Runtime Optimization Tutorial

1Understanding Runtime Learning Mode

FluentAI's Runtime Learning Mode observes your program's execution patterns and automatically applies optimizations based on actual runtime behavior. This is different from traditional static optimization which makes decisions at compile time.

// Enable learning mode with --learning flag
// fluentai run myprogram.flc --learning

// Your code automatically benefits from runtime optimization
let process_data = (items) => {
    items
        .filter(item => item.value > threshold)
        .map(item => expensive_transform(item))
        .reduce(0, (sum, item) => sum + item.score)
};

2Practical Example: Data Processing Pipeline

Let's walk through a real-world example of processing user analytics data:

// analytics_processor.flc
let process_user_events = (events, user_type) => {
    // This function will be called many times with the same user_type
    events
        .filter(event => {
            // Runtime learning will detect if user_type is always "premium"
            // and optimize this check away
            if (user_type == "premium") {
                event.timestamp > last_week
            } else {
                event.timestamp > last_day
            }
        })
        .map(event => {
            // Complex transformation that benefits from optimization
            {
                "user_id": event.user_id,
                "action": event.action,
                "value": calculate_value(event),
                "category": categorize_event(event)
            }
        })
        .group_by(event => event.category)
        .map_values(events => events.length)
};

// Simulate processing many batches
let run_analytics = () => {
    let total_results = [];
    
    // Process 1000 batches of events
    for i in range(0, 1000) {
        let events = fetch_event_batch(i);
        // After ~100 calls, this will use optimized version
        let results = process_user_events(events, "premium");
        total_results.push(results);
    }
    
    total_results
};

3Optimization Strategies

FluentAI supports multiple optimization strategies that can be applied automatically:

4Writing Optimization-Friendly Code

While learning mode works automatically, you can write code that helps it optimize better:

// ✅ Good: Pure function, easy to optimize
let calculate_score = (user, activity) => {
    let base_score = activity.points;
    let multiplier = user.level * 0.1 + 1.0;
    base_score * multiplier
};

// ❌ Avoid: Side effects make optimization harder
let calculate_score_with_logging = (user, activity) => {
    $("Calculating score...").print();  // Side effect
    let base_score = activity.points;
    let multiplier = user.level * 0.1 + 1.0;
    database.log_calculation(user.id);  // Another side effect
    base_score * multiplier
};

Best Practices:

• Consistent Types: Use consistent types in collections for better inline caching
• Separate Hot and Cold Paths: Keep frequently executed code separate from rarely used code
• Avoid Dynamic Code: Runtime-generated code can't be optimized as effectively
• Use Immutable Data: Immutable structures enable more aggressive optimizations

5Monitoring and Tuning

FluentAI provides tools to monitor optimization effectiveness:

# Run with detailed optimization stats
fluentai run myprogram.flc --learning --stats

# Save optimization model for reuse
fluentai run myprogram.flc --learning --save-model myapp.flai

# Load previous optimization model
fluentai run myprogram.flc --load-model myapp.flai

6Advanced Scenarios

Custom Optimization Hints

// Provide hints to the optimizer
let critical_function = (data) => {
    // @optimize: aggressive
    // Tells optimizer to spend more time optimizing this function
    complex_algorithm(data)
};

// Indicate likely values
let process_request = (request_type) => {
    // @likely: request_type == "GET"
    match request_type {
        "GET" => fast_path(),
        "POST" => slower_path(),
        _ => fallback()
    }
};

Profile-Guided Optimization

# Record production workload
fluentai run server.flc --learning --record-profile prod.profile

# Optimize based on production profile
fluentai compile server.flc --optimize-profile prod.profile -o server.opt

# Run optimized version
fluentai run server.opt

Summary

FluentAI's Runtime Learning Mode provides automatic optimization without manual intervention. Key takeaways:

• Just add --learning flag to enable automatic optimization
• Functions are optimized based on actual runtime behavior
• Write clean, pure functions for best results
• Monitor optimization effectiveness with --stats
• Save and reuse optimization models across runs