Code Optimization Tips and Techniques
What is your idea on Code Optimization?
Table of Contents:
- Code Optimization
- Advantages of Code Optimization
- Code Optimization Techniques
- Optimization Tips for Python code
- Common Subexpression Elimination and Code Movement
- Dead Code Elimination
- Strength Reduction
- Loop Unrolling
Code optimization is a method of code modification to improve code quality and efficiency.
A program/code may be optimized so that it becomes a smaller size, consumes less memory, executes more rapidly and performs fewer input and output operations. All these together we say it as code optimization.
Advantages of Code Optimization
- Better code readability
- Rapid execution
- Consumption of fewer resources/memory
These are the 3 main advantages of code optimization.
Here are some simple steps we can take code optimized
- Code optimization techniques
- Compile-time evaluation
- Common Subexpression Elimination
- Code Movement
- Dead code elimination
- Strength reduction
- Loop unrolling
All these steps are explained in detail in the video with examples.
Videolink: Code Optimization for Python
During compilation 2 different techniques are used:
- Constant Folding
- Constant Propagation
The details of these two techniques are explained in the above video with examples.
Common Subexpression Elimination
The technique common sub-expression Elimination is also explained in the video with an example.
Another technique is code movement. There may be some statements that need not be there at the part of the loop, so such statements which are written inside the loop need to be removed.
Because there may be some variables that do not play any role related to the loop. We may avoid such things to avoid overload to the runtime. So move the unwanted codes out of the loop whenever you write the loop. Make sure that the only required statements are there inside the loop. If any constant statements are there you may move these statements out of the loop.
Always keep in mind that to minimize the input and output statements. All these code movements are explained with examples in the video.
Dead Code Elimination
Another technique is dead code elimination, this portion explains to eliminate all the unreachable code. Some codes that will never execute, these types of codes are called dead codes. There are many coverage tools available to check how much percentage of our codes are actually used in a program.
Identify dead codes and eliminate unwanted statements using those codes.
Some operations will take more CPU time. The video illustrates strength reduction with an example of arithmetic operations, multiplication, and addition.
The loop statements are complicated for the CPU during run-time because it has to do the checks and updations. These checks are done many times so it is running frequently. You may avoid unwanted checks by the loop unrolling technique. This Loop unrolling technique is illustrated with an example in the video.
Optimization Tips for Python Code
For beginners and students, most of them prefer Python code. So here are some tips for python code optimization.
- Use built-in functions and libraries
- Use keys for sorts
- Use Python multiple assignments to swap variables
- Use local variables if possible.
Use built-in functions and libraries
Many built-in functions are available in python. If you are dealing with any strength handling codes or with any math-related codes always use built-in functions. Never write the entire thing repeatedly. For that, you need to be aware of the most popular libraries and built-in functions and make use of it wisely. You may reuse it and reuse the runtime.
Use keys for sorts
If you are sorting something, always use keys for sorting. Keys will always be unique so that we can reduce the sorting time.
Use Python multiple assignments to swap variables
To swap the variables we can use multiple assignment statements in Python. If you want to do swapping of the variables in any of the codes directly use the multiple assignment statement.
Use local variable of possible
Local variables are more preferred in Python because for all local variables there will be a namespace. So, local variables are retrieved faster than global variables.
First, they will check the local variables then only go for the global variables. So whenever possible use local variables. Try to avoid the “global” keyword. Only when it cannot be avoided use the global variable otherwise always try to use the local variables in functions. Optimization Tips for Python code have been described briefly with samples in the video.
Do you want to learn more about Python Code Optimization?
For beginners, I’d recommend:
Reading various code optimization resources/blogs, just like this one, Or taking a python certification course.
In our course, we teach you to:
- Use built-in functions in Python to create user-defined functions with different argument structures
- Create classes and apply OOPS features such as inheritance, overloading, overriding, etc
- Apply regular expressions for pattern matching
- Create GUI based applications using Tkinter and MySql
- Develop websites using Django Framework.
Everything step-by-step and how to apply it in real-life situations. Covers all the topics from core python programming to web development using Django Framework.
Mastering Python will be a good option for those who are seeking a career in Industry 4.0 trends like Artificial Intelligence (AI), Machine Learning (ML), Data Analysis, etc.