High Performance Computing: Programming and Applications presents techniques that address new performance issues in the programming of high performance computing (HPC) applications. Omitting tedious details, the book discusses hardware architecture concepts and programming techniques that are the most pertinent to application developers for achievi
Industrial Applications of High-Performance Computing: Best Global Practices offers a global overview of high-performance computing (HPC) for industrial applications, along with a discussion of software challenges, business models, access models (e.g., cloud computing), public-private partnerships, simulation and modeling, visualization, big data a
High Performance Computing is an integrated computing environment for solving large-scale computational demanding problems in science, engineering and business. Newly emerging areas of HPC applications include medical sciences, transportation, financial operations and advanced human-computer interface such as virtual reality. High performance computing includes computer hardware, software, algorithms, programming tools and environments, plus visualization. The book addresses several of these key components of high performance technology and contains descriptions of the state-of-the-art computer architectures, programming and software tools and innovative applications of parallel computers. In addition, the book includes papers on heterogeneous network-based computing systems and scalability of parallel systems. The reader will find information and data relative to the two main thrusts of high performance computing: the absolute computational performance and that of providing the most cost effective and affordable computing for science, industry and business. The book is recommended for technical as well as management oriented individuals.
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.
This book constitutes revised selected papers of the 7th Latin American High Performance Computing Conference, CARLA 2020, held in Cuenca, Ecuador, in September 2020. Due to the COVID-19 pandemic the conference was held in a virtual mode. The 15 revised full papers presented were carefully reviewed and selected out of 36 submissions. The papers included in this book are organized according to the topics on High Performance Computing Applications; High Performance Computing and Artificial Intelligence.
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.
In this fast-paced global economy, academia and industry must innovate to evolve and succeed. Today’s researchers and industry experts are seeking transformative technologies to meet the challenges of tomorrow. Cutting-edge technological advances in cybersecurity solutions aid in enabling the security of complex heterogeneous high-performance computing (HPC) environments. On the other hand, HPC facilitates powerful and intelligent innovative models for reducing time to response to identify and resolve a multitude of potential, newly emerging cyberattacks. Cybersecurity and High-Performance Computing Environments provides a collection of the current and emergent research innovations, practices, and applications focusing on the interdependence of cybersecurity and HPC domains for discovering and resolving new emerging cyber-threats. KEY FEATURES Represents a substantial research contribution to the state-of-the-art solutions for addressing the threats to confidentiality, integrity, and availability (CIA triad) in HPC environments Covers the groundbreaking and emergent solutions that utilize the power of the HPC environments to study and understand the emergent, multifaceted, anomalous, and malicious characteristics The content will help university students, researchers, and professionals understand how HPC research fits broader cybersecurity objectives and vice versa.
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.
"This book provides insight into the current trends and emerging issues by investigating grid and cloud evolution, workflow management, and the impact new computing systems have on the education fields as well as the industries"--Provided by publisher.
Every day we need to solve large problems for which supercomputers are needed. High performance computing (HPC) is a paradigm that allows to efficiently implement large-scale computational tasks on powerful supercomputers unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many challenging real world problems arising in engineering, economics, medicine and other areas can be formulated as large-scale computational tasks. The volume is a comprehensive collection of extended contributions from the High performance computing conference held in Borovets, Bulgaria, September 2019. This book presents recent advances in high performance computing. The topics of interest included into this volume are: HP software tools, Parallel Algorithms and Scalability, HPC in Big Data analytics, Modelling, Simulation & Optimization in a Data Rich Environment, Advanced numerical methods for HPC, Hybrid parallel or distributed algorithms. The volume is focused on important large-scale applications like Environmental and Climate Modeling, Computational Chemistry and Heuristic Algorithms.