C++ may be slower than Java in recursion

While implementing a dynamic programming solution in C++, I used a recursive function combined with an unordered_map to store intermediate results. Surprisingly, I found that in some cases, the C++ implementation ran slower than the Java version using ConcurrentSkipListMap—especially when the hash keys were composed of multiple floating-point numbers.

After consulting a few AI tools, Grok provided the most insightful explanation, pointing out several potential causes:

• Hashing overhead: When keys are combinations of floating-point numbers, computing their hashes can be computationally expensive. In recursive functions, this overhead accumulates quickly, especially when the key distribution is uneven.

• Hash collision and performance degradation: Tiny differences in floating-point values or clustered key distributions may lead to frequent hash collisions. This can degrade the average time complexity of operations (insert, lookup, delete) from $O(1)$ to $O(n)$.

• Memory management: In C++, unordered_map may trigger costly memory reallocations due to rehashing during recursive calls—more so than the dynamic adjustment overhead of a skip list like ConcurrentSkipListMap.

• Lack of concurrency: If recursion involves multithreading, unordered_map requires manual locking, whereas ConcurrentSkipListMap is designed for concurrency with a lock-free architecture, making it more efficient.

Among these, I believe the second point is the most likely cause. Specifically, for C++’s unordered_map, several floating-point-specific issues may contribute:

• Floating-point precision: The binary representation of floating-point numbers can introduce minuscule differences (e.g., 1.0 vs 1.0 + ε), requiring normalization or truncation. Without this, hash collisions become more frequent.

• Hash function quality: Default or naive custom hash functions often struggle with clustered floating-point values, resulting in uneven distributions and degraded performance to $O(n)$.

• Computational cost: Repeated computation of complex hashes for combinations of floating-point numbers within recursion adds significant CPU overhead.

In contrast, Java’s ConcurrentSkipListMap has structural advantages:

• No hashing required: Skip lists rely on comparisons instead of hashing, avoiding both collision-related slowdowns and the cost of hash computation.

• Straightforward floating-point comparison: While precision issues still exist, value comparisons are more intuitive, and the skip list’s $O(\log n)$ performance remains stable regardless of key distribution.