Parallel Processing 1980 To 2020
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Parallel Processing 1980 To 2020
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Author : Robert Kuhn
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-10-14
Parallel Processing 1980 To 2020 written by Robert Kuhn and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-14 with Computers categories.
This historical survey of parallel processing from 1980 to 2020 is a follow-up to the authors’ 1981 Tutorial on Parallel Processing, which covered the state of the art in hardware, programming languages, and applications. Here, we cover the evolution of the field since 1980 in: parallel computers, ranging from the Cyber 205 to clusters now approaching an exaflop, to multicore microprocessors, and Graphic Processing Units (GPUs) in commodity personal devices; parallel programming notations such as OpenMP, MPI message passing, and CUDA streaming notation; and seven parallel applications, such as finite element analysis and computer vision. Some things that looked like they would be major trends in 1981, such as big Single Instruction Multiple Data arrays disappeared for some time but have been revived recently in deep neural network processors. There are now major trends that did not exist in 1980, such as GPUs, distributed memory machines, and parallel processing in nearly every commodity device. This book is intended for those that already have some knowledge of parallel processing today and want to learn about the history of the three areas. In parallel hardware, every major parallel architecture type from 1980 has scaled-up in performance and scaled-out into commodity microprocessors and GPUs, so that every personal and embedded device is a parallel processor. There has been a confluence of parallel architecture types into hybrid parallel systems. Much of the impetus for change has been Moore’s Law, but as clock speed increases have stopped and feature size decreases have slowed down, there has been increased demand on parallel processing to continue performance gains. In programming notations and compilers, we observe that the roots of today’s programming notations existed before 1980. And that, through a great deal of research, the most widely used programming notations today, although the result of much broadening of these roots, remain close to target system architectures allowing the programmer to almost explicitly use the target’s parallelism to the best of their ability. The parallel versions of applications directly or indirectly impact nearly everyone, computer expert or not, and parallelism has brought about major breakthroughs in numerous application areas. Seven parallel applications are studied in this book.
Parallel Processing 1980 To 2020
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Author : Robert Kuhn
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Parallel Processing 1980 To 2020 written by Robert Kuhn and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This historical survey of parallel processing from 1980 to 2020 is a follow-up to the authors’ 1981 Tutorial on Parallel Processing, which covered the state of the art in hardware, programming languages, and applications. Here, we cover the evolution of the field since 1980 in: parallel computers, ranging from the Cyber 205 to clusters now approaching an exaflop, to multicore microprocessors, and Graphic Processing Units (GPUs) in commodity personal devices; parallel programming notations such as OpenMP, MPI message passing, and CUDA streaming notation; and seven parallel applications, such as finite element analysis and computer vision. Some things that looked like they would be major trends in 1981, such as big Single Instruction Multiple Data arrays disappeared for some time but have been revived recently in deep neural network processors. There are now major trends that did not exist in 1980, such as GPUs, distributed memory machines, and parallel processing in nearly every commodity device. This book is intended for those that already have some knowledge of parallel processing today and want to learn about the history of the three areas. In parallel hardware, every major parallel architecture type from 1980 has scaled-up in performance and scaled-out into commodity microprocessors and GPUs, so that every personal and embedded device is a parallel processor. There has been a confluence of parallel architecture types into hybrid parallel systems. Much of the impetus for change has been Moore’s Law, but as clock speed increases have stopped and feature size decreases have slowed down, there has been increased demand on parallel processing to continue performance gains. In programming notations and compilers, we observe that the roots of today’s programming notations existed before 1980. And that, through a great deal of research, the most widely used programming notations today, although the result of much broadening of these roots, remain close to target system architectures allowing the programmer to almost explicitly use the target’s parallelism to the best of their ability. The parallel versions of applications directly or indirectly impact nearly everyone, computer expert or not, and parallelism has brought about major breakthroughs in numerous application areas. Seven parallel applications are studied in this book.
Parallel Processing And Applied Mathematics
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Author : Roman Wyrzykowski
language : en
Publisher: Springer Nature
Release Date : 2023-04-27
Parallel Processing And Applied Mathematics written by Roman Wyrzykowski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-27 with Computers categories.
This two-volume set, LNCS 13826 and LNCS 13827, constitutes the proceedings of the 14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022, held in Gdansk, Poland, in September 2022. The 77 regular papers presented in these volumes were selected from 132 submissions. For regular tracks of the conference, 33 papers were selected from 62 submissions. The papers were organized in topical sections named as follows: Part I: numerical algorithms and parallel scientific computing; parallel non-numerical algorithms; GPU computing; performance analysis and prediction in HPC systems; scheduling for parallel computing; environments and frameworks for parallel/cloud computing; applications of parallel and distributed computing; soft computing with applications and special session on parallel EVD/SVD and its application in matrix computations. Part II: 9th Workshop on Language-Based Parallel Programming (WLPP 2022); 6th Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems (MAMHYP 2022); first workshop on quantum computing and communication; First Workshop on Applications of Machine Learning and Artificial Intelligence in High Performance Computing (WAML 2022); 4th workshop on applied high performance numerical algorithms for PDEs; 5th minisymposium on HPC applications in physical sciences; 8th minisymposium on high performance computing interval methods; 7th workshop on complex collective systems.
In Near Memory Computing
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Author : Daichi Fujiki
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
In Near Memory Computing written by Daichi Fujiki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near-memory computing has been attracting growing interest due to its potential to break the memory wall. Near-memory computing moves compute logic near the memory, and thereby reduces data movement. Recent work has also shown that certain memories can morph themselves into compute units by exploiting the physical properties of the memory cells, enabling in-situ computing in the memory array. While in- and near-memory computing can circumvent overheads related to data movement, it comes at the cost of restricted flexibility of data representation and computation, design challenges of compute capable memories, and difficulty in system and software integration. Therefore, wide deployment of in-/near-memory computing cannot be accomplished without techniques that enable efficient mapping of data-intensive applications to such devices,without sacrificing accuracy or increasing hardware costs excessively. This book describes various memory substrates amenable to in- and near-memory computing, architectural approaches for designing efficient and reliable computing devices, and opportunities for in-/near-memory acceleration of different classes of applications.
A Primer On Memory Persistency
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Author : Vaibhav Gogte
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
A Primer On Memory Persistency written by Vaibhav Gogte and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
This book introduces readers to emerging persistent memory (PM) technologies that promise the performance of dynamic random-access memory (DRAM) with the durability of traditional storage media, such as hard disks and solid-state drives (SSDs). Persistent memories (PMs), such as Intel's Optane DC persistent memories, are commercially available today. Unlike traditional storage devices, PMs can be accessed over a byte-addressable load-store interface with access latency that is comparable to DRAM. Unfortunately, existing hardware and software systems are ill-equipped to fully avail the potential of these byte-addressable memory technologies as they have been designed to access traditional storage media over a block-based interface. Several mechanisms have been explored in the research literature over the past decade to design hardware and software systems that provide high-performance access to PMs.Because PMs are durable, they can retain data across failures, such as power failures and program crashes. Upon a failure, recovery mechanisms may inspect PM data, reconstruct state and resume program execution. Correct recovery of data requires that operations to the PM are properly ordered during normal program execution. Memory persistency models define the order in which memory operations are performed at the PM. Much like memory consistency models, memory persistency models may be relaxed to improve application performance. Several proposals have emerged recently to design memory persistency models for hardware and software systems and for high-level programming languages. These proposals differ in several key aspects; they relax PM ordering constraints, introduce varying programmability burden, and introduce differing granularity of failure atomicity for PM operations.This primer provides a detailed overview of the various classes of the memory persistency models, their implementations in hardware, programming languages and software systems proposed in the recent research literature, and the PM ordering techniques employed by modern processors.
Robotic Computing On Fpgas
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Author : Shaoshan Liu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Robotic Computing On Fpgas written by Shaoshan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book provides a thorough overview of the state-of-the-art field-programmable gate array (FPGA)-based robotic computing accelerator designs and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots.
Ai For Computer Architecture
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Author : Lizhong Chen
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Ai For Computer Architecture written by Lizhong Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs.
Deep Learning Systems
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Author : Andres Rodriguez
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Deep Learning Systems written by Andres Rodriguez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to bettercollaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Computational Mechanics With Neural Networks
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Author : Genki Yagawa
language : en
Publisher: Springer Nature
Release Date : 2021-02-26
Computational Mechanics With Neural Networks written by Genki Yagawa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.
This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.
The Hank Show
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Author : McKenzie Funk
language : en
Publisher: St. Martin's Press
Release Date : 2023-10-03
The Hank Show written by McKenzie Funk and has been published by St. Martin's Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-03 with Biography & Autobiography categories.
The bizarre and captivating story of the most important person you've never heard of. The world we live in today, where everything is tracked by corporations and governments, originates with one manic, elusive, utterly unique man—as prone to bullying as he was to fits of surpassing generosity and surprising genius. His name was Hank Asher, and his life was a strange and spectacular show that changed the course of the future. In The Hank Show, critically acclaimed author and journalist McKenzie Funk relates Asher's stranger-than-fiction story—he careened from drug-running pilot to alleged CIA asset, only to be reborn as the pioneering computer programmer known as the father of data fusion. He was the multimillionaire whose creations now power a new reality where your every move is tracked by police departments, intelligence agencies, political parties, and financial firms alike. But his success was not without setbacks. He truly lived nine lives, on top of the world one minute, only to be forced out of the companies he founded and blamed for data breaches resulting in major lawsuits and market chaos. In the vein of the blockbuster movie Catch Me if You Can, this spellbinding work of narrative nonfiction propels you forward on a forty year journey of intrigue and innovation, from Colombia to the White House and from Silicon Valley to the 2016 Trump campaign, focusing a lens on the dark side of American business and its impact on the everyday fabric of our modern lives.