiring a technology that allows for active and realistic experiments, personalization, knowledge creation and evolution, as well as autonomous and dynamic creation of communities. The first section of the book is about the concept of Learning Grid and related technologies. The second chapter analyses and compares existing languages for the dynamic composition of distributed learning resources and services in a Learning Grid.

The ability of parallel computing to process large data sets and handle time-consuming operations has resulted in unprecedented advances in biological and scientific computing, modeling, and simulations. Exploring these recent developments, the Handbook of Parallel Computing: Models, Algorithms, and Applications provides comprehensive coverage on a

"This book presents, discusses, shares ideas, results and experiences on the recent important advances and future challenges on enabling technologies for achieving higher performance"--Provided by publisher.

The widespread use of object-oriented languages and Internet security concerns are just the beginning. Add embedded systems, multiple memory banks, highly pipelined units operating in parallel, and a host of other advances and it becomes clear that current and future computer architectures pose immense challenges to compiler designers-challenges th

When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chap