This state-of-the-art survey features topics related to the impact of multicore and coprocessor technologies in science and for large-scale applications in an interdisciplinary environment. The papers cover all issues of current research in mathematical modeling, design of parallel algorithms, aspects of microprocessor architecture, parallel programming languages, compilers, hardware-aware computing, heterogeneous platforms, emerging architectures, tools, performance tuning, and requirements for large-scale applications. The contributions presented in this volume offer a survey on the state of the art, the concepts and perspectives for future developments. They are an outcome of an inspiring conference conceived and organized by the editors within the junior scientist program of Heidelberg Academy for Sciences and Humanities titled "Facing the Multicore-Challenge", held at Heidelberg, Germany, in March 2010. The 12 revised full papers presented together with the extended abstracts of 3 invited lectures focus on combination of new aspects of multicore microprocessor technologies, parallel applications, numerical simulation, software development, and tools; thus they clearly show the potential of emerging technologies in the area of multicore and manycore processors that are paving the way towards personal supercomputing.

This state-of-the-art survey features topics related to the impact of multicore, manycore, and coprocessor technologies in science and for large-scale applications in an interdisciplinary environment. The papers cover issues of current research in mathematical modeling, design of parallel algorithms, aspects of microprocessor architecture, parallel programming languages, hardware-aware computing, heterogeneous platforms, manycore technologies, performance tuning, and requirements for large-scale applications. The contributions presented in this volume offer a survey on the state of the art, the concepts and perspectives for future developments. They are an outcome of an inspiring conference conceived and organized by the editors at the Karlsruhe Institute Technology (KIT) in September 2011. The twelve revised full papers presented together with two contributed papers focus on combination of new aspects of microprocessor technologies, parallel applications, numerical simulation, and software development; thus they clearly show the potential of emerging technologies in the area of multicore and manycore processors that are paving the way towards personal supercomputing and very likely towards exascale computing.

This state-of-the-art survey features topics related to the impact of multicore, manycore, and coprocessor technologies in science and large-scale applications in an interdisciplinary environment. The papers included in this survey cover research in mathematical modeling, design of parallel algorithms, aspects of microprocessor architecture, parallel programming languages, hardware-aware computing, heterogeneous platforms, manycore technologies, performance tuning, and requirements for large-scale applications. The contributions presented in this volume are an outcome of an inspiring conference conceived and organized by the editors at the University of Applied Sciences (HfT) in Stuttgart, Germany, in September 2012. The 10 revised full papers selected from 21 submissions are presented together with the twelve poster abstracts and focus on combination of new aspects of microprocessor technologies, parallel applications, numerical simulation, and software development; thus they clearly show the potential of emerging technologies in the area of multicore and manycore processors that are paving the way towards personal supercomputing and very likely towards exascale computing.

This book constitutes the refereed proceedings of the First Conference on Creativity in Intelligent Technologies and Data Science, CIT&DS 2015, held in Volgograd, Russia, in September 2015. The 66 revised full papers and two short papers presented were carefully reviewed and selected from 208 submissions. The papers are organized in topical sections on computational creativity for science and design; knowledge discovery in patent and open sources for creative tasks; software computer-aided design and agent-based systems; conceptual, cognitive and qualitative modeling with application in intelligent decision making; design creativity in CAD/CAM/CAE/PDM; intelligent decision support for continual improvement process; data science in energy management, transportation and urban development; data science in social networks analysis; natural language and image processing and analysis; game-based learning technologies in engineering education and educational games design; personalized learning in Web-based intelligent educational systems; e-inclusion: development of smart mobile applications for people with disabilities.

This book covers two main topics: First, novel fast and flexible simulation techniques for modern heterogeneous NoC-based multi-core architectures. These are implemented in the full-system simulator called InvadeSIM and designed to study the dynamic behavior of hundreds of parallel application programs running on such architectures while competing for resources. Second, a novel actor-oriented programming library called ActorX10, which allows to formally model parallel streaming applications by actor graphs and to analyze predictable execution behavior as part of so-called hybrid mapping approaches, which are used to guarantee real-time requirements of such applications at design time independent from dynamic workloads by a combination of static analysis and dynamic embedding.

This book provides an overview of and essential insights on invasive computing. Pursuing a comprehensive approach, it addresses proper concepts, invasive language constructs, and the principles of invasive hardware. The main focus is on the important topic of how to map task-parallel applications to future multi-core architectures including 1,000 or more processor units. A special focus today is the question of how applications can be mapped onto such architectures while not only taking into account functional correctness, but also non-functional execution properties such as execution times and security properties. The book provides extensive experimental evaluations, investigating the benefits of applying invasive computing and hybrid application mapping to give guarantees on non-functional properties such as timing, energy, and security. The techniques in this book are presented in a step-by-step manner, supported by examples and figures. All proposed ideas for providing guarantees on performance, energy consumption, and security are enabled by using the concept of invasive computing and the exclusive usage of resources.

The only book to offer special coverage of the fundamentals of multicore DSP for implementation on the TMS320C66xx SoC This unique book provides readers with an understanding of the TMS320C66xx SoC as well as its constraints. It offers critical analysis of each element, which not only broadens their knowledge of the subject, but aids them in gaining a better understanding of how these elements work so well together. Written by Texas Instruments’ First DSP Educator Award winner, Naim Dahnoun, the book teaches readers how to use the development tools, take advantage of the maximum performance and functionality of this processor and have an understanding of the rich content which spans from architecture, development tools and programming models, such as OpenCL and OpenMP, to debugging tools. It also covers various multicore audio and image applications in detail. Additionally, this one-of-a-kind book is supplemented with: A rich set of tested laboratory exercises and solutions Audio and Image processing applications source code for the Code Composer Studio (integrated development environment from Texas Instruments) Multiple tables and illustrations With no other book on the market offering any coverage at all on the subject and its rich content with twenty chapters, Multicore DSP: From Algorithms to Real-time Implementation on the TMS320C66x SoC is a rare and much-needed source of information for undergraduates and postgraduates in the field that allows them to make real-time applications work in a relatively short period of time. It is also incredibly beneficial to hardware and software engineers involved in programming real-time embedded systems.

The book reports on advanced solutions to the problem of simulating wing and nacelle stall, as presented and discussed by internationally recognized researchers at the Closing Symposium of the DFG Research Unit FOR 1066. Reliable simulations of flow separation on airfoils, wings and powered engine nacelles at high Reynolds numbers represent great challenges in defining suitable mathematical models, computing numerically accurate solutions and providing comprehensive experimental data for the validation of numerical simulations. Additional problems arise from the need to consider airframe-engine interactions and inhomogeneous onset flow conditions, as real aircraft operate in atmospheric environments with often-large distortions. The findings of fundamental and applied research into these and other related issues are reported in detail in this book, which targets all readers, academics and professionals alike, interested in the development of advanced computational fluid dynamics modeling for the simulation of complex aircraft flows with flow separation.

The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.