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.

The goal of this book is to present an overview of the current state-of-the-art in computer architecture performance evaluation. The book covers various aspects that relate to performance evaluation, ranging from performance metrics, to workload selection, to various modeling approaches such as analytical modeling and simulation. And because simulation is by far the most prevalent modeling technique in computer architecture evaluation, the book spends more than half its content on simulation, covering an overview of the various simulation techniques in the computer designer's toolbox, followed by various simulation acceleration techniques such as sampled simulation, statistical simulation, and parallel and hardware-accelerated simulation. The evaluation methods described in this book have a primary focus on performance. Although performance remains to be a key design target, it no longer is the sole design target. Power consumption and reliability have quickly become primary design concerns, and today they probably are as important as performance. Other important design constraints relate to cost, thermal issues, yield, etc. This book focuses on performance evaluation methods only. This does not compromise on the importance and general applicability of the techniques described in this book because power and reliability models are typically integrated into existing performance models. These integrated models pose similar challenges to the ones handled in this book. The book also focuses on presenting fundamental concepts and ideas. The book does not provide much quantitative data. Although quantitative data is crucial to performance evaluation, to understand the fundamentals of performance evaluation methods it is not. Moreover, quantitative data from different sources may be hard to compare, and may even be misleading, because the contexts in which the results were obtained may be very different - a comparison based on these numbe

Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.

This update of the popular book on computer architecture presents design ideas embodied in many high-performance machines and stresses techniques for evaluating them. Stone develops a proper understanding of the design process by treating the various trade-offs that exist in designing choices, and shows how good designs make efficient use of technology.Features Teaches techniques for the design and analysis of high-performance machines Develops students' intuition for design by treating various tradeoffs that exist in design choices Discusses many important topics: RISC architectures, interconnection meshes, Cache coherent and multiprocessors, and Cache Memory. Includes enhanced descriptions of RISC Processors Expands material on Cache Memory Analysis Current technology in RISC with a focused look on super scalar Additional memory models and techniques for doing Cache design New porposals for coherent memory systems in System C parallel processors Both design and thought problems and problems with limiting parameters are provided 0201526883B04062001

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 fast development of networking and software technologies, information processing infrastructure and applications have been growing at an impressive rate in both size and complexity, to such a degree that the design and development of high performance and scalable data processing systems and networks have become an ever-challenging issue. As a result, the use of performance modeling and m- surementtechniquesas a critical step in designand developmenthas becomea c- mon practice. Research and developmenton methodologyand tools of performance modeling and performance engineering have gained further importance in order to improve the performance and scalability of these systems. Since the seminal work of A. K. Erlang almost a century ago on the mod- ing of telephone traf c, performance modeling and measurement have grown into a discipline and have been evolving both in their methodologies and in the areas in which they are applied. It is noteworthy that various mathematical techniques were brought into this eld, including in particular probability theory, stochastic processes, statistics, complex analysis, stochastic calculus, stochastic comparison, optimization, control theory, machine learning and information theory. The app- cation areas extended from telephone networks to Internet and Web applications, from computer systems to computer software, from manufacturing systems to s- ply chain, from call centers to workforce management.