Media processing applications, such as three-dimensional graphics, video compression, and image processing, currently demand 10-100 billion operations per second of sustained computation. Fortunately, hundreds of arithmetic units can easily fit on a modestly sized 1cm2 chip in modern VLSI. The challenge is to provide these arithmetic units with enough data to enable them to meet the computation demands of media processing applications. Conventional storage hierarchies, which frequently include caches, are unable to bridge the data bandwidth gap between modern DRAM and tens to hundreds of arithmetic units. A data bandwidth hierarchy, however, can bridge this gap by scaling the provided bandwidth across the levels of the storage hierarchy. The stream programming model enables media processing applications to exploit a data bandwidth hierarchy effectively. Media processing applications can naturally be expressed as a sequence of computation kernels that operate on data streams. This programming model exposes the locality and concurrency inherent in these applications and enables them to be mapped efficiently to the data bandwidth hierarchy. Stream programs are able to utilize inexperience local data bandwidth when possible and consume expensive global data bandwidth only when necessary. Stream Processor Architecture presents the architecture of the Imagine streaming media processor, which delivers a peak performance of 20 billion floating-point operations per second. Imagine efficiently supports 48 arithmetic units with a three-tiered data bandwidth hierarchy. At the base of the hierarchy, the streaming memory system employs memory access scheduling to maximize the sustained bandwidth of external DRAM. At the center of the hierarchy, the global stream register file enables streams of data to be recirculated directly from one computation kernel to the next without returning data to memory. Finally, local distributed register files that directly feed the arithmetic units enable temporary data to be stored locally so that it does not need to consume costly global register bandwidth. The bandwidth hierarchy enables Imagine to achieve up to 96% of the performance of a stream processor with infinite bandwidth from memory and the global register file.

Get started with Apache Flink, the open source framework that powers some of the world’s largest stream processing applications. With this practical book, you’ll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink’s DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them. Learn concepts and challenges of distributed stateful stream processing Explore Flink’s system architecture, including its event-time processing mode and fault-tolerance model Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators Read data from and write data to external systems with exactly-once consistency Deploy and configure Flink clusters Operate continuously running streaming applications

Stream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.

A survey of architectural mechanisms and implementation techniques for exploiting fine- and coarse-grained parallelism within microprocessors. Beginning with a review of past techniques, the monograph provides a comprehensive account of state-of-the-art techniques used in microprocessors, covering both the concepts involved and implementations in sample processors. The whole is rounded off with a thorough review of the research techniques that will lead to future microprocessors. XXXXXXX Neuer Text This monograph surveys architectural mechanisms and implementation techniques for exploiting fine-grained and coarse-grained parallelism within microprocessors. It presents a comprehensive account of state-of-the-art techniques used in microprocessors that covers both the concepts involved and possible implementations. The authors also provide application-oriented methods and a thorough review of the research techniques that will lead to the development of future processors.

Understand the principles of software architecture with coverage on SOA, distributed and messaging systems, and database modeling Key FeaturesGain knowledge of architectural approaches on SOA and microservices for architectural decisionsExplore different architectural patterns for building distributed applicationsMigrate applications written in Java or Python to the Go languageBook Description Building software requires careful planning and architectural considerations; Golang was developed with a fresh perspective on building next-generation applications on the cloud with distributed and concurrent computing concerns. Hands-On Software Architecture with Golang starts with a brief introduction to architectural elements, Go, and a case study to demonstrate architectural principles. You'll then move on to look at code-level aspects such as modularity, class design, and constructs specific to Golang and implementation of design patterns. As you make your way through the chapters, you'll explore the core objectives of architecture such as effectively managing complexity, scalability, and reliability of software systems. You'll also work through creating distributed systems and their communication before moving on to modeling and scaling of data. In the concluding chapters, you'll learn to deploy architectures and plan the migration of applications from other languages. By the end of this book, you will have gained insight into various design and architectural patterns, which will enable you to create robust, scalable architecture using Golang. What you will learnUnderstand architectural paradigms and deep dive into MicroservicesDesign parallelism/concurrency patterns and learn object-oriented design patterns in GoExplore API-driven systems architecture with introduction to REST and GraphQL standardsBuild event-driven architectures and make your architectures anti-fragileEngineer scalability and learn how to migrate to Go from other languagesGet to grips with deployment considerations with CICD pipeline, cloud deployments, and so onBuild an end-to-end e-commerce (travel) application backend in GoWho this book is for Hands-On Software Architecture with Golang is for software developers, architects, and CTOs looking to use Go in their software architecture to build enterprise-grade applications. Programming knowledge of Golang is assumed.

More useful techniques, tips, and tricks for harnessing the power of the new generation of powerful GPUs.

Summary Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort. Foreword by Neha Narkhede, Cocreator of Apache Kafka Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Not all stream-based applications require a dedicated processing cluster. The lightweight Kafka Streams library provides exactly the power and simplicity you need for message handling in microservices and real-time event processing. With the Kafka Streams API, you filter and transform data streams with just Kafka and your application. About the Book Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you'll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You'll even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging. What's inside Using the KStreams API Filtering, transforming, and splitting data Working with the Processor API Integrating with external systems About the Reader Assumes some experience with distributed systems. No knowledge of Kafka or streaming applications required. About the Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience. Table of Contents PART 1 - GETTING STARTED WITH KAFKA STREAMS Welcome to Kafka Streams Kafka quicklyPART 2 - KAFKA STREAMS DEVELOPMENT Developing Kafka Streams Streams and state The KTable API The Processor APIPART 3 - ADMINISTERING KAFKA STREAMS Monitoring and performance Testing a Kafka Streams applicationPART 4 - ADVANCED CONCEPTS WITH KAFKA STREAMS Advanced applications with Kafka StreamsAPPENDIXES Appendix A - Additional configuration information Appendix B - Exactly once semantics