yaze/docs/analysis/dungeon_parallel_optimization_summary.md

DungeonEditor Parallel Optimization Implementation

🚀 Parallelization Strategy Implemented

Problem Identified

• DungeonEditor::LoadAllRooms: 17,966ms (17.97 seconds) - 99.9% of loading time
• Loading 296 rooms sequentially, each involving complex operations
• Perfect candidate for parallelization due to independent room processing

Solution: Multi-Threaded Room Loading

Key Optimizations

1. Parallel Room Processing

   // Load 296 rooms using up to 8 threads
   const int max_concurrency = std::min(8, std::thread::hardware_concurrency());
   const int rooms_per_thread = (296 + max_concurrency - 1) / max_concurrency;
   

2. Thread-Safe Result Collection

   std::mutex results_mutex;
   std::vector<std::pair<int, zelda3::RoomSize>> room_size_results;
   std::vector<std::pair<int, ImVec4>> room_palette_results;
   

3. Optimized Thread Distribution

   • 8 threads maximum (reasonable limit for room loading)
   • ~37 rooms per thread (296 ÷ 8 = 37 rooms per thread)
   • Hardware concurrency aware (adapts to available CPU cores)

Parallel Processing Flow

// Each thread processes a batch of rooms
for (int i = start_room; i < end_room; ++i) {
  // 1. Load room data (expensive operation)
  rooms[i] = zelda3::LoadRoomFromRom(rom_, i);
  
  // 2. Calculate room size
  auto room_size = zelda3::CalculateRoomSize(rom_, i);
  
  // 3. Load room objects
  rooms[i].LoadObjects();
  
  // 4. Process palette (thread-safe collection)
  // ... palette processing ...
}

Thread Safety Features

1. Mutex Protection: std::mutex results_mutex protects shared data structures
2. Lock Guards: std::lock_guard<std::mutex> ensures thread-safe result collection
3. Independent Processing: Each thread works on different room ranges
4. Synchronized Results: Results collected and sorted on main thread

Expected Performance Impact

Theoretical Speedup

• 8x faster with 8 threads (ideal case)
• Realistic expectation: 4-6x speedup due to:
  • Thread creation overhead
  • Mutex contention
  • Memory bandwidth limitations
  • Cache coherency issues

Expected Results

• Before: 17,966ms (17.97 seconds)
• After: 2,000-4,500ms (2-4.5 seconds)
• Total Loading Time: 2.5-5 seconds (down from 18.6 seconds)
• Overall Improvement: 70-85% reduction in loading time

Technical Implementation Details

Thread Management

std::vector<std::future<absl::Status>> futures;

for (int thread_id = 0; thread_id < max_concurrency; ++thread_id) {
  auto task = [this, &rooms, thread_id, rooms_per_thread, ...]() -> absl::Status {
    // Process room batch
    return absl::OkStatus();
  };
  
  futures.emplace_back(std::async(std::launch::async, task));
}

// Wait for all threads to complete
for (auto& future : futures) {
  RETURN_IF_ERROR(future.get());
}

Result Processing

// Sort results by room ID for consistent ordering
std::sort(room_size_results.begin(), room_size_results.end(), 
          [](const auto& a, const auto& b) { return a.first < b.first; });

// Process collected results on main thread
for (const auto& [room_id, room_size] : room_size_results) {
  room_size_pointers_.push_back(room_size.room_size_pointer);
  // ... process results ...
}

Monitoring and Validation

Performance Timing Added

• DungeonRoomLoader::PostProcessResults: Measures result processing time
• Thread creation overhead: Minimal compared to room loading time
• Result collection time: Expected to be <100ms

Logging and Debugging

util::logf("Loading %d dungeon rooms using %d threads (%d rooms per thread)", 
           kTotalRooms, max_concurrency, rooms_per_thread);

Benefits of This Approach

1. Massive Performance Gain: 70-85% reduction in loading time
2. Scalable: Automatically adapts to available CPU cores
3. Thread-Safe: Proper synchronization prevents data corruption
4. Maintainable: Clean separation of parallel processing and result collection
5. Robust: Error handling per thread with proper status propagation

Next Steps

1. Test Performance: Run application and measure actual speedup
2. Validate Results: Ensure room data integrity is maintained
3. Fine-tune: Adjust thread count if needed based on results
4. Monitor: Watch for any threading issues or performance regressions

This parallel optimization should transform YAZE from a slow-loading application to a lightning-fast ROM editor!