Computer Architecture Performance Evaluation Methods
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| Author | : Lieven Eeckhout |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 129 |
| Release | : 2010 |
| Genre | : Computers |
| ISBN | : 1608454673 |
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
| Author | : Lieven Eeckhout |
| Publisher | : Springer Nature |
| Total Pages | : 132 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017277 |
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
| Author | : C. M. Krishna |
| Publisher | : John Wiley & Sons |
| Total Pages | : 408 |
| Release | : 1995-10-14 |
| Genre | : Computers |
| ISBN | : 9780818670947 |
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.
| Author | : Paul J. Fortier |
| Publisher | : Digital Press |
| Total Pages | : 541 |
| Release | : 2003-06-25 |
| Genre | : Computers |
| ISBN | : 1555582605 |
| Author | : Lizhong Chen |
| Publisher | : Springer Nature |
| Total Pages | : 124 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017706 |
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.
| Author | : Karu Sankaralingam |
| Publisher | : Springer Nature |
| Total Pages | : 144 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017730 |
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.
| Author | : Brandon Reagen |
| Publisher | : Springer Nature |
| Total Pages | : 109 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017560 |
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 workloads themselves, 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.
| Author | : Ruby B. Lee |
| Publisher | : Springer Nature |
| Total Pages | : 96 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017420 |
Design for security is an essential aspect of the design of future computers. However, security is not well understood by the computer architecture community. Many important security aspects have evolved over the last several decades in the cryptography, operating systems, and networking communities. This book attempts to introduce the computer architecture student, researcher, or practitioner to the basic concepts of security and threat-based design. Past work in different security communities can inform our thinking and provide a rich set of technologies for building architectural support for security into all future computers and embedded computing devices and appliances. I have tried to keep the book short, which means that many interesting topics and applications could not be included. What the book focuses on are the fundamental security concepts, across different security communities, that should be understood by any computer architect trying to design or evaluate security-aware computer architectures.
| Author | : David J. Lilja |
| Publisher | : Cambridge University Press |
| Total Pages | : 284 |
| Release | : 2005-09-08 |
| Genre | : Computers |
| ISBN | : 9780521646703 |
Sets out the fundamental techniques used in analyzing and understanding the performance of computer systems.
| Author | : Tzvetan Metodi |
| Publisher | : Springer Nature |
| Total Pages | : 192 |
| Release | : 2022-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017315 |
Quantum computers can (in theory) solve certain problems far faster than a classical computer running any known classical algorithm. While existing technologies for building quantum computers are in their infancy, it is not too early to consider their scalability and reliability in the context of the design of large-scale quantum computers. To architect such systems, one must understand what it takes to design and model a balanced, fault-tolerant quantum computer architecture. The goal of this lecture is to provide architectural abstractions for the design of a quantum computer and to explore the systems-level challenges in achieving scalable, fault-tolerant quantum computation. In this lecture, we provide an engineering-oriented introduction to quantum computation with an overview of the theory behind key quantum algorithms. Next, we look at architectural case studies based upon experimental data and future projections for quantum computation implemented using trapped ions. While we focus here on architectures targeted for realization using trapped ions, the techniques for quantum computer architecture design, quantum fault-tolerance, and compilation described in this lecture are applicable to many other physical technologies that may be viable candidates for building a large-scale quantum computing system. We also discuss general issues involved with programming a quantum computer as well as a discussion of work on quantum architectures based on quantum teleportation. Finally, we consider some of the open issues remaining in the design of quantum computers. Table of Contents: Introduction / Basic Elements for Quantum Computation / Key Quantum Algorithms / Building Reliable and Scalable Quantum Architectures / Simulation of Quantum Computation / Architectural Elements / Case Study: The Quantum Logic Array Architecture / Programming the Quantum Architecture / Using the QLA for Quantum Simulation: The Transverse Ising Model / Teleportation-Based Quantum Architectures / Concluding Remarks
