Machine-Independent Code Optimization Techniques: Improving Intermediate Code

Explore machine-independent code optimization techniques used to enhance program performance before generating machine code. This guide covers various optimization methods, including constant folding, dead code elimination, and common subexpression elimination, improving code efficiency and readability.

Machine-Independent Code Optimization Techniques

What is Machine-Independent Optimization?

Machine-independent optimization focuses on improving a program's intermediate code representation (IR) before generating machine code. This means that the optimizations are performed without considering the specific target machine's architecture (CPU, memory, etc.). These optimizations improve the program's structure and eliminate inefficiencies like redundant calculations, unused variables, or unnecessary copies. This results in smaller and faster code that improves the overall program’s performance and readability.

Common Machine-Independent Optimization Techniques

Several techniques are used for machine-independent optimization:

1. Compile-Time Evaluation:

Expressions are evaluated during compilation instead of at runtime. This reduces runtime overhead.

// Before optimization
z = 5 * (45.0 / 5.0) * r;

// After optimization
z = 45 * r;

2. Variable Propagation:

Replaces variables with their computed values, simplifying expressions and reducing calculations.

// Before optimization
x = a;
y = x * b;

// After optimization
y = a * b;

3. Dead Code Elimination:

Removes statements that have no effect on the program’s outcome (e.g., assigning a value to a variable that's never used).

// Before optimization
x = a; // 'x' is never used
y = a * b;

// After optimization
y = a * b;

4. Code Motion:

Moves loop-invariant computations (calculations whose results don’t change within the loop) outside the loop. This reduces calculations inside the loop, improving performance.

// Before optimization
for (i = 0; i < 10; i++) {
  int temp = 10;
  // ... code ...
}

// After optimization
int temp = 10;
for (i = 0; i < 10; i++) {
  // ... code ...
}

5. Induction Variable Elimination and Strength Reduction:

Strength reduction replaces computationally expensive operations (multiplication) with cheaper ones (addition). Induction variables (variables whose value changes predictably within a loop) can be simplified. For example, in a loop that repeatedly multiplies by 4, this multiplication can be replaced by a series of addition operations.

// Before optimization
for (int i = 0; i < 10; i++) {
  y = i * 4;
}

// After optimization
int t = 0;
for (int i = 0; i < 10; i++) {
  y = t;
  t += 4;
}