Advances in Parallel Computing (IOS Press) – serie

The demand for more computing power has been a constant trend in many fields of science, engineering and business. Now more than ever, the need for more and more processing power is emerging in the resolution of complex problems from life sciences, financial services, drug discovery, weather forecasting, massive data processing for e-science, e-commerce and e-government etc. Grid and P2P paradigms are based on the premise to deliver greater computing power at less cost, thus enabling the solution of such complex problems. Parallel Programming, Models and Applications in Grid and P2P Systems presents recent advances for grid and P2P paradigms, middleware, programming models, communication libraries, as well as their application to the resolution of real-life problems. By approaching grid and P2P paradigms in an integrated and comprehensive way, we believe that this book will serve as a reference for researchers and developers of the grid and P2P computing communities. Important features of the book include an up-to-date survey of grid and P2P programming models, middleware and communication libraries, new approaches for modeling and performance analysis in grid and P2P systems, novel grid and P2P middleware as well as grid and P2P-enabled applications for real-life problems. Academics, scientists, software developers and engineers interested in the grid and P2P paradigms will find the comprehensive coverage of this book useful for their academic, research and development activity.

In 2011 many computer users were exploring the opportunities and the benefits of the massive parallelism offered by heterogeneous computing. In 2000 the Khronos Group, a not-for-profit industry consortium, was founded to create standard open APIs for parallel computing, graphics and dynamic media. Among them has been OpenCL, an open system for programming heterogeneous computers with components made by multiple manufacturers. This publication explains how heterogeneous computers work and how to program them using OpenCL. It also describes how to combine OpenCL with OpenGL for displaying graphical effects in real time. Chapter 1 describes briefly two older de facto standard and highly successful parallel programming systems: MPI and OpenMP. Collectively, the MPI, OpenMP, and OpenCL systems cover programming of all major parallel architectures: clusters, shared-memory computers, and the newest heterogeneous computers. Chapter 2, the technical core of the book, deals with OpenCL fundamentals: programming, hardware, and the interaction between them. Chapter 3 adds important information about such advanced issues as double-versus-single arithmetic precision, efficiency, memory use, and debugging. Chapters 2 and 3 contain several examples of code and one case study on genetic algorithms. These examples are related to linear algebra operations, which are very common in scientific, industrial, and business applications. Most of the book’s examples can be found on the enclosed CD, which also contains basic projects for Visual Studio, MinGW, and GCC. This supplementary material will assist the reader in getting a quick start on OpenCL projects.

The US, Europe, Japan and China are racing to develop the next generation of supercomputers – exascale machines capable of 10 to the 18th power calculations a second – by 2020. But the barriers are daunting: the challenge is to change the paradigm of high-performance computing.The 2012 biennial high performance workshop, held in Cetraro, Italy in June 2012, focused on the challenges facing the computing research community to reach exascale performance in the next decade. This book presents papers from this workshop, arranged into four major topics: energy, scalability, new architectural concepts and programming of heterogeneous computing systems.Chapter 1 introduces the status of present supercomputers, which are still about two orders of magnitude separated from the exascale mark. Chapter 2 examines energy demands, a major limiting factor of today's fastest supercomputers; the quantum leap in performance required for exascale computing will require a shift in architectures and technology. In Chapter 3, scalable computer paradigms for dense linear algebra on massive heterogeneous systems are presented, and Chapter 4 discusses architectural concepts. Finally, Chapter 5 addresses the programming of heterogeneous systems.This book will be of interest to all those wishing to understand how the development of modern supercomputers is set to advance in the next decade.

As predicted by Gordon E. Moore in 1965, the performance of computer processors increased at an exponential rate. Nevertheless, the increases in computing speeds of single processor machines were eventually curtailed by physical constraints. This led to the development of parallel computing, and whilst progress has been made in this field, the complexities of parallel algorithm design, the deficiencies of the available software development tools and the complexity of scheduling tasks over thousands and even millions of processing nodes represent a major challenge to the construction and use of more powerful parallel systems.This book presents the proceedings of the biennial International Conference on Parallel Computing (ParCo2015), held in Edinburgh, Scotland, in September 2015. Topics covered include computer architecture and performance, programming models and methods, as well as applications. The book also includes two invited talks and a number of mini-symposia.Exascale computing holds enormous promise in terms of increasing scientific knowledge acquisition and thus contributing to the future well-being and prosperity of mankind. A number of innovative approaches to the development and use of future high-performance and high-throughput systems are to be found in this book, which will be of interest to all those whose work involves the handling and processing of large amounts of data.

The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment.The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

For the last four decades, parallel computing platforms have increasingly formed the basis for the development of high performance systems primarily aimed at the solution of intensive computing problems, and the application of parallel computing systems has also become a major factor in furthering scientific research. But such systems also offer the possibility of solving the problems encountered in the processing of large-scale scientific data sets, as well as in the analysis of Big Data in the fields of medicine, social media, marketing, economics etc.This book presents papers from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2016. The workshop covered a wide range of topics and new developments related to the solution of intensive and large-scale computing problems, and the contributions included in this volume cover aspects of the evolution of parallel platforms and highlight some of the problems encountered with the development of ever more powerful computing systems. The importance of future large-scale data science applications is also discussed.The book will be of particular interest to all those involved in the development or application of parallel computing systems.

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance.The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data.The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance.The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data.The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

The most powerful computers work by harnessing the combined computational power of millions of processors, and exploiting the full potential of such large-scale systems is something which becomes more difficult with each succeeding generation of parallel computers. Alternative architectures and computer paradigms are increasingly being investigated in an attempt to address these difficulties. Added to this, the pervasive presence of heterogeneous and parallel devices in consumer products such as mobile phones, tablets, personal computers and servers also demands efficient programming environments and applications aimed at small-scale parallel systems as opposed to large-scale supercomputers.This book presents a selection of papers presented at the conference: Parallel Computing (ParCo2017), held in Bologna, Italy, on 12 to 15 September 2017. The conference included contributions about alternative approaches to achieving High Performance Computing (HPC) to potentially surpass exa- and zetascale performances, as well as papers on the application of quantum computers and FPGA processors. These developments are aimed at making available systems better capable of solving intensive computational scientific/engineering problems such as climate models, security applications and classic NP-problems, some of which cannot currently be managed by even the most powerful supercomputers available. New areas of application, such as robotics, AI and learning systems, data science, the Internet of Things (IoT), and in-car systems and autonomous vehicles were also covered.As always, ParCo2017 attracted a large number of notable contributions covering present and future developments in parallel computing, and the book will be of interest to all those working in the field.

Due to the increasing need to solve complex problems, high-performance computing (HPC) is now one of the most fundamental infrastructures for scientific development in all disciplines, and it has progressed massively in recent years as a result. HPC facilitates the processing of big data, but the tremendous research challenges faced in recent years include: the scalability of computing performance for high velocity, high variety and high volume big data; deep learning with massive-scale datasets; big data programming paradigms on multi-core; GPU and hybrid distributed environments; and unstructured data processing with high-performance computing.This book presents 19 selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale era, held in Tehran, Iran, in April 2017. The book is divided into 3 sections: State of the Art and Future Scenarios, Big Data Challenges, and HPC Challenges, and will be of interest to all those whose work involves the processing of Big Data and the use of HPC.

The realization that the use of components off the shelf (COTS) could reduce costs sparked the evolution of the massive parallel computing systems available today. The main problem with such systems is the development of suitable operating systems, algorithms and application software that can utilise the potential processing power of large numbers of processors. As a result, systems comprising millions of processors are still limited in the applications they can efficiently solve. Two alternative paradigms that may offer a solution to this problem are Quantum Computers (QC) and Brain Inspired Computers (BIC). This book presents papers from the 14th edition of the biennial international conference on High Performance Computing - From Clouds and Big Data to Exascale and Beyond, held in Cetraro, Italy, from 2 - 6 July 2018. It is divided into 4 sections covering data science, quantum computing, high-performance computing, and applications. The papers presented during the workshop covered a wide spectrum of topics on new developments in the rapidly evolving supercomputing field – including QC and BIC – and a selection of contributions presented at the workshop are included in this volume. In addition, two papers presented at a workshop on Brain Inspired Computing in 2017 and an overview of work related to data science executed by a number of universities in the USA, parts of which were presented at the 2018 and previous workshops, are also included.The book will be of interest to all those whose work involves high-performance computing.