⚡️ Speed up function areIntersecting by 40%#35
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codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
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⚡️ Speed up function areIntersecting by 40%#35codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
areIntersecting by 40%#35codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
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This optimization achieves a **40% runtime improvement** (from 41.8μs to 29.8μs) by eliminating unnecessary intermediate variable assignments in a rectangle intersection check. **What Changed:** The optimized version removes all 8 local variable declarations (`l1`, `r1r`, `t1`, `b1`, `l2`, `r2r`, `t2`, `b2`) and directly accesses the rectangle properties in the return statement's comparison expression. **Why It's Faster:** 1. **Reduced memory operations**: The original code performs 8 variable assignments (storing each property to a local variable), then reads those variables for comparison. The optimized version accesses each property directly once, eliminating 8 write operations and the associated memory allocation overhead. 2. **Lower interpreter overhead**: Each statement in JavaScript/TypeScript has execution overhead (parsing, interpreting, memory management). By reducing from 9 statements to 1, the interpreter has less work to do per function call. 3. **Improved CPU cache efficiency**: With fewer memory locations involved, the processor's cache remains more efficient. Modern JavaScript engines can also better optimize a single-expression function through JIT compilation. **Performance Profile:** The line profiler shows the original code spent ~28ms across 8 assignment statements (lines 9-17), which is completely eliminated in the optimized version. The comparison logic itself (line 19) takes negligible time in both versions, confirming that the bottleneck was the variable creation overhead. **Test Results:** The optimization shows particularly strong gains in: - Edge cases with special values (NaN: 77% faster, Infinity: 94% faster, negative coordinates: 116% faster) - Performance tests with repeated calls (up to 112% faster in batch operations) - Most basic functionality tests (45-90% faster) A few tests show minor slowdowns (1-13%), likely due to measurement variance in sub-microsecond timing, but the overall improvement is consistent and significant across the test suite. This optimization is ideal for high-frequency collision detection, UI layout calculations, or any scenario involving repeated rectangle intersection checks.
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📄 40% (0.40x) speedup for
areIntersectinginapp/client/src/utils/boxHelpers.ts⏱️ Runtime :
41.8 microseconds→29.8 microseconds(best of250runs)📝 Explanation and details
This optimization achieves a 40% runtime improvement (from 41.8μs to 29.8μs) by eliminating unnecessary intermediate variable assignments in a rectangle intersection check.
What Changed:
The optimized version removes all 8 local variable declarations (
l1,r1r,t1,b1,l2,r2r,t2,b2) and directly accesses the rectangle properties in the return statement's comparison expression.Why It's Faster:
Reduced memory operations: The original code performs 8 variable assignments (storing each property to a local variable), then reads those variables for comparison. The optimized version accesses each property directly once, eliminating 8 write operations and the associated memory allocation overhead.
Lower interpreter overhead: Each statement in JavaScript/TypeScript has execution overhead (parsing, interpreting, memory management). By reducing from 9 statements to 1, the interpreter has less work to do per function call.
Improved CPU cache efficiency: With fewer memory locations involved, the processor's cache remains more efficient. Modern JavaScript engines can also better optimize a single-expression function through JIT compilation.
Performance Profile:
The line profiler shows the original code spent ~28ms across 8 assignment statements (lines 9-17), which is completely eliminated in the optimized version. The comparison logic itself (line 19) takes negligible time in both versions, confirming that the bottleneck was the variable creation overhead.
Test Results:
The optimization shows particularly strong gains in:
A few tests show minor slowdowns (1-13%), likely due to measurement variance in sub-microsecond timing, but the overall improvement is consistent and significant across the test suite.
This optimization is ideal for high-frequency collision detection, UI layout calculations, or any scenario involving repeated rectangle intersection checks.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
📊 Performance Profile
View detailed line-by-line performance analysis
To edit these changes
git checkout codeflash/optimize-areIntersecting-ml25ggtsand push.