An all-inclusive survey of the fundamentals of parallel and distributed computing. The use of parallel and distributed computing has increased dramatically over the past few years, giving rise to a variety of projects, implementations, and buzzwords surrounding the subject. Although the areas of parallel and distributed computing have traditionally evolved separately, these models have overlapping goals and characteristics. Parallel and Distributed Computing surveys the models and paradigms in this converging area of parallel and distributed computing and considers the diverse approaches within a common text. Covering a comprehensive set of models and paradigms, the material also skims lightly over more specific details and serves as both an introduction and a survey. Novice readers will be able to quickly grasp a balanced overview with the review of central concepts, problems, and ideas, while the more experienced researcher will appreciate the specific comparisons between models, the coherency of the parallel and distributed computing field, and the discussion of less well-known proposals. Other topics covered include:

• Data parallelism
• Shared-memory programming
• Message passing
• Client/server computing
• Code mobility
• Coordination, object-oriented, high-level, and abstract models
• And much more

Parallel and Distributed Computing is a perfect tool for students and can be used as a foundation for parallel and distributed computing courses. Application developers will find this book helpful to get an overview before choosing a particular programming style to study in depth, and researchers and programmers will appreciate the wealth of information concerning the various areas of parallel and distributed computing.

This textbook is designed as a first book on concurrent programming for computer science undergraduates, and provides a comprehensive introduction to the problems of concurrency. Concurrency is of vital importance in many areas of computer science, particularly in operating systems. It is also increasingly being taught in undergraduate courses. The book builds on the student's familiarity with sequential programming in a high level language, which should make it very accessible to computer science students. The book is concerned mainly with the high level aspects of concurrency, which will be equally applicable to traditional time sliced or more recent truly parallel systems.

This is an introduction to the field of efficient parallel algorithms and to the techniques for efficient parallelisation. It is self-contained and presumes no special knowledge of parallel computers or particular mathematics. The book emphasises designing algorithms within the timeless and abstracted context of a high-level programming language rather than within highly specific computer architectures. This is an approach which concentrates on the essence of algorithmic theory, determining and taking advantage of the inherently parallel nature of certain types of problem. The authors present regularly-used techniques and a range of algorithms which includes some of the more celebrated and well-known. Efficient Parallel Algorithms is targeted at non-specialists who are considering entering the field of parallel algorithms. It will be particularly useful for courses aimed at advanced undergraduate or new postgraduate students of computer science and mathematics.

Games Programming has been written for the personal computer owner who is setting out to write fams programs. It charts the territory which lies beyond the simplistic arcade game and which leads to the more intellectually exciting game moderators and learning programs. This is not another book of program listings, nor does it present game algorithms in isolation from the computer methods which make them work. The aim is to equip the reader with an awareness of modern techniques of structured programming, a robust attitude to computer languages, and a source of valuable 'know how'.

Communicating Process Architecture (CPA) describes an approach to system development that is process-oriented. It makes no great distinction between hardware and software. It has a major root in the theory of Communicating Sequential Processes (CSP). However, the underlying theory is not limited to CSP. The importance of mobility of both channel and process within a network sees integration with ideas from the eth-calculus. Other formalisms are also exploited, such as BSP and MPI. The focus is on sound methods for the engineering of significant concurrent systems, including those that are distributed (across the Internet or within a single chip) and/or software-scheduled on a single execution unit. Traditionally, at CPA, the emphasis has been on theory and practice - developing and applying tools based upon CSP and related theories to build high-integrity systems of significant size. In particular, interest focuses on achieving scalability and security against error. The development of Java, C, and C++, libraries to facilitate secure concurrent programming using 'mainstream' languages has allowed CPA to continue and proliferate. This work continues in support of the engineering of distributed applications. Recently, there has been greater reference to theory and its more direct application to programming systems and languages. In this volume the formal CSP is very well presented. The papers provide a healthy mixture of the academic and commercial, software and hardware, application and infrastructure, which reflects the nature of the discipline.

With the rise of multi-core architecture, parallel programming is an increasingly important topic for software engineers and computer system designers. Written by well-known researchers Larry Snyder and Calvin Lin, this highly anticipated first edition emphasizes the principles underlying parallel computation, explains the various phenomena, and clarifies why these phenomena represent opportunities or barriers to successful parallel programming. Ideal for an advanced upper-level undergraduate course, Parallel Programming supplies enduring knowledge that will outlive the current hardware and software, aiming to inspire future researchers to build tomorrow's solutions. ...the first basic book on the subject that I've ever seen that seems to have the pulse on the true issues of parallelism that are relevant for students. --Alan Edelman, MIT Principles of Parallel Programming is a wonderful book and I plan to use it in our new parallel programming course...---Peiyi Tang, University of Arkansas, Little Rock I like Principles of Parallel Programming] very much for a few specific reasons: it's concise, covers the most relevant topics but does not take thousand pages to do it, it is hands on and it covers...recent developments with multi-core and GPGPU. --Edin Hodzic, Santa Clara University

The two volume set LNCS 7133 and LNCS 7134 constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Applied Parallel and Scientific Computing, PARA 2010, held in Reykjavik, Iceland, in June 2010. These volumes contain three keynote lectures, 29 revised papers and 45 minisymposia presentations arranged on the following topics: cloud computing, HPC algorithms, HPC programming tools, HPC in meteorology, parallel numerical algorithms, parallel computing in physics, scientific computing tools, HPC software engineering, simulations of atomic scale systems, tools and environments for accelerator based computational biomedicine, GPU computing, high performance computing interval methods, real-time access and processing of large data sets, linear algebra algorithms and software for multicore and hybrid architectures in honor of Fred Gustavson on his 75th birthday, memory and multicore issues in scientific computing - theory and praxis, multicore algorithms and implementations for application problems, fast PDE solvers and a posteriori error estimates, and scalable tools for high performance computing.

In modern computer science, there exists no truly sequential computing system; and most advanced programming is parallel programming. This is particularly evident in modern application domains like scientific computation, data science, machine intelligence, etc. This lucid introductory textbook will be invaluable to students of computer science and technology, acting as a self-contained primer to parallel programming. It takes the reader from introduction to expertise, addressing a broad gamut of issues. It covers different parallel programming styles, describes parallel architecture, includes parallel programming frameworks and techniques, presents algorithmic and analysis techniques and discusses parallel design and performance issues. With its broad coverage, the book can be useful in a wide range of courses; and can also prove useful as a ready reckoner for professionals in the field.

Graphs are among the most important abstract data types in computer science, and the algorithms that operate on them are critical to modern life. Graphs have been shown to be powerful tools for modeling complex problems because of their simplicity and generality. Graph algorithms are one of the pillars of mathematics, informing research in such diverse areas as combinatorial optimization, complexity theory, and topology. Algorithms on graphs are applied in many ways in today's world - from Web rankings to metabolic networks, from finite element meshes to semantic graphs. The current exponential growth in graph data has forced a shift to parallel computing for executing graph algorithms. Implementing parallel graph algorithms and achieving good parallel performance have proven difficult. This book addresses these challenges by exploiting the well-known duality between a canonical representation of graphs as abstract collections of vertices and edges and a sparse adjacency matrix representation. This linear algebraic approach is widely accessible to scientists and engineers who may not be formally trained in computer science. The authors show how to leverage existing parallel matrix computation techniques and the large amount of software infrastructure that exists for these computations to implement efficient and scalable parallel graph algorithms. The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance. Graph Algorithms in the Language of Linear Algebra is the first book to cover graph algorithms accessible to engineers and scientists not trained in computer science but having a strong linear algebra background, enabling them to quickly understand and apply graph algorithms. It also covers array-based graph algorithms, showing readers how to express canonical graph algorithms using a highly elegant and efficient array notation and how to tap into the large range of tools and techniques that have been built for matrices and tensors; parallel array-based algorithms, demonstrating with examples how to easily implement parallel graph algorithms using array-based approaches, which enables readers to address much larger graph problems; and array-based theory for analyzing graphs, providing a template for using array-based constructs to develop new theoretical approaches for graph analysis.

This publication contains papers from the Occam User Group (OUG). The main aim of the OUG is to act as an independent forum for the exchange of ideas, results and information in research and development of projects in the area of parallel systems design and programming using various communicating processes oriented approaches for transputer based machines. The papers collected in this volume cover topics such as: methodology of Occam programming, parallel asynchronous algorithms, control of real parallelisms in highly parallel machines of transputers, applications in scientific programming, real-time processing, image processing, etc.

Monitor, log, and trace your cloud applications using the power of AWS' myriad observability tools to ensure the systems you build are resilient Key Features * Implement observability in your cloud applications and systems with the power of AWS * Ensure your customers' satisfaction by identifying and fixing bottlenecks quickly * Learn from the experts to get the best possible insight into AWS' observability solutions Book Description Cloud observability is complex if you're new to the cloud and even if you're an experienced cloud practitioner. Thankfully, the world's most popular cloud provider, AWS, provides multiple tools for identifying performance bottlenecks in modern distributed applications. An Insider's Guide to Observability on AWS will help you use these tools to provide the logging, monitoring, and tracing that your systems need to be as efficient as possible. This comprehensive guide to observability on AWS covers all the bases, taking you from basic observability with CloudWatch, through automated observability, to machine-learning-powered tools such as AWS DevOps Guru, and everything in between. You'll learn how to implement observability in containers, in serverless applications, and for user experience monitoring. This is truly an all-encompassing guide that leaves no stone unturned in its quest to give you the knowledge, skills, and practice to implement observability in your applications from end to end and visualize the results using the wide range of tools provided by AWS. You'll also see some of the guidelines and best practices, such as how the Well-Architected Framework relates to observability. By the end of the book, you will find it easy to implement observability in your applications using AWS' native and managed open source tools. What you will learn * Take metrics from an EC2 instance and visualize them in a dashboard * Conduct distributed tracing using AWS X-Ray * Derive operational metrics using CloudWatch Logs * Achieve observability of containerized applications in ECS and EKS * Use CloudWatch and Lambda Insights to monitor serverless applications * Visualize your insights with Amazon Managed Grafana * Harness the power of the ELK stack with OpenSearch * Scale the observability of applications in complex organizations Who This Book Is For This book is intended for SREs, cloud developers and DevOps engineers using AWS native services and tools as well as open source managed services on AWS to achieve the required observability targets. It will also provide guidance to Solution Architects on achieving operational excellence in adopting cloud observability solutions for the workloads. Readers need to have a basic understanding of AWS cloud fundamentals and different services available on AWS cloud to run their applications like EC2, storage solutions like S3, container solutions like ECS, EKS, etc.

The constantly increasing demand for more computing power can seem impossible to keep up with. However, multicore processors capable of performing computations in parallel allow computers to tackle ever larger problems in a wide variety of applications. This book provides a comprehensive introduction to parallel computing, discussing theoretical issues such as the fundamentals of concurrent processes, models of parallel and distributed computing, and metrics for evaluating and comparing parallel algorithms, as well as practical issues, including methods of designing and implementing shared- and distributed-memory programs, and standards for parallel program implementation, in particular MPI and OpenMP interfaces. Each chapter presents the basics in one place followed by advanced topics, allowing novices and experienced practitioners to quickly find what they need. A glossary and more than 80 exercises with selected solutions aid comprehension. The book is recommended as a text for advanced undergraduate or graduate students and as a reference for practitioners.

In recent years, most applications deal with constraint decision-making systems as problems are based on imprecise information and parameters. It is difficult to understand the nature of data based on applications and it requires a specific model for understanding the nature of the system. Further research on constraint decision-making systems in engineering is required. Constraint Decision-Making Systems in Engineering derives and explores several types of constraint decisions in engineering and focuses on new and innovative conclusions based on problems, robust and efficient systems, and linear and non-linear applications. Covering topics such as fault detection, data mining techniques, and knowledge-based management, this premier reference source is an essential resource for engineers, managers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

In the last few years, courses on parallel computation have been developed and offered in many institutions in the UK, Europe and US as a recognition of the growing significance of this topic in mathematics and computer science. There is a clear need for texts that meet the needs of students and lecturers and this book, based on the author's lecture at ETH Zurich, is an ideal practical student guide to scientific computing on parallel computers working up from a hardware instruction level, to shared memory machines, and finally to distributed memory machines. Aimed at advanced undergraduate and graduate students in applied mathematics, computer science, and engineering, subjects covered include linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and, in some cases, Fortran. This book is also ideal for practitioners and programmers.

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2014, held in Hillsboro, OR, USA, in September 2014. The 25 revised full papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on accelerator programming; algorithms for parallelism; compilers; debugging; vectorization.

DNA microarrays, or biochips, are small glass chips embedded with ordered rows of DNA and by providing a massive parallel platform for data gathering represent a fundamental technical advance in biomedical research. This volume is a comprehensive overview of DNA microarray technology and will be invaluable to any researcher interested in taking advantage of this powerful new technique.

Governments around the world have policies to promote links between industry and academic and government laboratories in order to foster economic growth and innovation in the technology-based industries. Knowledge Frontiers gives new insights into this process and offers an original framework for tracking these interactions. The book shows what 'knowledge' companies want from public sector research, and how they network to get this knowledge in three new and promising fields of advanced technology - biotechnology, engineering ceramics, and parallel computing. The authors first look at some of the background issues - policy issues about links between industry and public sector research; the ways in which science and technology interact in the innovation process; and general developments in each of the technologies examined. They look in more detail at public-private research links in the three areas. They find similarities which point to the general importance to innovation of frontier research in universities, and the need to encourage informal interaction/contact between industrial and public sector researchers. They also find differences between the fields which suggest that the policies to provide research links should be more effectively targeted, as an integral part of the broader objective of fostering 'strategic technologies'. Knowledge Frontiers advances our understanding of the various types of knowledge used in the course of research, design, and development leading to innovation. It is essential reading for those wanting to get to grips with the complex and dynamic realities of the innovation process - be they researchers, managers, or policy makers.

Go beyond connecting services to understand the unique challenges encountered in industrial environments by building Industrial IoT architectures using AWS Purchase of the print or kindle book includes a free eBook in the PDF format Key Features Understand the key components of IoT Architecture and how it applies to Industry 4.0 Walk through extensive examples and solutions across multiple Industries Learn how to collect, process, store, and analyse Industrial IoT data Book DescriptionWhen it comes to using the core and managed services available on AWS for making decisions about architectural environments for an enterprise, there are as many challenges as there are advantages. This Industrial IoT book follows the journey of data from the shop floor to the boardroom, identifying goals and aiding in strong architectural decision-making. You'll begin from the ground up, analyzing environment needs and understanding what is required from the captured data, applying industry standards and conventions throughout the process. This will help you realize why digital integration is crucial and how to approach an Industrial IoT project from a holistic perspective. As you advance, you'll delve into the operational technology realm and consider integration patterns with common industrial protocols for data gathering and analysis with direct connectivity to data through sensors or systems. The book will equip you with the essentials for designing industrial IoT architectures while also covering intelligence at the edge and creating a greater awareness of the role of machine learning and artificial intelligence in overcoming architectural challenges. By the end of this book, you'll be ready to apply IoT directly to the industry while adapting the concepts covered to implement AWS IoT technologies. What you will learn Discover Industrial IoT best practices and conventions Understand how to get started with edge computing Define and build IoT solution architectures from scratch Use AWS as the core of your solution platform Apply advanced analytics and machine learning to your data Deploy edge processing to react in near real time to events within your environment Who this book is forThis book is for architects, engineers, developers, and technical professionals interested in building an edge and cloud-based Internet of Things ecosystem with a focus on industry solutions. Since the focus of this book is specifically on IoT, a solid understanding of core IoT technologies and how they work is necessary to get started. If you are someone with no hands-on experience, but are familiar with the subject, you'll find the use cases useful to learn how architectural decisions are made.

In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo methods.

Petri nets are a popular and powerful formal model for the analysis and modelling of concurrent systems, and a rich theory has developed around them. Petri nets are taught to undergraduates, and also used by industrial practitioners. This book focuses on a particular class of petri nets, free choice petri nets, which play a central role in the theory. The text is very clearly organised, with every notion carefully explained and every result proved. Clear exposition is given for place invariants, siphons, traps and many other important analysis techniques. The material is organised along the lines of a course book, and each chapter contains numerous exercises, making this book ideal for graduate students and research workers alike.

The foundations of parallel computation, especially the efficiency of computation, are the concern of this book. Distinguished international researchers have contributed fifteen chapters which together form a coherent stream taking the reader who has little prior knowledge of the field to a position of being familiar with leading edge issues. The book may also function as a source of teaching material and reference for researchers. The first part is devoted to the Parallel Random Access Machine (P-RAM) model of parallel computation. The initial chapters justify and define the model, which is then used for the development of algorithm design in a variety of application areas such as deterministic algorithms, randomisation and algorithm resilience. The second part deals with distributed memory models of computation. The question of efficiently implementing P-RAM algorithms within these models is addressed as are the immensely interesting prospects for general purpose parallel computation.

Leverage OpenTelemetry's API, libraries, tools and the collector to produce and collect telemetry along with using open-source tools to analyze distributed traces, check metrics and logs, and gain insights into application health Key Features Get to grips with OpenTelemetry, an open-source cloud-native software observability standard Use vendor-neutral tools to instrument applications to produce better telemetry and improve observability Understand how telemetry data can be correlated and interpreted to understand distributed systems Book DescriptionCloud-Native Observability with OpenTelemetry is a guide to helping you look for answers to questions about your applications. This book teaches you how to produce telemetry from your applications using an open standard to retain control of data. OpenTelemetry provides the tools necessary for you to gain visibility into the performance of your services. It allows you to instrument your application code through vendor-neutral APIs, libraries and tools. By reading Cloud-Native Observability with OpenTelemetry, you'll learn about the concepts and signals of OpenTelemetry - traces, metrics, and logs. You'll practice producing telemetry for these signals by configuring and instrumenting a distributed cloud-native application using the OpenTelemetry API. The book also guides you through deploying the collector, as well as telemetry backends necessary to help you understand what to do with the data once it's emitted. You'll look at various examples of how to identify application performance issues through telemetry. By analyzing telemetry, you'll also be able to better understand how an observable application can improve the software development life cycle. By the end of this book, you'll be well-versed with OpenTelemetry, be able to instrument services using the OpenTelemetry API to produce distributed traces, metrics and logs, and more. What you will learn Understand the core concepts of OpenTelemetry Explore concepts in distributed tracing, metrics, and logging Discover the APIs and SDKs necessary to instrument an application using OpenTelemetry Explore what auto-instrumentation is and how it can help accelerate application instrumentation Configure and deploy the OpenTelemetry Collector Get to grips with how different open-source backends can be used to analyze telemetry data Understand how to correlate telemetry in common scenarios to get to the root cause of a problem Who this book is forThis book is for software engineers, library authors, and systems operators looking to better understand their infrastructure, services and applications by leveraging telemetry data like never before. Working knowledge of Python programming is assumed for the example applications that you'll be building and instrumenting using the OpenTelemetry API and SDK. Some familiarity with Go programming, Linux, and Docker is preferable to help you set up additional components in various examples throughout the book.

Solve the complexity of running a business in a multi-cloud environment with practical guidelines backed by industry experience Key Features * Explore the benefits of the major cloud providers to make better informed decisions * Accelerate digital transformation with multi-cloud adoption, including the use of PaaS and SaaS concepts * Get the best out of multi-cloud by exploring relevant use cases for data platforms and IoT Book Description Most enterprises adopt multi-cloud with the intention of accelerating digital transformation, but moving data and applications to public clouds and implementing Platform as a Service (PaaS) and Software as a Service (SaaS) solutions are challenging. One of the biggest challenges is deciding what parts of which services are the most useful to help the company thrive. Through this book, you'll learn how to choose the most apt cloud service and how to manage operations, cost, and security, all while learning how to overcome the complexities associated with multi-cloud adoption via use cases (IoT, data mining, Web3, financial management, and more). This new edition is focused on helping you stay in control of your cloud environments by using the concepts of BaseOps, FinOps, and DevSecOps. You'll learn how to develop, release, and manage products and services in the major public clouds Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), whilst optimizing costs and maximizing security using the various tools and services that these cloud providers offer. By the end of this book, you will have become familiar with the complexities associated with running a business in a multi-cloud environment and identified ways to solve these complexities in the domains of operations, financial management, and security. What you will learn * Learn how to choose the right cloud platform via various use cases * Understand the concepts associated with multi-cloud, including IaC, SaaS, PaaS, and CaC * Use the techniques and tools offered by Azure, AWS, and GCP to integrate security * Learn about enterprise architecture, value streams, and well-architected frameworks of Azure, AWS, and GCP * Use FinOps to define cost models and create transparency in cloud costs with showback and chargeback * Improve security with the DevSecOps maturity model * Explore the concepts of AIOps and GreenOps Who This Book Is For Cloud architects, solutions architects, enterprise architects, and cloud consultants will find this book valuable. Basic knowledge of any one of the major public clouds (Azure, AWS, or GCP) will be helpful.

Get to grips with Microsoft Azure cost management and gain complete, reliable, and sustainable control of your cloud spend Key Features Explore resource rightsizing and cleanup methods and their implementation Learn key resource usage optimization concepts Understand app optimization and plan for optimized and sustainable cloud native applications Book DescriptionCloud teams and ICT cost controllers working with Azure will be able to put their knowledge to work with this practical guide, introducing a process model for structured cost governance. The Road to Azure Cost Governance is a must-read if you find yourself facing the harsh reality of monthly cloud costs gradually getting out of control. Starting with how resources are created and managed, everything you need to know in order to track, display, optimize, rightsize, and clean up cloud resources will be tackled with a workflow approach that will leave the choice of operation to you (be it the Azure CLI, automation, logic apps, or even custom code). Using real-world datasets, you'll learn everything from basic cost management to modeling your cloud spend across your technical resources in a sustainable way. The book will also show you how to create a recursive optimization process that will give you full control of spending and savings, while helping you reserve budget for future cloud projects and innovation. By the end of this Azure book, you'll have a clear understanding and control of your cloud spend along with knowledge of a number of cost-saving techniques used by companies around the world, application optimization patterns, and the carbon impact of your cloud infrastructure. What you will learn Use Azure reporting, monitoring, and configurations to model your cloud resources and make costs clearer Discover resource-saving techniques and put them into practice Establish a continuous clean-up and rightsizing process Explore and implement automation to drive recurrent savings Find out how to use Azure Reservations in the best possible way Get started with building cloud native, cost-optimized applications Get to grips with implementing cost- and carbon-aware applications on Azure Who this book is forIf you're someone who deals with Azure cloud costs and has a technical background, this book will help you understand and control your cloud spending. This book is for decision-makers, cloud managers, cloud architects, cost controllers, and software solution professionals working with Microsoft cloud services in Azure and looking to build optimized solutions for their enterprise operations.