Code Optimization in Compiler Design

Why Optimize the code? 

Optimizing code in compiler design is important because it directly affects the performance of the compiled code. A well-optimized code runs faster and consumes fewer resources, leading to improved overall system performance and reduced energy consumption. Additionally, optimization can reduce the size of the generated code, which is important for embedded systems with limited memory. The optimization process can also help identify and eliminate bottlenecks in the code, leading to more efficient algorithms and improved software design. Overall, optimization is a critical step in the compiler design process that can greatly improve the end-user experience.

When to Optimize?

Code Optimization in compiler design is carried out at the end of the development. It is added at the end because, as a result, it reduces the readability(which cannot be afforded at the beginning of the development), and it adds code that results in an increase in the performance.

Types of Code Optimization

The code optimization process can be broadly classified into two types :

• Machine Independent Optimization
• Machine Dependent Optimization

1. Machine Independent Optimization

This step of code optimization aims to optimize the intermediate code to produce a better target code. No CPU registers or absolute memory addresses are involved in the section of the intermediate code that is translated here.

2. Machine Dependent Optimization

After the target code has been created and converted to fit the target machine architecture, machine-dependent optimization is performed. It may use absolute memory references rather than relative memory accesses and requires CPU registers. Machine-dependent optimizers make a concerted attempt to maximize the memory hierarchy's benefits.

Also see, Cross Compiler

Code Optimization Techniques in Compiler Design

A code has several statements, loops, branches, etc. So code optimization must be performed on all of them. The code optimization is done differently, considering the following.

1. Loop Optimization

The majority of programs in the system operate in a loop. It is vital to optimize the loops to save CPU cycles and memory. The following strategies can be used to improve loops.

• Loop-invariant code: It is a piece of code that sits in the loop and computes the same value each time an iteration is performed. This code may be moved out of the loop by storing it to be calculated just once rather than with each iteration.
• Induction analysis: If a loop-invariant value changes the value of a variable within the loop, it is termed an induction variable.
• Strength reduction: Some expressions use more CPU cycles, time, and memory than others. These expressions should be replaced with less expensive expressions without sacrificing the expression's output. For example, multiplication (x * 2) uses more CPU cycles than (x 1) but produces the same output.

Figure 1 - Loop Code flow chart

2. Partially dead code

Some code statements include calculated values utilized only in particular conditions, i.e., the values are used sometimes and not others. Partially dead-code refers to such codes.

The control flow diagram below shows a program section in which the variable 'a is utilized to assign the output of the equation 'x * y'. Let's pretend that the ‘a variable's value is never utilized within the loop. 'a' is given the variable 'z' value, which will be utilized later in the program, immediately after the control leaves the loop. We may infer that because the assignment code 'a' is never utilized anywhere, it is suitable for deletion.

Figure 2 - Dead Code flow chart

Similarly, the conditional statement in the image above is always false, meaning that the code written in the "true" case will never be run and so may be eliminated.

3. Unreachable Code Elimination

A control flow graph should be created first. An inaccessible code block does not have an incoming edge. The inaccessible branches can be deleted after continual propagation and folding.

4. Function Inlining 

The body of the function takes the place of a function call. This saves a lot of time by eliminating the need to copy all parameters, store the return address, and so on. Let us explain this with an example below:

int addtwonum(int a, int b){
	return a+b;
}
int subtract(int a, int b){
	return addtwonum(a, -b);
}

Here, we see that by negating one of the numbers according to the context of the scenario, we call the addition function for subtraction. Now, let us see the below snippet:

int subtract(int a, int b){
return a+ (-b);
}

Here, we did the work of the function addtwonum itself into the subtract function. This is function inlining.

5. Function Cloning 

For different calling arguments, specialized codes for a function are constructed. Overloading a function is an example of this. We can understand it with the following snippet:

void solve(int a){
	….
}
void solve(int a, int b){
	….
}
void solve(int a, float b, long c){
	….
}

We can see that the function's name is the same(solve), but one of them will be called according to the different parameters being passed to it.

6. Partial Redundancy

In a parallel route, redundant expressions are calculated many times without changing the operands. Partial-redundant expressions, on the other hand, are calculated several times along a path without changing the operands. By employing a code-motion approach, loop-invariant code may be rendered largely redundant.

An example of a partially redundant code can be:

If (condition) {
   a = y OP z;
} else {
   ...
}
c = y OP z;

We assume that the operands' values (y and z) do not change when variable an is assigned to variable c. If the condition statement is true, y OP z is calculated twice; otherwise, it is computed once. As stated below, code motion may be utilized to remove redundancy:

If (condition) {
   ...
   tmp = y OP z;
   a = tmp;
   ...
} else {
   ...
   tmp = y OP z;
}
c = tmp;

y OP z should only be computed once, regardless of whether the condition is true or not.

Where to apply Optimization?

Optimization should be applied at various stages of software development:

1. Algorithm Design: Optimize algorithms for better time and space complexity.
2. Code Level: Apply optimizations to source code to improve execution efficiency.
3. Compiler Level: Employ compiler optimizations during compilation to generate optimized machine code.
4. Database Queries: Optimize database queries for faster data retrieval.
5. Network Communication: Optimize network protocols and data transmission for improved performance.

Advantages of Code Optimization

The advantages of Code Optimization are:

• Improved Performance: Optimized code runs faster and consumes fewer system resources.
• Reduced Execution Time: Optimization reduces execution time, enhancing overall system responsiveness.
• Resource Efficiency: Optimized code consumes less memory and CPU cycles, leading to better resource utilization.
• Enhanced Scalability: Optimized systems can handle larger workloads without significant performance degradation.
• Better User Experience: Faster response times and smoother execution improve user satisfaction.

Disadvantages of Code Optimization

The disadvantages of Code Optimization are:

• Complexity: Optimization techniques often introduce complexity to code, making it harder to understand and maintain.
• Debugging Challenges: Optimized code may obscure bugs and make debugging more challenging.
• Increased Development Time: Optimization requires additional time and effort during development.
• Platform Dependencies: Optimization techniques may be platform-specific, limiting portability.
• Diminished Readability: Over-optimization can lead to code that is difficult to read and comprehend, hampering collaboration and code reviews.

Frequently Asked Questions

What is the need of code optimization in compiler design?

Code optimization in compiler design improves the performance and efficiency of the compiled code, reducing resource consumption and improving system performance. Code optimization is crucial for embedded systems with limited memory, as it helps reduce the size of the generated code.

What are different issues in code optimization in compiler design?

These limitations can make compilers less suitable for certain types of software development, particularly where low-level control and optimization are critical. Compilers have some disadvantages, such as:

• longer compile time
• potential loss of performance compared to hand-written assembly code
• difficulty in debugging optimized code. 

What are the key areas of code optimization in compiler design?

The key areas of code optimization in compiler design are instruction scheduling, register allocation, loop unrolling, dead code elimination, constant propagation, and function inlining. These techniques aim to make code faster, more efficient, and smaller while preserving its functionality. 

Why do we need code optimization in the compiler?

Code optimization is a program transformation approach that aims to enhance code by reducing resource consumption (i.e., CPU, Memory) while maintaining high performance. High-level general programming structures are replaced with highly efficient low-level programming codes.

Conclusion

We have discussed the concept of code optimization in compiler design and its techniques in this article. We started with its meaning, why to optimize, and when to optimize. Then, we looked at different types of code optimizations and different code optimization techniques.

You can find more articles related to code optimization here.

Recommended Reading:

• First and Follow in Compiler Design
• Backpatching In Compiler Design
• Phases of a Compiler
• Phases of compiler