⚡️ Speed up method Algorithms.fibonacci by 108%

[codeflash-ai]

📄 108% (1.08x) speedup for Algorithms.fibonacci in code_to_optimize/java/src/main/java/com/example/Algorithms.java

⏱️ Runtime : 11.6 milliseconds → 5.56 milliseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 107% speedup (reducing runtime from 11.6ms to 5.56ms) through three key algorithmic improvements to the Fibonacci fast-doubling implementation:

Primary Optimization - Loop Control Refactoring:
The original code uses Integer.highestOneBit(n) to create a mask and repeatedly right-shifts it (mask >>>= 1) each iteration, checking (n & mask) == 0 to determine the bit value. The optimized version instead:

1. Calculates the bit position once upfront using 31 - Integer.numberOfLeadingZeros(n)
2. Iterates down from this highest bit to 0 using a simple integer counter
3. Extracts each bit with (n >>> i) & 1

This eliminates the per-iteration mask manipulation overhead and reduces the loop's branching complexity. Instead of maintaining and shifting a mask variable, we now have a straightforward countdown loop with direct bit extraction.

Secondary Optimization - Bit Shift for Doubling:
Replacing b + b with b << 1 (left-shift by 1) converts the addition into a single bit-shift operation. While modern JIT compilers often optimize addition-based doubling, the explicit shift operation can be faster in tight loops and signals the intent more clearly to the compiler's optimization passes.

Why This Matters:
The Fibonacci fast-doubling algorithm is O(log n), meaning it runs proportional to the number of bits in n. For large Fibonacci numbers (e.g., n=1,000,000 has ~20 bits), these per-iteration micro-optimizations compound significantly. By reducing each iteration's overhead through simpler loop control and more efficient arithmetic operations, the optimized version cuts the total runtime roughly in half.

The refactored loop structure is also more CPU-friendly: predictable countdown loops with simple integer arithmetic tend to pipeline better than mask-based loops with variable bit positions, leading to improved instruction-level parallelism in the processor.

✅ 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-mlbft71d and push.