# APE Performance Tuning Guide (v1.0.0)

**Author:** David Van Aelst  
**Status:** Scaffold - implementation pending  
**Version:** 1.0.0

---

## Overview

This guide covers performance optimization techniques for APE programs, including benchmarking, profiling, and best practices.

## Benchmarking

### Basic Benchmarking

```python
from ape.benchmarks import BenchmarkRunner

runner = BenchmarkRunner(warmup_iterations=5)

# Benchmark a function
result = runner.benchmark(
    name="fibonacci",
    func=lambda: fib(30),
    iterations=100
)

print(result)
```

**Output:**
```
fibonacci:
  Iterations: 100
  Total time: 2.450000s
  Average: 0.024500s
  Min/Max: 0.023100s / 0.026700s
  Std dev: 0.000800s
```

### Comparing Implementations

```python
runner = BenchmarkRunner()

implementations = {
    "recursive": lambda: fib_recursive(30),
    "iterative": lambda: fib_iterative(30),
    "memoized": lambda: fib_memoized(30)
}

results = runner.compare(implementations, iterations=100)

for name, result in results.items():
    print(f"{name}: {result.avg_time:.6f}s")
```

**Output:**
```
recursive: 0.024500s
iterative: 0.000120s  (204x faster)
memoized: 0.000015s   (1633x faster)
```

## Profiling

### Execution Profiling

```python
runner = BenchmarkRunner()

profile_data = runner.profile(
    name="complex_task",
    func=lambda: process_large_dataset()
)

# Analyze hotspots
print(profile_data.hotspots)
```

### Task-Level Profiling

APE provides built-in profiling for tasks:

```ape
task process_data(items: List<Any>):
    results = []
    for item in items:
        result = expensive_operation(item)
        results.append(result)
    return results

# Enable profiling
profile = execute_with_profile(process_data, items)

print("Task execution time:", profile.total_time)
print("Iterations:", profile.iteration_count)
```

## Optimization Techniques

### 1. Algorithm Selection

**Choose efficient algorithms:**

```ape
# BAD: O(n²) nested loops
task find_duplicates_slow(items: List<Integer>):
    duplicates = []
    for i in range(items.length()):
        for j in range(i + 1, items.length()):
            if items[i] == items[j]:
                duplicates.append(items[i])
    return duplicates

# GOOD: O(n) with map
task find_duplicates_fast(items: List<Integer>):
    seen: Map<Integer, Boolean> = {}
    duplicates = []
    for item in items:
        if seen.has(item):
            duplicates.append(item)
        else:
            seen.set(item, true)
    return duplicates
```

### 2. Reduce Function Calls

**Minimize overhead:**

```ape
# BAD: Repeated function calls
task sum_squares_slow(n: Integer):
    total = 0
    for i in range(n):
        total = total + square(i)
    return total

# GOOD: Inline simple operations
task sum_squares_fast(n: Integer):
    total = 0
    for i in range(n):
        total = total + (i * i)
    return total
```

### 3. Hoist Loop-Invariant Code

**Move calculations outside loops:**

```ape
# BAD: Recalculating inside loop
task process_items(items: List<Any>, config: Map):
    threshold = config["processing"]["threshold"]
    for item in items:
        if item.value > threshold:  # threshold recalculated each iteration
            process(item)

# GOOD: Calculate once
task process_items_optimized(items: List<Any>, config: Map):
    threshold = config["processing"]["threshold"]
    for item in items:
        if item.value > threshold:
            process(item)
```

### 4. Use Appropriate Data Structures

**Choose based on access patterns:**

```ape
# For lookups: Use Map instead of List
# BAD: O(n) lookup
task find_user_by_id_slow(users: List<User>, id: Integer):
    for user in users:
        if user.id == id:
            return user
    return null

# GOOD: O(1) lookup
task find_user_by_id_fast(users: Map<Integer, User>, id: Integer):
    return users.get(id, null)
```

### 5. Avoid Premature Optimization

**Profile first, optimize second:**

```python
# 1. Write clear code first
def process_data(data):
    return [transform(item) for item in data]

# 2. Profile to find bottlenecks
profile = runner.profile("process_data", lambda: process_data(large_dataset))

# 3. Optimize only the bottlenecks
# (Based on profile results)
```

## Compiler Optimization

### Enable Optimizations

```python
from ape.compiler.pipeline import CompilationPipeline

# Use optimization level 2 (default)
pipeline = CompilationPipeline(optimization_level=2)

# For maximum performance
pipeline = CompilationPipeline(optimization_level=3)
```

### Optimization Impact

| Level | Optimizations | Compile Time | Performance |
|-------|--------------|--------------|-------------|
| 0 | None | Fastest | Baseline |
| 1 | Basic | Fast | +5-10% |
| 2 | Standard | Medium | +15-25% |
| 3 | Aggressive | Slow | +20-35% |

## Memory Optimization

### 1. Avoid Unnecessary Copies

```ape
# BAD: Creating copies
task process_list(items: List<Integer>):
    copy = items  # Creates copy
    for item in copy:
        process(item)

# GOOD: Use original
task process_list_optimized(items: List<Integer>):
    for item in items:
        process(item)
```

### 2. Release Resources Early

```ape
task process_large_file(filename: String):
    data = read_file(filename)
    
    # Process early parts
    result = process_header(data[0:1000])
    
    # Release large data structure when done
    data = null
    
    return result
```

## Best Practices

### 1. Benchmark Real Workloads

```python
# Don't just benchmark toy examples
# BAD
runner.benchmark("add", lambda: 2 + 2)

# GOOD
runner.benchmark("typical_workflow", lambda: process_typical_request())
```

### 2. Run Multiple Iterations

```python
# Use enough iterations for statistical significance
result = runner.benchmark(
    "operation",
    func=lambda: operation(),
    iterations=1000  # Not just 10
)
```

### 3. Warmup Before Timing

```python
# BenchmarkRunner does this automatically
runner = BenchmarkRunner(warmup_iterations=5)
```

### 4. Profile in Production-Like Conditions

```python
# Use realistic data sizes
large_dataset = generate_realistic_data(size=10000)
runner.benchmark("production", lambda: process(large_dataset))
```

### 5. Document Performance Requirements

```ape
"""
Performance requirements:
- Process 1000 items in < 100ms
- Memory usage < 50MB
- Startup time < 1s
"""
task batch_processor(items: List<Item>):
    # Implementation
    pass
```

## Common Performance Pitfalls

### 1. String Concatenation in Loops

```ape
# BAD: O(n²) behavior
task build_string(items: List<String>):
    result = ""
    for item in items:
        result = result + item + ", "
    return result

# GOOD: Use list and join
task build_string_optimized(items: List<String>):
    parts = []
    for item in items:
        parts.append(item)
    return join(parts, ", ")
```

### 2. Unnecessary Sorting

```ape
# BAD: Sorting when not needed
task find_max(items: List<Integer>):
    sorted_items = sort(items)
    return sorted_items[sorted_items.length() - 1]

# GOOD: Single pass
task find_max_optimized(items: List<Integer>):
    max_value = items[0]
    for item in items:
        if item > max_value:
            max_value = item
    return max_value
```

### 3. Redundant Computations

```ape
# BAD: Recalculating
task area_of_circles(radii: List<Number>):
    areas = []
    for r in radii:
        areas.append(3.14159 * r * r)  # PI recalculated
    return areas

# GOOD: Use constant
import math_ext

task area_of_circles_optimized(radii: List<Number>):
    areas = []
    for r in radii:
        areas.append(math_ext.PI * r * r)
    return areas
```

## Performance Testing

### Create Performance Tests

```python
# tests/performance/test_benchmark.py
import pytest
from ape.benchmarks import BenchmarkRunner

def test_performance_requirement():
    runner = BenchmarkRunner()
    
    result = runner.benchmark(
        "critical_operation",
        func=lambda: critical_operation(),
        iterations=100
    )
    
    # Assert performance requirement
    assert result.avg_time < 0.01, f"Too slow: {result.avg_time}s"
```

### Continuous Performance Monitoring

```python
# Track performance over time
results_history = []

for version in versions:
    result = benchmark_version(version)
    results_history.append((version, result.avg_time))

# Detect regressions
if results_history[-1][1] > results_history[-2][1] * 1.1:
    print("WARNING: 10% performance regression detected")
```

## Future Enhancements

Planned performance features:
- Adaptive optimization based on runtime profiling
- Native code generation for hot paths
- Parallel execution for independent tasks
- Hardware-specific optimizations

---

**Note:** This feature is scaffolded in v1.0.0. Full implementation is planned for a future release.
