⚡️ Speed up method Algorithms.fibonacci by 42%

[codeflash-ai]

📄 42% (0.42x) speedup for Algorithms.fibonacci in code_to_optimize/java/src/main/java/com/example/Algorithms.java

⏱️ Runtime : 7.17 milliseconds → 5.04 milliseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 42% runtime improvement (from 7.17ms to 5.04ms) by eliminating redundant bit shift operations in the fast-doubling loop.

Key Optimization:

The original loop performs two bit operations per iteration:

1. (n >> i) - shifts n right by i bits
2. & 1 - masks the least significant bit

Since i decrements from highestBit to 0, this effectively scans bits from most significant to least significant, but does so by repeatedly shifting the entire value n.

The optimized version precomputes a bit mask (bit = 1 << highestBit) and shifts the mask itself down each iteration (bit >>= 1). The check becomes (n & bit) == 0, which tests whether a specific bit in n is set without shifting n at all.

Why This Is Faster:

1. Fewer shift operations: The original code shifts n by varying amounts on every loop iteration (O(log n) shifts of potentially large values). The optimized code performs a single initial shift to create the mask, then shifts only the small mask value once per iteration.

2. Simpler bit extraction: Masking against a precomputed bit position (n & bit) is slightly more efficient than the shift-then-mask pattern ((n >> i) & 1), as it avoids the variable-shift operation entirely.

3. Reduced instruction count: Each iteration saves one variable-shift operation and replaces it with a fixed right-shift of a small constant, which modern CPUs can execute more efficiently.

For the fast-doubling algorithm (used when n ≥ 50), this optimization reduces the per-iteration overhead in what is already a logarithmic-time algorithm. The 42% speedup demonstrates that even small micro-optimizations in tight loops can compound significantly when computing large Fibonacci numbers where the loop runs log₂(n) times.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 12 Passed
🌀 Generated Regression Tests	🔘 None Found
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	No coverage data found for fibonacci

⚙️ Click to see Existing Unit Tests

To edit these changes git checkout codeflash/optimize-Algorithms.fibonacci-mlbdfzsn and push.