Author: Seyed H Roosta Publisher: Springer Science & Business Media ISBN: 1461212200 Category : Computers Languages : en Pages : 579
Book Description
Motivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain.
Author: Paraschos Koutris Publisher: Foundations and Trends in Databases ISBN: 9781680834062 Category : Electronic data processing Languages : en Pages : 144
Book Description
This monograph reviews some of the recent theoretical results on efficient data processing on large distributed architectures, as well as some of the relevant classical results on parallel sorting and parallel matrix multiplication.
Author: Paraschos Koutris Publisher: ISBN: 9781680834079 Category : Distributed databases Languages : en Pages : 131
Book Description
In the last decade or so we have witnessed a growing interest in processing large data sets on large distributed clusters. The idea was pioneered by the MapReduce framework, and has been widely adopted by several other systems, including PigLatin, Hive, Scope, U-SQL, Dremmel, Spark and Myria. A large part of the complex data analysis performed by these systems consists of a sequence of relatively simple query operations, such as joining two or more tables. This survey discusses recent algorithmic developments for distributed data processing. It uses a theoretical model of parallel processing called the Massively Parallel Computation (MPC) model, which is a simplification of the BSP model where the only cost is given by the amount of communication and the number of communication rounds. The survey studies several algorithms for multi-join queries, for sorting, and for matrix multiplication, and discusses their relationships and common techniques applied across the different data processing tasks.
Author: Michael A. Heroux Publisher: SIAM ISBN: 9780898718133 Category : Computers Languages : en Pages : 421
Book Description
Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering.
Author: Vivek Kale Publisher: CRC Press ISBN: 1351029207 Category : Computers Languages : en Pages : 342
Book Description
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.
Author: Fayez Gebali Publisher: John Wiley & Sons ISBN: 0470934638 Category : Computers Languages : en Pages : 372
Book Description
There is a software gap between the hardware potential and the performance that can be attained using today's software parallel program development tools. The tools need manual intervention by the programmer to parallelize the code. Programming a parallel computer requires closely studying the target algorithm or application, more so than in the traditional sequential programming we have all learned. The programmer must be aware of the communication and data dependencies of the algorithm or application. This book provides the techniques to explore the possible ways to program a parallel computer for a given application.
Author: Ioannis Karydis Publisher: Springer ISBN: 3319299190 Category : Computers Languages : en Pages : 203
Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2015, held in Patras, Greece, in September 2015 in conjunction with ALGO 2015. The 13 revised full papers presented together with 2 tutorial papers were carefully reviewed and selected from 37 initial submissions. They cover a wide range of topics in two main tracks: algorithmic aspects of large-scale data stores, and software tools and distributed architectures for cloud-based data management.
Author: Arun Kumar Sangaiah Publisher: Academic Press ISBN: 0128172932 Category : Technology & Engineering Languages : en Pages : 282
Book Description
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Author: Seyed H Roosta
Author: Paraschos Koutris
Author: Paraschos Koutris
Author: Michael A. Heroux
Author: Vivek Kale
Author: Fayez Gebali
Author: Ankur Moitra
Author: Ioannis Karydis
Author: Guy E. Blelloch
Author: Arun Kumar Sangaiah