- Hardcover: 474 pages
- Publisher: Springer; 2nd ed. 2011 edition (August 4, 2011)
- Language: English
- ISBN-10: 3642225829
- ISBN-13: 978-3642225826
- Product Dimensions: 9.2 x 1.2 x 6.3 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 3.9 out of 5 stars See all reviews (3 customer reviews)
- Amazon Best Sellers Rank: #2,543,238 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Analyzing Computer System Performance with Perl::PDQ 2nd ed. 2011 Edition
Use the Amazon App to scan ISBNs and compare prices.
O'Reilly Learning Series
Featured 'Learning' Series from O'Reilly Media. See more
Customers who viewed this item also viewed
What other items do customers buy after viewing this item?
From the reviews
"The literature of computer performance analysis is generally composed of two groups: heavy theoretical treatises and performance cookbooks. […] Surprisingly, this book is exceptionally well balanced between theory and practice […] . I strongly recommend this book, both for the novice practitioner and for the experienced performance analyst. Both can extract a vast array of benefits, ranging from understanding the theoretical concepts of performance modeling, to building for themselves a powerful modeling tool […] ." Jair Merlo, Computing Reviews, May 2005
From the reviews of the second edition:
“Besides its case studies on applying queueing theory to the analysis of computer performance, this book on queueing circuits also includes a quick user guide to Perl Pretty Damn Quick (PDQ), a reference manual, and numerous examples. … The book is written to be a field manual for enlightened practitioners of performance analysis, or possibly a school textbook. … It is a laudable achievement to make performance analysis theoretical foundations available to anyone who is interested and can master high-school algebra.” (A. Squassabia, ACM Computing Reviews, December, 2011)
From the Back Cover
To solve performance problems in modern computing infrastructures, often comprising thousands of servers running hundreds of applications, spanning multiple tiers, you need tools that go beyond mere reporting. You need tools that enable performance analysis of application workflow across the entire enterprise. That's what PDQ (Pretty Damn Quick) provides. PDQ is an open-source performance analyzer based on the paradigm of queues. Queues are ubiquitous in every computing environment as buffers, and since any application architecture can be represented as a circuit of queueing delays, PDQ is a natural fit for analyzing system performance.
Building on the success of the first edition, this considerably expanded second edition now comprises four parts. Part I contains the foundational concepts, as well as a new first chapter that explains the central role of queues in successful performance analysis. Part II provides the basics of queueing theory in a highly intelligible style for the non-mathematician; little more than high-school algebra being required. Part III presents many practical examples of how PDQ can be applied. The PDQ manual has been relegated to an appendix in Part IV, along with solutions to the exercises contained in each chapter.Throughout, the Perl code listings have been newly formatted to improve readability. The PDQ code and updates to the PDQ manual are available from the author's web site at www.perfdynamics.com
Browse award-winning titles. See more
If you are a seller for this product, would you like to suggest updates through seller support?
Top Customer Reviews
The 2nd edition has several newly written introductory chapters to provide the needed
context for someone new to performance analysis, for example, the new introduction
to queueing theory is an wonderful exposition for a newbie. I only wished I had the
2nd edition when I bought the 1st edition.
Here is the link to more details on Dr. Gunther's site.
Now all we need is a Kindle edition.
The "universal scalability law" to which he refers on p. 5 extends Amdahl's Law via the addition of a "queuing" term that models effects such as data exchange between parallel processes. And although Dr. Gunther suggests that this queuing term ought to grow linearly with the number of parallel processes, this queuing term depends on the specific communication architecture of the computer system and can grow non-linearly, for example, as log to the base two of the number of processes.
This logarithmic growth law can occur because one processor may not communicate directly with all other processors. Instead, one processor may send information to two other processors, and each of those two processors may send information to two more processors, and so forth. Therefore, in order to model the communication that occurs in such a communication cascade, the queuing term should grow as log(n).
Moreover, performance data that are obtained from current parallel computer systems do not always conform to Dr. Gunther's "universal" scalability "law" under other conditions. For example, a large volume of data that exceeds the capacity of the total cache memory when distributed across a few processors may well fit into total cache memory when distributed across a larger number of processors. Under these conditions, the scalability for the larger number of processors appears to grow "super-linearly" relative to the scalability of a few processors. However, on p. 5 of his book, Dr. Gunther states that "the data showed efficiencies of greater than 100%, which is simply not possible." Efficiencies of greater than 100% are not only possible, they are commonly observed due to cache memory effects. Thus, although Dr. Gunther's book is a useful introduction to the subject of measuring and modeling the behavior of parallel computer architectures, his universal scalability law should not be considered to be universal.