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As computers become more complex, the number and complexity of the tasks facing the computer architect have increased. Computer performance often depends in complex way on the design parameters and intuition that must be supplemented by performance studies to enhance design productivity. This book introduces computer architects to computer system performance models and shows how they are relatively simple, inexpensive to implement, and sufficiently accurate for most purposes. It discusses the development of performance models based on queuing theory and probability. The text also shows how they are used to provide quick approximate calculations to indicate basic performance tradeoffs and narrow the range of parameters to consider when determining system configurations. It illustrates how performance models can demonstrate how a memory system is to be configured, what the cache structure should be, and what incremental changes in cache size can have on the miss rate. A particularly deep knowledge of probability theory or any other mathematical field to understand the papers in this volume is not required.
Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.
The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerators that have become increasingly important for mainstream applications.
With the growing need for effective communication networks in telecommunications and distributed computer systems, system designers need to be aware of the developments of sophisticated models for evaluating system performance. This book is ideally designed for performance engineers and system designers with the main focus of the text on queueing network models.
In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms traditional design exploration techniques. This book should help a skilled systems designer to learn techniques for using MILP in their problems, and the skilled optimization expert to understand the types of computer systems problems that MILP can be applied to.
The computing world is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation. This book focuses on the shift, exploring the ways in which software and technology in the 'cloud' are accessed by cell phones, tablets, laptops, and more
Offering a carefully reviewed selection of over 50 papers illustrating the breadth and depth of computer architecture, this text includes insightful introductions to guide readers through the primary sources.
This textbook provides a clear and concise introduction to computer architecture and implementation. Two important themes are interwoven throughout the book. The first is an overview of the major concepts and design philosophies of computer architecture and organization. The second is the early introduction and use of analytic modeling of computer performance. A unique feature of the book is that memory systems are discussed before processor implementations. The book contains many worked examples and over 130 homework exercises. It is an ideal textbook for a one-semester undergraduate course in computer architecture and implementation.
Performance evaluation is at the foundation of computer architecture research and development. Contemporary microprocessors are so complex that architects cannot design systems based on intuition and simple models only. Adequate performance evaluation methods are absolutely crucial to steer the research and development process in the right direction. However, rigorous performance evaluation is non-trivial as there are multiple aspects to performance evaluation, such as picking workloads, selecting an appropriate modeling or simulation approach, running the model and interpreting the results using meaningful metrics. Each of these aspects is equally important and a performance evaluation method that lacks rigor in any of these crucial aspects may lead to inaccurate performance data and may drive research and development in a wrong direction. The goal of this book is to present an overview of the current state-of-the-art in computer architecture performance evaluation, with a special emphasis on methods for exploring processor architectures. The book focuses on fundamental concepts and ideas for obtaining accurate performance data. The book covers various topics in performance evaluation, ranging from performance metrics, to workload selection, to various modeling approaches including mechanistic and empirical modeling. And because simulation is by far the most prevalent modeling technique, more than half the book's content is devoted to simulation. The book provides an overview of the simulation techniques in the computer designer's toolbox, followed by various simulation acceleration techniques including sampled simulation, statistical simulation, parallel simulation and hardware-accelerated simulation. Table of Contents: Introduction / Performance Metrics / Workload Design / Analytical Performance Modeling / Simulation / Sampled Simulation / Statistical Simulation / Parallel Simulation and Hardware Acceleration / Concluding Remarks
A book for experts and practitioners, emphasizing the intuition and reasoning behind definitions and derivations related to evaluating computer systems performance.