⚡️ Speed up method GlobalGeodetic.TileLatLonBounds by 14%#23
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⚡️ Speed up method GlobalGeodetic.TileLatLonBounds by 14%#23codeflash-ai[bot] wants to merge 1 commit intomasterfrom
GlobalGeodetic.TileLatLonBounds by 14%#23codeflash-ai[bot] wants to merge 1 commit intomasterfrom
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The optimized code achieves a **14% speedup** through several key performance optimizations:
**1. Memory Layout Optimization (`__slots__`)**
- Added `__slots__ = ('tileSize', 'resFact')` to prevent dynamic attribute creation, reducing memory overhead and improving attribute access speed.
**2. Arithmetic Optimizations**
- **Precomputed division**: In `__init__`, calculates `inv_tileSize = 1.0 / self.tileSize` once and uses multiplication (`180.0 * inv_tileSize`) instead of repeated division operations.
- **Bitshift for powers of 2**: For integer zooms (0-30), uses `1 << zoom` instead of `2**zoom`, which is significantly faster since bitshifting is a single CPU operation vs. exponentiation.
- **Reduced redundant calculations**: Caches `tile_factor = self.tileSize * res` to avoid recomputing this value four times per call.
**3. Variable Extraction**
- Extracts intermediate calculations (`tx0`, `ty0`, `tx1`, `ty1`) to separate variables, reducing the complexity of the return statement and potentially improving compiler optimizations.
**Test Case Performance Analysis:**
- The optimizations are most effective for **high-frequency tile generation scenarios** (like the large batch tests showing 23.4% speedup)
- **Integer zoom levels** (most common in mapping applications) benefit most from the bitshift optimization
- **Edge cases with non-integer zooms** still work correctly but use the fallback `2**zoom` calculation
- The optimizations maintain full backward compatibility while providing consistent performance gains across all test scenarios
These micro-optimizations compound effectively because `TileBounds` is typically called thousands of times during tile generation workflows.
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📄 14% (0.14x) speedup for
GlobalGeodetic.TileLatLonBoundsinopendm/tiles/gdal2tiles.py⏱️ Runtime :
314 microseconds→275 microseconds(best of316runs)📝 Explanation and details
The optimized code achieves a 14% speedup through several key performance optimizations:
1. Memory Layout Optimization (
__slots__)__slots__ = ('tileSize', 'resFact')to prevent dynamic attribute creation, reducing memory overhead and improving attribute access speed.2. Arithmetic Optimizations
__init__, calculatesinv_tileSize = 1.0 / self.tileSizeonce and uses multiplication (180.0 * inv_tileSize) instead of repeated division operations.1 << zoominstead of2**zoom, which is significantly faster since bitshifting is a single CPU operation vs. exponentiation.tile_factor = self.tileSize * resto avoid recomputing this value four times per call.3. Variable Extraction
tx0,ty0,tx1,ty1) to separate variables, reducing the complexity of the return statement and potentially improving compiler optimizations.Test Case Performance Analysis:
2**zoomcalculationThese micro-optimizations compound effectively because
TileBoundsis typically called thousands of times during tile generation workflows.✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
To edit these changes
git checkout codeflash/optimize-GlobalGeodetic.TileLatLonBounds-mh508miqand push.