Fibonacci hashing offers a faster alternative to the typical modulo operator (%) for distributing items into hash tables, especially when the table size is a power of two. It leverages the golden ratio's properties by multiplying the hash key by a large constant derived from the golden ratio and then bit-shifting the result, effectively achieving a modulo operation without the expensive division. This method produces a more even distribution compared to modulo with prime table sizes, particularly when dealing with keys exhibiting sequential patterns, thus reducing collisions and improving performance. While theoretically superior, its benefits may be negligible in modern systems due to compiler optimizations and branch prediction for modulo with powers of two.
The blog post showcases efficient implementations of hash tables and dynamic arrays in C, prioritizing speed and simplicity over features. The hash table uses open addressing with linear probing and a power-of-two size, offering fast lookups and insertions. Resizing is handled by allocating a larger table and rehashing all elements, a process triggered when the table reaches a certain load factor. The dynamic array, built atop realloc, doubles in capacity when full, ensuring amortized constant-time appends while minimizing wasted space. Both examples emphasize practical performance over complex optimizations, providing clear and concise code suitable for embedding in performance-sensitive applications.
Hacker News users discuss the practicality and efficiency of Chris Wellons' C implementations of hash tables and dynamic arrays. Several commenters praise the clear and concise code, finding it a valuable learning resource. Some debate the choice of open addressing over separate chaining for the hash table, with proponents of open addressing citing better cache locality and less memory overhead. Others highlight the importance of proper hash functions and the potential performance degradation with high load factors in open addressing. A few users suggest alternative approaches, such as using C++ containers or optimizing for specific use cases, while acknowledging the educational value of Wellons' straightforward C examples. The discussion also touches on the trade-offs of manual memory management and the challenges of achieving both simplicity and performance.
Summary of Comments ( 10 )
https://news.ycombinator.com/item?id=43677122
HN commenters generally praise the article for clearly explaining Fibonacci hashing and its benefits over modulo. Some point out that the technique is not forgotten, being used in game development and hash table implementations within popular languages like Java. A few commenters discuss the nuances of the golden ratio's properties and its suitability for hashing, with one noting the importance of good hash functions over minor speed differences in the hashing algorithm itself. Others shared alternative hashing methods like "Multiply-with-carry" and "SplitMix64", along with links to resources on hash table performance testing. A recurring theme is that Fibonacci hashing shines with power-of-two table sizes, losing its advantages (and potentially becoming worse) with prime table sizes.
The Hacker News post titled "Fibonacci Hashing: The Optimization That the World Forgot" (https://news.ycombinator.com/item?id=43677122) has a moderate number of comments, generating a discussion around the merits and applicability of Fibonacci hashing.
Several commenters delve into the practicalities of Fibonacci hashing, questioning its supposed superiority over simpler modulo methods. One recurring point is the potential performance impact of multiplication on various architectures. While the article champions multiplication as faster than modulo, some commenters argue that this isn't universally true. Modern CPUs, they point out, often have efficient modulo instructions, especially when dealing with powers of two. One commenter specifically mentions that modulo by a power of two can be as simple as a bitwise AND operation, which is extremely fast. Therefore, the supposed speed advantage of Fibonacci hashing becomes less clear-cut and highly dependent on the specific hardware.
Another key discussion thread centers around the quality of hash distribution. Some commenters express skepticism about Fibonacci hashing consistently outperforming modulo, especially when dealing with real-world data that might not be uniformly distributed. Concerns are raised about potential clustering or patterns in the hashed values that could negatively impact performance. One commenter highlights the importance of benchmarking with realistic datasets to demonstrate any tangible benefits over traditional methods. They also mention Knuth's multiplicative hashing method as a strong contender, suggesting it often provides a good balance between speed and distribution quality.
A few commenters provide valuable context by linking to related resources and discussions. One link points to a Stack Overflow post discussing the choice of the multiplier in multiplicative hashing. Another commenter shares a link to a paper analyzing different hashing methods. These external resources add depth to the conversation and provide alternative perspectives on the topic.
Finally, some commenters offer practical advice and considerations. One commenter suggests that the choice of hashing method should depend on the specific application and its performance requirements. They emphasize the need to profile and measure the impact of different hashing strategies rather than relying on theoretical assumptions. Another commenter points out the potential complexity of implementing Fibonacci hashing correctly, which could outweigh its theoretical benefits in some cases.
In summary, the comments section provides a balanced perspective on Fibonacci hashing, challenging the article's claim of it being a forgotten optimization. The discussion highlights the importance of considering hardware specifics, data distribution, and practical implementation challenges when evaluating any hashing method.