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Red Hat Enterprise Linux 8 Essentials Book now available.

Purchase a copy of Red Hat Enterprise Linux 8 (RHEL 8) Essentials in eBook ($24.99) or Print ($36.99) format

Red Hat Enterprise Linux 8 Essentials Print and eBook (ePub/PDF/Kindle) editions contain 31 chapters and over 250 pages

Buy Print Preview Book

Tips for Developers

Every good programming textbook covers problems with memory allocation and the performance of specific functions. As you develop your software, be aware of issues that might increase power consumption on the systems on which the software runs. Although these considerations do not affect every line of code, you can optimize your code in areas which are frequent bottlenecks for performance.
Some techniques that are often problematic include:
  • using threads.
  • unnecessary CPU wake-ups and not using wake-ups efficiently. If you must wake up, do everything at once (race to idle) and as quickly as possible.
  • using [f]sync() unnecessarily.
  • unnecessary active polling or using short, regular timeouts. (React to events instead).
  • not using wake-ups efficiently.
  • inefficient disk access. Use large buffers to avoid frequent disk access. Write one large block at a time.
  • inefficient use of timers. Group timers across applications (or even across systems) if possible.
  • excessive I/O, power consumption, or memory usage (including memory leaks)
  • performing unnecessary computation.
The following sections examine some of these areas in greater detail.

A.1. Using Threads

It is widely believed that using threads makes applications perform better and faster, but this is not true in every case.
Python
Python uses the Global Lock Interpreter[1], so threading is profitable only for larger I/O operations. Unladen-swallow [2] is a faster implementation of Python with which you might be able to optimize your code.
Perl
Perl threads were originally created for applications running on systems without forking (such as systems with 32-bit Windows operating systems). In Perl threads, the data is copied for every single thread (Copy On Write). Data is not shared by default, because users should be able to define the level of data sharing. For data sharing the threads::shared module has to be included. However, data is not only then copied (Copy On Write), but the module also creates tied variables for the data, which takes even more time and is even slower. [3]
C
C threads share the same memory, each thread has its own stack, and the kernel does not have to create new file descriptors and allocate new memory space. C can really use the support of more CPUs for more threads. Therefore, to maximize the performance of your threads, use a low-level language like C or C++. If you use a scripting language, consider writing a C binding. Use profilers to identify poorly performing parts of your code. [4]

 
 
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