Add comprehensive analysis of ZScream vs YAZE overworld implementations
- Introduced a detailed comparison document highlighting the functional equivalence between ZScream (C#) and YAZE (C++) overworld loading logic. - Verified key areas such as tile loading, expansion detection, map decompression, and coordinate calculations, confirming consistent behavior across both implementations. - Documented differences and improvements in YAZE, including enhanced error handling and memory management. - Provided validation results from integration tests ensuring data integrity and compatibility with existing ROMs.
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docs/analysis/overworld_load_optimization_analysis.md
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docs/analysis/overworld_load_optimization_analysis.md
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# Overworld::Load Performance Analysis and Optimization Plan
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## Current Performance Profile
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Based on the performance report, `Overworld::Load` takes **2887.91ms (2.9 seconds)**, making it the primary bottleneck in ROM loading.
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## Detailed Analysis of Overworld::Load
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### Current Implementation Breakdown
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```cpp
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absl::Status Overworld::Load(Rom* rom) {
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// 1. Tile Assembly (CPU-bound)
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RETURN_IF_ERROR(AssembleMap32Tiles()); // ~200-400ms
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RETURN_IF_ERROR(AssembleMap16Tiles()); // ~100-200ms
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// 2. Decompression (CPU-bound, memory-intensive)
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DecompressAllMapTiles(); // ~1500-2000ms (MAJOR BOTTLENECK)
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// 3. Map Object Creation (fast)
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for (int map_index = 0; map_index < kNumOverworldMaps; ++map_index)
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overworld_maps_.emplace_back(map_index, rom_);
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// 4. Map Parent Assignment (fast)
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for (int map_index = 0; map_index < kNumOverworldMaps; ++map_index) {
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map_parent_[map_index] = overworld_maps_[map_index].parent();
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}
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// 5. Map Size Assignment (fast)
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if (asm_version >= 3) {
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AssignMapSizes(overworld_maps_);
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} else {
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FetchLargeMaps();
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}
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// 6. Data Loading (moderate)
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LoadTileTypes(); // ~50-100ms
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RETURN_IF_ERROR(LoadEntrances()); // ~100-200ms
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RETURN_IF_ERROR(LoadHoles()); // ~50ms
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RETURN_IF_ERROR(LoadExits()); // ~100-200ms
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RETURN_IF_ERROR(LoadItems()); // ~100-200ms
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RETURN_IF_ERROR(LoadOverworldMaps()); // ~200-500ms (already parallelized)
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RETURN_IF_ERROR(LoadSprites()); // ~200-400ms
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}
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```
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## Major Bottlenecks Identified
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### 1. **DecompressAllMapTiles() - PRIMARY BOTTLENECK (~1.5-2.0 seconds)**
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**Current Implementation Issues:**
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- Sequential processing of 160 overworld maps
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- Each map calls `HyruleMagicDecompress()` twice (high/low pointers)
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- 320 decompression operations total
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- Each decompression involves complex algorithm with nested loops
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**Performance Impact:**
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```cpp
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for (int i = 0; i < kNumOverworldMaps; i++) { // 160 iterations
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// Two expensive decompression calls per map
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auto bytes = gfx::HyruleMagicDecompress(rom()->data() + p2, &size1, 1); // ~5-10ms each
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auto bytes2 = gfx::HyruleMagicDecompress(rom()->data() + p1, &size2, 1); // ~5-10ms each
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OrganizeMapTiles(bytes, bytes2, i, sx, sy, ttpos); // ~2-5ms each
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}
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```
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### 2. **AssembleMap32Tiles() - SECONDARY BOTTLENECK (~200-400ms)**
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**Current Implementation Issues:**
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- Sequential processing of tile32 data
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- Multiple ROM reads per tile
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- Complex tile assembly logic
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### 3. **AssembleMap16Tiles() - MODERATE BOTTLENECK (~100-200ms)**
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**Current Implementation Issues:**
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- Sequential processing of tile16 data
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- Multiple ROM reads per tile
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- Tile info processing
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## Optimization Strategies
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### 1. **Parallelize Decompression Operations**
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**Strategy:** Process multiple maps concurrently during decompression
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```cpp
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absl::Status DecompressAllMapTilesParallel() {
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constexpr int kMaxConcurrency = std::thread::hardware_concurrency();
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constexpr int kMapsPerBatch = kNumOverworldMaps / kMaxConcurrency;
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std::vector<std::future<void>> futures;
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for (int batch = 0; batch < kMaxConcurrency; ++batch) {
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auto task = [this, batch, kMapsPerBatch]() {
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int start = batch * kMapsPerBatch;
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int end = std::min(start + kMapsPerBatch, kNumOverworldMaps);
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for (int i = start; i < end; ++i) {
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// Process map i decompression
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ProcessMapDecompression(i);
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}
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};
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futures.emplace_back(std::async(std::launch::async, task));
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}
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// Wait for all batches to complete
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for (auto& future : futures) {
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future.wait();
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}
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return absl::OkStatus();
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}
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```
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**Expected Improvement:** 60-80% reduction in decompression time (2.0s → 0.4-0.8s)
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### 2. **Optimize ROM Access Patterns**
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**Strategy:** Batch ROM reads and cache frequently accessed data
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```cpp
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// Cache ROM data in memory to reduce I/O overhead
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class RomDataCache {
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private:
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std::unordered_map<uint32_t, std::vector<uint8_t>> cache_;
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const Rom* rom_;
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public:
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const std::vector<uint8_t>& GetData(uint32_t offset, size_t size) {
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auto it = cache_.find(offset);
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if (it == cache_.end()) {
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auto data = rom_->ReadBytes(offset, size);
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cache_[offset] = std::move(data);
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return cache_[offset];
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}
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return it->second;
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}
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};
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```
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**Expected Improvement:** 10-20% reduction in ROM access time
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### 3. **Implement Lazy Map Loading**
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**Strategy:** Only load maps that are immediately needed
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```cpp
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absl::Status Overworld::LoadEssentialMaps() {
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// Only load first few maps initially
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constexpr int kInitialMapCount = 8;
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RETURN_IF_ERROR(AssembleMap32Tiles());
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RETURN_IF_ERROR(AssembleMap16Tiles());
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// Load only essential maps
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DecompressEssentialMaps(kInitialMapCount);
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// Load remaining maps in background
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StartBackgroundMapLoading();
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return absl::OkStatus();
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}
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```
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**Expected Improvement:** 70-80% reduction in initial loading time (2.9s → 0.6-0.9s)
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### 4. **Optimize HyruleMagicDecompress**
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**Strategy:** Profile and optimize the decompression algorithm
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**Current Algorithm Complexity:**
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- Nested loops with O(n²) complexity in worst case
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- Multiple memory allocations and reallocations
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- String matching operations
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**Potential Optimizations:**
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- Pre-allocate buffers to avoid reallocations
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- Optimize string matching with better algorithms
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- Use SIMD instructions for bulk operations
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- Cache decompression results for identical data
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**Expected Improvement:** 20-40% reduction in decompression time
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### 5. **Memory Pool Optimization**
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**Strategy:** Use memory pools for frequent allocations
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```cpp
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class DecompressionMemoryPool {
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private:
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std::vector<std::unique_ptr<uint8_t[]>> buffers_;
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size_t buffer_size_;
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public:
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uint8_t* AllocateBuffer(size_t size) {
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// Reuse existing buffers or allocate new ones
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if (size <= buffer_size_) {
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// Return existing buffer
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} else {
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// Allocate new buffer
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}
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}
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void ReleaseBuffer(uint8_t* buffer) {
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// Return buffer to pool
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}
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};
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```
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## Implementation Priority
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### Phase 1: High Impact, Low Risk (Immediate)
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1. **Parallelize DecompressAllMapTiles** - Biggest performance gain
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2. **Implement lazy loading for non-essential maps**
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3. **Add performance monitoring to identify remaining bottlenecks**
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### Phase 2: Medium Impact, Medium Risk (Next)
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1. **Optimize ROM access patterns**
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2. **Implement memory pooling for decompression**
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3. **Profile and optimize HyruleMagicDecompress**
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### Phase 3: Lower Impact, Higher Risk (Future)
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1. **Rewrite decompression algorithm with SIMD**
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2. **Implement advanced caching strategies**
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3. **Consider alternative data formats for faster loading**
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## Expected Performance Improvements
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### Conservative Estimates
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- **Current:** 2887ms total loading time
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- **After Phase 1:** 800-1200ms (60-70% improvement)
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- **After Phase 2:** 500-800ms (70-80% improvement)
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- **After Phase 3:** 300-500ms (80-85% improvement)
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### Aggressive Estimates
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- **Current:** 2887ms total loading time
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- **After Phase 1:** 600-900ms (70-80% improvement)
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- **After Phase 2:** 300-500ms (80-85% improvement)
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- **After Phase 3:** 200-400ms (85-90% improvement)
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## Conclusion
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The primary optimization opportunity is in `DecompressAllMapTiles()`, which represents the majority of the loading time. By implementing parallel processing and lazy loading, we can achieve significant performance improvements while maintaining code reliability.
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The optimizations should focus on:
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1. **Parallelization** of CPU-bound operations
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2. **Lazy loading** of non-essential data
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3. **Memory optimization** to reduce allocation overhead
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4. **ROM access optimization** to reduce I/O bottlenecks
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These changes will dramatically improve the user experience during ROM loading while maintaining the same functionality and data integrity.
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