Performance Optimization

Optimizing your bot's performance ensures it executes quickly, avoids timeouts, and can implement sophisticated strategies without slowdowns. This guide covers techniques for writing efficient bot code.

Understanding Turn Time Limits

Each bot has a limited time budget per turn (default: 30,000 microseconds or 30 milliseconds).

Turn Timeout Consequences

If your bot exceeds the turn timeout:

• The bot skips that turn (no commands executed)
• Repeated timeouts can result in penalties
• In extreme cases, the bot may be disqualified

Typical Execution Times

Bot Complexity	Typical Execution Time
Simple bot (basic movement, linear targeting)	< 1 ms
Intermediate bot (pattern detection, statistical targeting)	2-10 ms
Advanced bot (wave surfing, guess-factor targeting)	10-25 ms
Complex statistical bot (large data structures, clustering)	15-30 ms

The goal: Stay well under 30ms to maintain consistent performance.

TPS vs. Computation Time

TPS (Turns Per Second) controls visualization speed, not computation limits:

• Visual speed: How fast you see the battle (adjustable in GUI)
• Computation time: The actual limit per bot per turn (fixed at 30ms default)

See TPS for visualization speed control.

Important: Even at 60 TPS visual speed, your bot still has 30ms per turn. TPS affects display only, not bot execution.

Optimization Techniques

Algorithm Efficiency

Choose appropriate algorithms:

• Linear targeting - O(1), very fast
• Circular targeting - O(1) with trigonometry, still fast
• Statistical targeting - O(n) where n = data points, potentially slow
• Pattern matching - O(n×m) where n = history length, m = pattern length, can be very slow

Optimize loops:

// Inefficient - creates ArrayList every turn
for (Wave wave : new ArrayList<>(waves)) {
    // Process wave
}

// Better - iterate directly
Iterator<Wave> it = waves.iterator();
while (it.hasNext()) {
    Wave wave = it.next();
    if (wave.hasHit()) {
        it.remove();
    }
}

Data Structure Selection

Choose efficient data structures:

For frequent insertions/removals:

• LinkedList (Java)
• deque (Python)
• LinkedList<T> (C#)

For quick lookups:

• HashMap (Java)
• dict (Python)
• Dictionary<TKey, TValue> (C#)

For sorted data:

• TreeMap (Java)
• SortedDict (Python's sortedcontainers)
• SortedDictionary<TKey, TValue> (C#)

Caching Calculations

Avoid recalculating values that don't change:

// Bad - calculates every turn
double enemyDistance = Math.hypot(
    enemy.getX() - getX(),
    enemy.getY() - getY()
);

// Better - cache until enemy moves
private double cachedEnemyDistance;
private long lastEnemyUpdate;

void onScannedBot(ScannedBotEvent e) {
    cachedEnemyDistance = e.getDistance();
    lastEnemyUpdate = getTime();
}

Trigonometry Optimization

Trigonometric functions are relatively expensive:

Pre-compute where possible:

// Pre-compute common angles
private static final double[] SIN_TABLE = new double[360];
private static final double[] COS_TABLE = new double[360];

static {
    for (int i = 0; i < 360; i++) {
        double radians = Math.toRadians(i);
        SIN_TABLE[i] = Math.sin(radians);
        COS_TABLE[i] = Math.cos(radians);
    }
}

// Fast lookup (approximate)
public double fastSin(double degrees) {
    return SIN_TABLE[(int) degrees % 360];
}

Use built-in optimizations:

// Slower
double distance = Math.sqrt(dx * dx + dy * dy);

// Faster (when available)
double distance = Math.hypot(dx, dy);

Limiting Data Storage

Large data structures slow down iteration:

Limit history size:

private static final int MAX_HISTORY = 1000;

void recordData(ScanData data) {
    history.add(data);
    if (history.size() > MAX_HISTORY) {
        history.remove(0); // Remove oldest
    }
}

Use circular buffers:

private ScanData[] history = new ScanData[MAX_HISTORY];
private int historyIndex = 0;

void recordData(ScanData data) {
    history[historyIndex++ % MAX_HISTORY] = data;
}

Lazy Evaluation

Defer expensive calculations until necessary:

// Bad - calculates even if not needed
double angle = calculateBestAngle();
if (shouldFire()) {
    setFire(angle);
}

// Better - only calculate when needed
if (shouldFire()) {
    double angle = calculateBestAngle();
    setFire(angle);
}

Profiling Your Bot

Identifying Bottlenecks

Measure execution time of different sections:

long start = System.nanoTime();
performTargeting();
long targetingTime = System.nanoTime() - start;

start = System.nanoTime();
performMovement();
long movementTime = System.nanoTime() - start;

if (getTime() % 100 == 0) {
    System.out.println("Targeting: " + targetingTime/1000 + "µs");
    System.out.println("Movement: " + movementTime/1000 + "µs");
}

Finding Performance Issues

Common performance bottlenecks:

• Excessive object creation - Creates garbage collection pressure
• Nested loops - O(n²) or worse complexity
• Large data structure iteration - Processing thousands of entries per turn
• Unnecessary calculations - Computing values that won't be used

Memory Management

Garbage Collection

Minimize object creation in the main loop:

// Bad - creates new objects every turn
Point2D.Double myPos = new Point2D.Double(getX(), getY());

// Better - reuse objects
private Point2D.Double myPos = new Point2D.Double();

public void run() {
    while (true) {
        myPos.setLocation(getX(), getY());
        // Use myPos
    }
}

Memory Leaks

Remove unused data:

// Don't forget to clean up old waves
waves.removeIf(Wave::hasPassedTarget);

// Don't store references to destroyed bots
if (enemy.getEnergy() <= 0) {
    enemyData.remove(enemy.getName());
}

Platform-Specific Considerations

Java/Kotlin

• JIT compilation warms up over time (first few rounds may be slower)
• Use ArrayList for fast iteration, HashMap for lookups
• Avoid Stream API in hot loops (adds overhead)

Python

• Use numpy for numerical calculations (much faster than pure Python)
• List comprehensions faster than manual loops
• Consider __slots__ for data classes to reduce memory

C#

• Structs faster than classes for small data (no heap allocation)
• Use List<T> and Dictionary<TKey, TValue> for performance
• LINQ convenient but adds overhead in tight loops

Testing Performance

Benchmarking Improvements

Compare execution times before/after optimization:

1. Add timing measurements to key sections
2. Run 35+ round battles for statistical validity
3. Compare average execution times
4. Ensure win rate doesn't decrease with optimizations

Avoiding Premature Optimization

Follow this priority:

1. Correctness first - Make it work
2. Clarity second - Make it readable
3. Performance third - Make it fast (only where needed)

Profile before optimizing - Don't guess what's slow, measure it.

Further Reading

• TPS - Visualization speed vs. computation time
• Testing & Debugging Guide - Profiling and performance testing
• Physics - Understanding game mechanics for efficient calculations
• APIs - Bot API documentation for each language
• The Book of Robocode - Advanced algorithmic techniques and implementations