High Performance In Memory Computing With Apache Ignite
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High Performance in-memory computing with Apache Ignite
This book covers a verity of topics, including in-memory data grid, highly available service grid, streaming (event processing for IoT and fast data) and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those, who have the following use cases: 1) You have a high volume of ACID transactions in your system. 2) You have database bottleneck in your application and want to solve the problem. 3) You want to develop and deploy Microservices in a distributed fashion. 4) You have an existing Hadoop ecosystem (OLAP) and want to improve the performance of map/reduce jobs without making any changes in your existing map/reduce jobs. 5) You want to share Spark RDD directly in-memory (without storing the state into the disk) 7) You are planning to process continuous never-ending streams and complex events of data. 8) You want to use distributed computations in parallel fashion to gain high performance.
Emerging Computing Techniques in Engineering
The book is divided into three volumes to cover all computing topics. This is the first volume and it has 23 chapters. It focuses on general computing techniques such as cloud computing, grid computing, pervasive computing, optical computing, web computing, parallel computing, distributed computing, high-performance computing, GPU computing, exascale & extreme computing, in-memory computing, embedded computing, quantum computing, and green computing
High-Performance Big Data Computing
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.