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Understand Mathematics, Understand Computing
In this book the authors aim to endow the reader with an operational, conceptual, and methodological understanding of the discrete mathematics that can be used to study, understand, and perform computing. They want the reader to understand the elements of computing, rather than just know them. The basic topics are presented in a way that encourages readers to develop their personal way of thinking about mathematics. Many topics are developed at several levels, in a single voice, with sample applications from within the world of computing. Extensive historical and cultural asides emphasize the human side of mathematics and mathematicians. By means of lessons and exercises on “doing” mathematics, the book prepares interested readers to develop new concepts and invent new techniques and technologies that will enhance all aspects of computing. The book will be of value to students, scientists, and engineers engaged in the design and use of computing systems, and to scholars and practitioners beyond these technical fields who want to learn and apply novel computational ideas.
Topics in Parallel and Distributed Computing
This book introduces beginning undergraduate students of computing and computational disciplines to modern parallel and distributed programming languages and environments, including map-reduce, general-purpose graphics processing units (GPUs), and graphical user interfaces (GUI) for mobile applications. The book also guides instructors via selected essays on what and how to introduce parallel and distributed computing topics into the undergraduate curricula, including quality criteria for parallel algorithms and programs, scalability, parallel performance, fault tolerance, and energy efficiency analysis. The chapters designed for students serve as supplemental textual material for early comp...
Handbook on Parallel and Distributed Processing
Here, authors from academia and practice provide practitioners, scientists and graduates with basic methods and paradigms, as well as important issues and trends across the spectrum of parallel and distributed processing. In particular, they cover such fundamental topics as efficient parallel algorithms, languages for parallel processing, parallel operating systems, architecture of parallel and distributed systems, management of resources, tools for parallel computing, parallel database systems and multimedia object servers, as well as the relevant networking aspects. A chapter is dedicated to each of parallel and distributed scientific computing, high-performance computing in molecular sciences, and multimedia applications for parallel and distributed systems.
Euro-Par 2016: Parallel Processing
This book constitutes the refereed proceedings of the 22nd International Conference on Parallel and Distributed Computing, Euro-Par 2016, held in Grenoble, France, in August 2016. The 47 revised full papers presented together with 2 invited papers and one industrial paper were carefully reviewed and selected from 176 submissions. The papers are organized in 12 topical sections: Support Tools and Environments; Performance and Power Modeling, Prediction and Evaluation; Scheduling and Load Balancing; High Performance Architectures and Compilers; Parallel and Distributed Data Management and Analytics; Cluster and Cloud Computing; Distributed Systems and Algorithms; Parallel and Distributed Programming, Interfaces, Languages; Multicore and Manycore Parallelism; Theory and Algorithms for Parallel Computation and Networking; Parallel Numerical Methods and Applications; Accelerator Computing.
Handbook of Scheduling
Researchers in management, industrial engineering, operations, and computer science have intensely studied scheduling for more than 50 years, resulting in an astounding body of knowledge in this field. Handbook of Scheduling: Algorithms, Models, and Performance Analysis, the first handbook on scheduling, provides full coverage of the most recent and advanced topics on the subject. It assembles researchers from all relevant disciplines in order to facilitate cross-fertilization and create new scheduling insights. The book comprises six major parts, each of which breaks down into smaller chapters: · Part I introduces materials and notation, with tutorials on complexity theory and algorithms f...
Parallel Processing and Applied Mathematics
This book constitutes the thoroughly refereed post-proceedings of the 6th International Conference on Parallel Processing and Applied Mathematics, PPAM 2005. The book presents 135 papers organized in topical sections on parallel and distributed architectures, parallel and distributed non-numerical algorithms, performance analysis, prediction and optimization, grid programming, tools and environments for clusters and grids, applications of parallel/distributed/grid computing, evolutionary computing with applications, parallel data mining, parallel numerics, and mathematical and computing methods.
Euro-Par 2020: Parallel Processing Workshops
This book constitutes revised selected papers from the workshops held at the 26th International Conference on Parallel and Distributed Computing, Euro-Par 2020, which took place in Warsaw, Poland, in August 2020. The workshops were held virtually due to the coronavirus pandemic. The 27 full papers presented in this volume were carefully reviewed and selected from 50 submissions. Euro-Par is an annual, international conference in Europe, covering all aspects of parallel and distributed processing. These range from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-edged applications, from architecture, compiler, language and interface design and implementation to tools, support infrastructures, and application performance aspects.
Euro-Par 2010 - Parallel Processing
This book constitutes the refereed proceedings of the 16th International Euro-Par Conference held in Ischia, Italy, in August/September 2010. The 90 revised full papers presented were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on support tools and environments; performance prediction and evaluation; scheduling and load-balancing; high performance architectures and compilers; parallel and distributed data management; grid, cluster and cloud computing; peer to peer computing; distributed systems and algorithms; parallel and distributed programming; parallel numerical algorithms; multicore and manycore programming; theory and algorithms for parallel computation; high performance networks; and mobile and ubiquitous computing.
Parallel Processing and Applied Mathematics, Part II
The LNCS series reports State-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components In parallel to the printed book, each new volume is published electronically in LNCS Online
Euro-Par 2020: Parallel Processing
This book constitutes the proceedings of the 26th International Conference on Parallel and Distributed Computing, Euro-Par 2020, held in Warsaw, Poland, in August 2020. The conference was held virtually due to the coronavirus pandemic. The 39 full papers presented in this volume were carefully reviewed and selected from 158 submissions. They deal with parallel and distributed computing in general, focusing on support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architectures and compilers; data management, analytics and machine learning; cluster, cloud and edge computing; theory and algorithms for parallel and distributed processing; parallel and distributed programming, interfaces, and languages; multicore and manycore parallelism; parallel numerical methods and applications; and accelerator computing.
