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Optimizing Linux® Performance: A Hands-On Guide to Linux® Performance ToolsBy
Table of Contents
| Index
The first comprehensive, expert guide for end-to-end Linux
application optimization
Learn to choose the right toolsand use them together to
solve real problems in real production environmentsSuperior application performance is more crucial than
everand in today''s complex production environments, it''s
tougher to ensure, too. If you use Linux, you have extraordinary
advantages: complete source code access, plus an exceptional array
of optimization tools. But the tools are scattered across the
Internet. Many are poorly documented. And few experts know how to
use them together to solve real problems. Now, one of those experts
has written the definitive Linux tuning primer for every
professional: Optimizing Linux® Performance.Renowned Linux benchmarking specialist Phillip Ezolt introduces
each of today''s most important Linux optimization tools, showing
how they fit into a proven methodology for perfecting overall
application performance. Using realistic examples, Ezolt shows
developers how to pinpoint exact lines of source code that are
impacting performance. He teaches sysadmins and application
developers how to rapidly drill down to specific bottlenecks, so
they can implement solutions more quickly. You''ll discover how
to:
Identify bottlenecks even if you''re not familiar with the
underlying system
Find and choose the right performance tools for any
problem
Recognize the meaning of the events you''re measuring
Optimize system CPU, user CPU, memory, network I/O, and disk
I/Oand understand their interrelationships
Fix CPU-bound, latency-sensitive, and I/O-bound applications,
through case studies you can easily adapt to your own
environmentInstall and use oprofile, the advanced systemwide profiler for
Linux systemsIf you''re new to tuning, Ezolt gives you a clear and practical
introduction to all the principles and strategies you''ll need. If
you''re migrating to Linux, you''ll quickly master Linux equivalents
to the tools and techniques you already know. Whatever your
background or environment, this book can help you improve the
performance of all your Linux applicationsincreasing
business value and user satisfaction at the same time.
9.5. Optimizing Process CPU Usage
When a particular process or application has been determined to be a CPU bottleneck, it is necessary to determine where (and why) it is spending its time.Figure 9-3 shows the method for investigating a processs CPU usage.
Figure 9-3.
9.5.1. Is the Process Spending Time in User or Kernel Space?
You can use the time command to determine whether an application is spending its time in kernel or user mode. oprofile can also be used to determine where time is spent. By profiling per process, it is possible to see whether a process is spending its time in the kernel or user space.If the application is spending a significant amount of time in kernel space (greater than 25 percent), go to Section 9.5.2. Otherwise, go to Section 9.5.3.
9.5.2. Which System Calls Is the Process Making, and How Long Do They Take to Complete?
Next, run strace to see Section 9.9.
9.5.3. In Which Functions Does the Process Spend Time?
Next, run oprofile on the application using the cycle event to determine which functions are using all the CPU cycles (that is, which functions are spending all the application time).Keep in mind that although oprofile shows you how much time was spent in a process, when profiling at the function level, it is not clear whether a particular function is hot because it is called very often or whether it just takes a long time to complete.One way to determine which case is true is to acquire a source-level annotation from oprofile and look for instructions/source lines that should have little overhead (such as assignments). The number of samples that they have will approximate the number of times that the function was called relative to other high-cost source lines. Again, this is only approximate because oprofile samples only the CPU, and out-of-order processors can misattribute some cycles.It is also helpful to get a call graph of the functions to determine how the hot functions are being called. To do this, go to Section 9.5.4.
9.5.4. What Is the Call Tree to the Hot Functions?
Next, you Section 9.9.
9.5.5. Do Cache Misses Correspond to the Hot Functions or Source Lines?
Next, run Section 9.9.
