Parallel Computing Hits The Power Wall
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Parallel Computing Hits the Power Wall
Author: Arthur Francisco Lorenzon
language: en
Publisher: Springer Nature
Release Date: 2019-11-05
This book describes several approaches to adaptability that are applied for the optimization of parallel applications, such as thread-level parallelism exploitation and dynamic voltage and frequency scaling on multicore systems. This book explains how software developers can apply a novel technique to adapt the number of threads at runtime without any modification in the source code nor recompilation. This book is useful for software developers in general since it offers realistic examples throughout to demonstrate various techniques presented.
Euro-Par 2008 Workshops - Parallel Processing
Parallel and distributed processing, although within the focus of computer science research for a long time, is gaining more and more importance in a wide spectrum of applications. These proceedings aim to demonstrate the use of parallel and distributed processing concepts in different application fields, and attempt to spark interest in novel research directions to parallel and high-performance computing research in general. The objective of these workshops is to specifically address researchers coming from university, industry and governmental research organizations and application-oriented companies in order to close the gap between purely scientific research and the applicab- ity of the research ideas to real-life problems. Euro-Par is an annual series of international conferences dedicated to the pro- tion and advancement of all aspects of parallel and distributed computing. The 2008 event was the 14th issue of the conference. Euro-Par has for a long time been eager to attract colocated events sharing the same goal of promoting the dev- opment of parallel and distributed computing, both as an industrial technique and an academic discipline, extending the frontier of both the state of the art and the state of the practice. Since 2006, Euro-Par has been offering researchers the chance to co- cate advanced technical workshops back-to-back with the main conference.
Parallel Computing Architectures and APIs
Parallel Computing Architectures and APIs: IoT Big Data Stream Processing commences from the point high-performance uniprocessors were becoming increasingly complex, expensive, and power-hungry. A basic trade-off exists between the use of one or a small number of such complex processors, at one extreme, and a moderate to very large number of simpler processors, at the other. When combined with a high-bandwidth, interprocessor communication facility leads to significant simplification of the design process. However, two major roadblocks prevent the widespread adoption of such moderately to massively parallel architectures: the interprocessor communication bottleneck, and the difficulty and high cost of algorithm/software development. One of the most important reasons for studying parallel computing architectures is to learn how to extract the best performance from parallel systems. Specifically, you must understand its architectures so that you will be able to exploit those architectures during programming via the standardized APIs. This book would be useful for analysts, designers and developers of high-throughput computing systems essential for big data stream processing emanating from IoT-driven cyber-physical systems (CPS). This pragmatic book: Devolves uniprocessors in terms of a ladder of abstractions to ascertain (say) performance characteristics at a particular level of abstraction Explains limitations of uniprocessor high performance because of Moore’s Law Introduces basics of processors, networks and distributed systems Explains characteristics of parallel systems, parallel computing models and parallel algorithms Explains the three primary categorical representatives of parallel computing architectures, namely, shared memory, message passing and stream processing Introduces the three primary categorical representatives of parallel programming APIs, namely, OpenMP, MPI and CUDA Provides an overview of Internet of Things (IoT), wireless sensor networks (WSN), sensor data processing, Big Data and stream processing Provides introduction to 5G communications, Edge and Fog computing Parallel Computing Architectures and APIs: IoT Big Data Stream Processing discusses stream processing that enables the gathering, processing and analysis of high-volume, heterogeneous, continuous Internet of Things (IoT) big data streams, to extract insights and actionable results in real time. Application domains requiring data stream management include military, homeland security, sensor networks, financial applications, network management, web site performance tracking, real-time credit card fraud detection, etc.