Fpga Accelerated Simulation Of Computer Systems
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Fpga Accelerated Simulation Of Computer Systems
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Author : Hari Angepat
language : en
Publisher: Springer
Release Date : 2014-08-26
Fpga Accelerated Simulation Of Computer Systems written by Hari Angepat and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-26 with Technology & Engineering categories.
To date, the most common form of simulators of computer systems are software-based running on standard computers. One promising approach to improve simulation performance is to apply hardware, specifically reconfigurable hardware in the form of field programmable gate arrays (FPGAs). This manuscript describes various approaches of using FPGAs to accelerate software-implemented simulation of computer systems and selected simulators that incorporate those techniques. More precisely, we describe a simulation architecture taxonomy that incorporates a simulation architecture specifically designed for FPGA accelerated simulation, survey the state-of-the-art in FPGA-accelerated simulation, and describe in detail selected instances of the described techniques. Table of Contents: Preface / Acknowledgments / Introduction / Simulator Background / Accelerating Computer System Simulators with FPGAs / Simulation Virtualization / Categorizing FPGA-based Simulators / Conclusion / Bibliography / Authors' Biographies
Fpga Accelerated Simulation Of Computer Systems
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Author : Hari Angepat
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Fpga Accelerated Simulation Of Computer Systems written by Hari Angepat 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.
To date, the most common form of simulators of computer systems are software-based running on standard computers. One promising approach to improve simulation performance is to apply hardware, specifically reconfigurable hardware in the form of field programmable gate arrays (FPGAs). This manuscript describes various approaches of using FPGAs to accelerate software-implemented simulation of computer systems and selected simulators that incorporate those techniques. More precisely, we describe a simulation architecture taxonomy that incorporates a simulation architecture specifically designed for FPGA accelerated simulation, survey the state-of-the-art in FPGA-accelerated simulation, and describe in detail selected instances of the described techniques. Table of Contents: Preface / Acknowledgments / Introduction / Simulator Background / Accelerating Computer System Simulators with FPGAs / Simulation Virtualization / Categorizing FPGA-based Simulators / Conclusion / Bibliography / Authors' Biographies
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.
Compiling Algorithms For Heterogeneous Systems
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Author : Steven Bell
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Compiling Algorithms For Heterogeneous Systems written by Steven Bell 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.
Most emerging applications in imaging and machine learning must perform immense amounts of computation while holding to strict limits on energy and power. To meet these goals, architects are building increasingly specialized compute engines tailored for these specific tasks. The resulting computer systems are heterogeneous, containing multiple processing cores with wildly different execution models. Unfortunately, the cost of producing this specialized hardware—and the software to control it—is astronomical. Moreover, the task of porting algorithms to these heterogeneous machines typically requires that the algorithm be partitioned across the machine and rewritten for each specific architecture, which is time consuming and prone to error. Over the last several years, the authors have approached this problem using domain-specific languages (DSLs): high-level programming languages customized for specific domains, such as database manipulation, machine learning, or image processing. By giving up generality, these languages are able to provide high-level abstractions to the developer while producing high-performance output. The purpose of this book is to spur the adoption and the creation of domain-specific languages, especially for the task of creating hardware designs. In the first chapter, a short historical journey explains the forces driving computer architecture today. Chapter 2 describes the various methods for producing designs for accelerators, outlining the push for more abstraction and the tools that enable designers to work at a higher conceptual level. From there, Chapter 3 provides a brief introduction to image processing algorithms and hardware design patterns for implementing them. Chapters 4 and 5 describe and compare Darkroom and Halide, two domain-specific languages created for image processing that produce high-performance designs for both FPGAs and CPUs from the same source code, enabling rapid design cycles and quick porting of algorithms. The final section describes how the DSL approach also simplifies the problem of interfacing between application code and the accelerator by generating the driver stack in addition to the accelerator configuration. This book should serve as a useful introduction to domain-specialized computing for computer architecture students and as a primer on domain-specific languages and image processing hardware for those with more experience in the field.
Innovations In The Memory System
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Author : Rajeev Balasubramonian
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Innovations In The Memory System written by Rajeev Balasubramonian 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.
The memory system has the potential to be a hub for future innovation. While conventional memory systems focused primarily on high density, other memory system metrics like energy, security, and reliability are grabbing modern research headlines. With processor performance stagnating, it is also time to consider new programming models that move some application computations into the memory system. This, in turn, will lead to feature-rich memory systems with new interfaces. The past decade has seen a number of memory system innovations that point to this future where the memory system will be much more than dense rows of unintelligent bits. This book takes a tour through recent and prominent research works, touching upon new DRAM chip designs and technologies, near data processing approaches, new memory channel architectures, techniques to tolerate the overheads of refresh and fault tolerance, security attacks and mitigations, and memory scheduling.
Embedded Computer Systems Architectures Modeling And Simulation
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Author : Cristina Silvano
language : en
Publisher: Springer Nature
Release Date : 2023-11-06
Embedded Computer Systems Architectures Modeling And Simulation written by Cristina Silvano and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-06 with Computers categories.
This book constitutes the proceedings of the 22st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021, which took place in July 2022 in Samos, Greece. The 11 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 45 submissions. The conference covers a wide range of embedded systems design aspects, including machine learning accelerators, and power management and programmable dataflow systems.
Architectural And Operating System Support For Virtual Memory
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Author : Abhishek Bhattacharjee
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Architectural And Operating System Support For Virtual Memory written by Abhishek Bhattacharjee 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 computer engineers, academic researchers, new graduate students, and seasoned practitioners an end-to-end overview of virtual memory. We begin with a recap of foundational concepts and discuss not only state-of-the-art virtual memory hardware and software support available today, but also emerging research trends in this space. The span of topics covers processor microarchitecture, memory systems, operating system design, and memory allocation. We show how efficient virtual memory implementations hinge on careful hardware and software cooperation, and we discuss new research directions aimed at addressing emerging problems in this space. Virtual memory is a classic computer science abstraction and one of the pillars of the computing revolution. It has long enabled hardware flexibility, software portability, and overall better security, to name just a few of its powerful benefits. Nearly all user-level programs today take for granted that they will have been freed from the burden of physical memory management by the hardware, the operating system, device drivers, and system libraries. However, despite its ubiquity in systems ranging from warehouse-scale datacenters to embedded Internet of Things (IoT) devices, the overheads of virtual memory are becoming a critical performance bottleneck today. Virtual memory architectures designed for individual CPUs or even individual cores are in many cases struggling to scale up and scale out to today's systems which now increasingly include exotic hardware accelerators (such as GPUs, FPGAs, or DSPs) and emerging memory technologies (such as non-volatile memory), and which run increasingly intensive workloads (such as virtualized and/or "big data" applications). As such, many of the fundamental abstractions and implementation approaches for virtual memory are being augmented, extended, or entirely rebuilt in order to ensure that virtual memory remains viable and performant in the years to come.
An Open Source Research Platform For Heterogeneous Systems On Chip
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Author : Andreas Dominic Kurth
language : en
Publisher: BoD – Books on Demand
Release Date : 2022-10-05
An Open Source Research Platform For Heterogeneous Systems On Chip written by Andreas Dominic Kurth and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-05 with Science categories.
Heterogeneous systems on chip (HeSoCs) combine general-purpose, feature-rich multi-core host processors with domain-specific programmable many-core accelerators (PMCAs) to unite versatility with energy efficiency and peak performance. By virtue of their heterogeneity, HeSoCs hold the promise of increasing performance and energy efficiency compared to homogeneous multiprocessors, because applications can be executed on hardware that is designed for them. However, this heterogeneity also increases system complexity substantially. This thesis presents the first research platform for HeSoCs where all components, from accelerator cores to application programming interface, are available under permissive open-source licenses. We begin by identifying the hardware and software components that are required in HeSoCs and by designing a representative hardware and software architecture. We then design, implement, and evaluate four critical HeSoC components that have not been discussed in research at the level required for an open-source implementation: First, we present a modular, topology-agnostic, high-performance on-chip communication platform, which adheres to a state-of-the-art industry-standard protocol. We show that the platform can be used to build high-bandwidth (e.g., 2.5 GHz and 1024 bit data width) end-to-end communication fabrics with high degrees of concurrency (e.g., up to 256 independent concurrent transactions). Second, we present a modular and efficient solution for implementing atomic memory operations in highly-scalable many-core processors, which demonstrates near-optimal linear throughput scaling for various synthetic and real-world workloads and requires only 0.5 kGE per core. Third, we present a hardware-software solution for shared virtual memory that avoids the majority of translation lookaside buffer misses with prefetching, supports parallel burst transfers without additional buffers, and can be scaled with the workload and number of parallel processors. Our work improves accelerator performance for memory-intensive kernels by up to 4×. Fourth, we present a software toolchain for mixed-data-model heterogeneous compilation and OpenMP offloading. Our work enables transparent memory sharing between a 64-bit host processor and a 32-bit accelerator at overheads below 0.7 % compared to 32-bit-only execution. Finally, we combine our contributions to a research platform for state-of-the-art HeSoCs and demonstrate its performance and flexibility.
Advances In Computers
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Author : Marvin Zelkowitz
language : en
Publisher: Academic Press
Release Date : 2009-06-12
Advances In Computers written by Marvin Zelkowitz and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-12 with Computers categories.
This is volume 72 of Advances in Computers, a series that began back in 1960 and is the oldest continuing series chronicling the ever-changing landscape of information technology. Each year three volumes are produced, which present approximately 20 chapters that describe the latest technology in the use of computers today. In this volume 72, we present the current status in the development of a new generation of high-performance computers. The computer today has become ubiquitous with millions of machines being sold (and discarded) annually. Powerful machines are produced for only a few hundred U.S. dollars, and one of the problems faced by vendors of these machines is that, due to the continuing adherence to Moore's law, where the speed of such machines doubles about every 18 months, we typically have more than enough computer power for our needs for word processing, surfing the web, or playing video games. However, the same cannot be said for applications that require large powerful machines. Applications such as weather and climate prediction, fluid flow for designing new airplanes or automobiles, or nuclear plasma flow require as much computer power as we can provide, and even that is not enough. Today's machines operate at the teraflop level (trillions of floating point operations per second) and this book describes research into the petaflop region (1,015 FLOPS). The six chapters provide an overview of current activities that will provide for the introduction of these machines in the years 2011 through 2015.
Deep Learning For Computer Architects
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Author : Brandon Reagen
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Deep Learning For Computer Architects written by Brandon Reagen 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.
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloadsthemselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.