⚡️ Speed up method Algorithms.fibonacci by 151%

[codeflash-ai]

📄 151% (1.51x) speedup for Algorithms.fibonacci in code_to_optimize/java/src/main/java/com/example/Algorithms.java

⏱️ Runtime : 13.4 milliseconds → 5.35 milliseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 151% speedup (13.4ms → 5.35ms) through three key micro-optimizations in the tight loop of the fast doubling Fibonacci algorithm:

1. Mask-based iteration eliminates variable shifts:
The original code uses for (int i = highestBit; i >= 0; i--) with (n >>> i) performing a variable-width right shift on every iteration. The optimized version replaces this with a pre-computed mask (mask = 1 << highestBit) that shifts right by a fixed amount (mask >>= 1). This change:

• Eliminates the variable i and its decrement operation
• Replaces expensive variable-width shifts (n >>> i) with a simpler fixed-width shift of the mask
• Changes the bit test from (n >>> i) & 1 to n & mask, reducing operations from shift+AND to just AND

2. Addition replaces left shift:
Changing b << 1 to b + b is slightly faster on many processors because addition can execute in parallel with other ALU operations, whereas shifts may compete for the same execution units. While seemingly trivial, in a tight loop executing many iterations for large Fibonacci numbers, this compounds into measurable savings.

3. Eliminates repeated bit indexing:
The original code computes (n >>> i) & 1 on each iteration, requiring both a shift of n and a mask operation. The optimized version tests n & mask once per iteration, where the mask is pre-positioned, eliminating the need to shift n repeatedly.

Why this matters:
For large Fibonacci indices, the loop executes O(log n) times (up to 31 iterations for 32-bit integers). These micro-optimizations—removing variable shifts, simplifying arithmetic, and reducing per-iteration operations—compound across iterations. The 2.5x runtime improvement demonstrates that even in algorithmically optimal O(log n) code, careful attention to low-level operations in hot loops can yield substantial performance gains.

The optimizations preserve exact mathematical behavior, overflow semantics, and API compatibility while purely improving execution efficiency.

✅ 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-mlbg2v4t and push.