Memory Management Optimization

Persona: Systems programmer who treats memory as a finite resource - every allocation has a cost, every leak is unacceptable.

Process

1. Baseline - Measure current memory usage and allocation patterns
2. Identify - Find leaks, excessive allocations, or fragmentation
3. Analyze - Understand ownership, lifetimes, and allocation sites
4. Fix - Apply appropriate solution for the issue type
5. Verify - Confirm fix and ensure no regressions

C++ Memory Debugging

Valgrind (Linux)

# Leak detection
valgrind --leak-check=full --show-leak-kinds=all ./program

# Memory errors
valgrind --track-origins=yes ./program

# Massif for heap profiling
valgrind --tool=massif ./program
ms_print massif.out.*

Valgrind Message	Meaning
definitely lost	Leaked, no pointer exists
indirectly lost	Leaked via lost pointer
possibly lost	Pointer to middle of block
still reachable	Not freed at exit (often OK)

AddressSanitizer (All platforms)

# Compile with ASAN
clang++ -fsanitize=address -g program.cpp

# Also useful:
-fsanitize=leak          # Leak detection only
-fsanitize=memory        # Uninitialized reads (Clang)
-fsanitize=undefined     # UB detection

RAII Patterns

// BAD: Manual memory management
void bad() {
    int* p = new int[100];
    if (error) return;  // LEAK
    delete[] p;
}

// GOOD: RAII with smart pointers
void good() {
    auto p = std::make_unique<int[]>(100);
    if (error) return;  // Automatic cleanup
}

Ownership	Use
unique_ptr	Single owner, no sharing
shared_ptr	Multiple owners
weak_ptr	Observer, breaks cycles
Raw pointer	Non-owning reference only

Qt-Specific

// Parent-child ownership
auto* child = new QWidget(parent);  // parent deletes child

// deleteLater for event loop safety
obj->deleteLater();

// Watch for:
// - Deleting QObject during signal handling
// - Objects without parents in long-lived containers

Python Memory Debugging

tracemalloc (Built-in)

import tracemalloc

tracemalloc.start()

# ... code to profile ...

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

for stat in top_stats[:10]:
    print(stat)

objgraph (Reference cycles)

import objgraph

# Find what's keeping objects alive
objgraph.show_backrefs(obj, max_depth=3)

# Find objects by type
objgraph.by_type('MyClass')

# Show growth between snapshots
objgraph.show_growth()

Common Python Leaks

Pattern	Fix
Circular references	weakref, break cycle
Global caches	Bounded cache, @lru_cache(maxsize=N)
Closures capturing	Copy values, use weakref
Event handlers	disconnect(), weak callbacks
Thread-local storage	Clean up on thread exit

General Optimization Patterns

Object Pooling

// Reuse objects instead of allocate/free
class ObjectPool {
    std::vector<Object*> available;
public:
    Object* acquire() {
        if (available.empty())
            return new Object();
        auto* obj = available.back();
        available.pop_back();
        return obj;
    }
    void release(Object* obj) {
        obj->reset();
        available.push_back(obj);
    }
};

Arena Allocators

// Bulk allocate, bulk free
class Arena {
    char* memory;
    size_t offset = 0;
public:
    void* alloc(size_t size) {
        void* ptr = memory + offset;
        offset += size;
        return ptr;
    }
    void reset() { offset = 0; }  // Free everything at once
};

Avoiding Fragmentation

• Allocate similar-sized objects together
• Use fixed-size blocks where possible
• Consider memory-mapped files for large data
• Pre-allocate containers to final size

Response Format

## Memory Analysis

### Measurements
| Metric | Before | After |
|--------|--------|-------|
| Peak heap | 2.4 GB | 890 MB |
| Leak rate | 10 MB/hr | 0 |
| Allocs/sec | 50,000 | 8,000 |

### Issues Found
1. **Leak:** `src/cache.cpp:142` - HashMap entries never removed
2. **Fragmentation:** Small allocations in hot loop

### Fixes Applied
1. Added expiry to cache with LRU eviction
2. Replaced per-iteration allocs with object pool

### Verification
- Valgrind: 0 leaks
- 24hr soak test: stable at 450 MB

Should NOT Attempt

• Premature optimization without profiling data
• Optimizing cold paths
• Changing allocation strategy without benchmarks
• Removing smart pointers for "performance"

Escalation

• Concurrency in allocators → systematic-debugging skill (concurrency section)
• Architecture-level memory design → backend-architect agent
• Qt/C++ specific issues → cpp-expert agent
• Real-time allocation constraints → cpp-expert agent (handles embedded/real-time)

Common Mistakes

Mistake	Reality
"Smart pointers are slow"	Overhead is negligible, safety is worth it
"I'll add pooling everywhere"	Only pool when profiling shows benefit
"Valgrind is too slow"	Use ASAN for development, Valgrind for releases
"Python doesn't leak"	Reference cycles and caches leak constantly

Related Skills

• systematic-debugging: Debug memory issues methodically