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This is a short, occasionally funny, book on how to solve and avoid application and/or computer performance problems. I wrote it to give back the knowledge, insights, tips, and tricks I was given over the last 25 years of my computing career. It shows practical ways to use key performance laws and gives well tested advice on how (and when) to do performance monitoring, capacity planning, load testing, and performance modeling. It works for any application or collection of computers because it teaches you how to decipher whatever meters they give you and how to discover more about those meters than the documentation reveals. This book covers the things that will always be true no matter what technology you are using. It will continue to be useful 20 years from now when today's technology, if it runs at all, will look as quaint as a mechanical cuckoo clock. There is no complex math required; yet it allows you to easily use some fairly advanced techniques. Simple arithmetic, and a spreadsheet program, is all that is required of you. Lastly, it helps with the human side of performance. It shows you how to get the help you need and how to present your findings (good or bad) all the way up to the CIO level.
Sets out the fundamental techniques used in analyzing and understanding the performance of computer systems.
Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.
Computer Performance Modeling Handbook
Makes performance analysis and queueing theory concepts simple to understand and available to anyone with a background in high school algebra Presents the practical application of these concepts in the context of modern, distributed, computer system designs Packed with helpful examples that are based on the author's experience analyzing the performance of large-scale systems over the past 20 years.
Computer Systems Organization -- Performance of Systems.
Practical, real-world solutions are given to potential problems covering the entire system life cycle. This book describes how to map real-life systems (databases, data centers, and e-commerce applications) into analytic performance models. The authors elaborate upon these models and use them to help the reader better understand performance issues.
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.
This book is written for computer engineers and scientists active in the development of software and hardware systems. It supplies the understanding and tools needed to effectively evaluate the performance of individual computer and communication systems. It covers the theoretical foundations of the field as