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.
The following sections examine some of these areas in greater detail.
It is widely believed that using threads makes applications perform better and faster, but this is not true in every case.
Python uses the Global Lock Interpreter[], so threading is profitable only for larger I/O operations. Unladen-swallow [] is a faster implementation of Python with which you might be able to optimize your code.
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. []
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. []
