Contemporary High Performance Computing: From Petascale toward Exascale, Volume 3 focuses on the ecosystems surrounding the world’s leading centers for high performance computing (HPC). It covers many of the important factors involved in each ecosystem: computer architectures, software, applications, facilities, and sponsors. This third volume will be a continuation of the two previous volumes, and will include other HPC ecosystems using the same chapter outline: description of a flagship system, major application workloads, facilities, and sponsors. Features: Describes many prominent, international systems in HPC from 2015 through 2017 including each system’s hardware and software architecture Covers facilities for each system including power and cooling Presents application workloads for each site Discusses historic and projected trends in technology and applications Includes contributions from leading experts Designed for researchers and students in high performance computing, computational science, and related areas, this book provides a valuable guide to the state-of-the art research, trends, and resources in the world of HPC.

This book constitutes the revised selected papers of the 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, held in Oak Ridge, TN, USA*, in October 2021. The 33 full papers and 3 short papers presented were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; advanced computing applications: use cases that combine multiple aspects of data and modeling; advanced computing systems and software: connecting instruments from edge to supercomputers; deploying advanced computing platforms: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

This book highlights new developments in the teaching and learning of algebraic thinking with 5- to 12-year-olds. Based on empirical findings gathered in several countries on five continents, it provides a wealth of best practices for teaching early algebra. Building on the work of the ICME-13 (International Congress on Mathematical Education) Topic Study Group 10 on Early Algebra, well-known authors such as Luis Radford, John Mason, Maria Blanton, Deborah Schifter, and Max Stephens, as well as younger scholars from Asia, Europe, South Africa, the Americas, Australia and New Zealand, present novel theoretical perspectives and their latest findings. The book is divided into three parts that focus on (i) epistemological/mathematical aspects of algebraic thinking, (ii) learning, and (iii) teaching and teacher development. Some of the main threads running through the book are the various ways in which structures can express themselves in children’s developing algebraic thinking, the roles of generalization and natural language, and the emergence of symbolism. Presenting vital new data from international contexts, the book provides additional support for the position that essential ways of thinking algebraically need to be intentionally fostered in instruction from the earliest grades.

This open access book explains the state of the art in quantum software engineering and design, independent from a specific hardware. It deals with quantum software theoretical aspects and with classical software engineering concepts like agile development approaches, validation, measurement, and deployment applied in a quantum or hybrid environment, and is complemented by a number of various industry applications. After an introductory chapter overviewing the contents of the subsequent chapters, the book is composed of three parts. It starts with a theoretical part on quantum software, as a bold declaration that quantum software theory is deep and valuable independent from the existence of specific quantum hardware. It is based upon the claim that quantum software is the more general theory subsuming classical and hybrid software system theories. The second, more extensive part deals with quantum software system and engineering design. Its quality follows from the comparison of the broad diversity of sometimes conflicting views. Moreover, the variety of approaches to design, enable the reader to make a well-pondered rational choice of preference. The book concludes with a third part, referring to multiple software applications and corresponding laboratory experiences, in order to understand their implications in practice and avoid repeating past mistakes. This book is of interest to industry professionals and researchers in academia, which are either producing or applying quantum software systems in their work or are considering their potential utility in the future. Furthermore, it also could be beneficial for practitioners already experienced with classical software engineering who desire to understand the fundamentals or possible applications of quantum software.

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.

The Grid computing concept, which allows users to integrate administratively and g- graphically dispersed computing resources, has been gaining traction in a number of application areas during the past few years. By interconnecting many – heterogeneous, though usually virtualized – computing resources, virtual computer centers or superc- puters can be created, providing a seamless supply of computing resources. Grid comp- ing provides benefits not only for scientific computing (e.g., SETI@home, which interconnects one million computers across 226 countries with a total processing power of 711 TFLOPS) but also in a commercial environment. It is projected that computing Grids can lower the total IT costs of businesses by 30%. The report “Grid Computing: A Vertical Market Perspective 2005–2010” (by The Insight Research Corporation) estimates an increase of worldwide Grid spending from $714.9 million in 2005 to approximately $19.2 billion in 2010. One of the most prominent activities in academia is the EGEE project being funded with 30 MEuro by the European Commission. EGEE brings together researchers from over 27 countries with the common aim of developing a service Grid infrastructure, which is suited for scientific computing with very high demand for processing power.