.NET 7's Span<T>.SequenceEqual, when comparing byte spans, outperforms memcmp in many scenarios, particularly with smaller inputs. This surprising result stems from SequenceEqual's optimized implementation that leverages vectorization (SIMD instructions) and other platform-specific enhancements. While memcmp is generally fast, it can be less efficient on certain architectures or with smaller data sizes. Therefore, when working with byte spans in .NET 7 and later, SequenceEqual is often the preferred choice for performance, offering a simpler and potentially faster approach to byte comparison.
Richard Cock's blog post, "Span.SequenceEquals is faster than memcmp," explores a surprising performance discovery in .NET. The author initially sought a faster way to compare byte arrays, assuming the tried-and-true memcmp function from the C standard library would be the most performant option. This assumption stemmed from memcmp's likely optimized implementation at the assembly level, potentially leveraging specialized CPU instructions like SIMD.
Cock's investigation began by benchmarking memcmp against several .NET-based comparison methods. Unexpectedly, the .NET's Span<T>.SequenceEquals method, designed for generic sequence comparison, consistently outperformed memcmp, even when comparing byte arrays. This result was surprising because Span<T>.SequenceEquals, being a generic method, might be expected to carry some overhead compared to a specialized function like memcmp designed solely for byte comparison.
The blog post then delves into the reasons behind this performance disparity. Through detailed profiling and analyzing the generated assembly code, Cock discovered that the RyuJIT compiler, .NET's Just-In-Time compiler, applies significant optimizations to Span<T>.SequenceEquals when used with byte arrays. These optimizations include vectorization using SIMD instructions, effectively processing multiple bytes simultaneously. Furthermore, RyuJIT also eliminates bounds checks within the loop, further reducing overhead. The combined effect of these optimizations allows Span<T>.SequenceEquals to achieve a significant performance advantage over the unoptimized memcmp calls made through P/Invoke.
Specifically, the author discovered that while their P/Invoke call to memcmp was not being inlined by the JIT compiler, the call to SequenceEquals was being inlined and heavily optimized. This inlining avoided the function call overhead and allowed the JIT to leverage the context of the comparison within the calling method, further improving performance.
The post concludes by highlighting the power of .NET's runtime optimizations. The fact that a generic method like Span<T>.SequenceEquals can outperform a specialized C function speaks to the effectiveness of RyuJIT's optimizations. It encourages developers to consider and explore .NET's built-in functionalities before resorting to external libraries or P/Invoke, as the runtime can often provide surprisingly efficient implementations. The author further suggests that this performance difference underscores the importance of profiling and benchmarking to identify unexpected performance bottlenecks and discover optimal solutions within the .NET ecosystem.
Summary of Comments ( 27 )
https://news.ycombinator.com/item?id=43524665
Hacker News users discuss the surprising performance advantage of
Span<T>.SequenceEqualsovermemcmpfor comparing byte arrays, especially when dealing with shorter arrays. Several commenters speculate that the JIT compiler is able to optimizeSequenceEqualsmore effectively, potentially by eliminating bounds checks or leveraging SIMD instructions. The overhead of callingmemcmp, a native function, is also mentioned as a possible factor. Some skepticism is expressed, with users questioning the benchmarking methodology and suggesting that the results might not generalize to all scenarios. One commenter suggests using a platform intrinsic instead ofmemcmpwhen the length is not known at compile time. Another commenter highlights the benefits of writing clear code and letting the JIT compiler handle optimization.The Hacker News post "Span.SequenceEquals is faster than memcmp" sparked a discussion with several insightful comments. Many commenters focused on the nuances of performance comparisons and the specific scenarios where
SequenceEqualsmight outperformmemcmp.One commenter pointed out the importance of considering data alignment when comparing these methods. They highlighted that
memcmpbenefits significantly from aligned data, whileSequenceEqualsmight not experience the same advantage. This difference in behavior, they argued, could explain some of the performance discrepancies observed in the original article. The commenter went on to speculate that the benchmark might have involved unaligned data, favoringSequenceEquals. They suggested repeating the benchmark with aligned data for a fairer comparison.Another commenter delved into the implementation details of
SequenceEquals. They explained how the method likely leverages vectorized instructions, leading to performance gains. They also emphasized that the specific hardware and runtime environment play a crucial role in determining which method is faster. This comment reinforced the idea that performance optimization is context-dependent and requires careful consideration of various factors.Adding to the discussion about alignment, one user suggested that the choice between
SequenceEqualsandmemcmpcould depend on the expected data patterns. For frequently unaligned data,SequenceEqualsmight be the better option. Conversely, if data alignment is guaranteed or highly probable,memcmpcould be preferred. This practical advice provided a useful guideline for developers facing similar optimization challenges.The potential overhead of range checks in
SequenceEqualswas also brought up. One comment suggested that these checks, while important for safety, might introduce some performance cost. However, they acknowledged that modern compilers are often capable of eliminating redundant checks, mitigating this potential issue.Finally, a commenter emphasized the importance of accurate benchmarking methodology. They suggested using established benchmarking libraries to ensure reliable and repeatable results. This comment highlighted the importance of rigorous testing when comparing performance.
Overall, the comments provide a valuable extension to the original article. They offer insights into the complexities of performance optimization, emphasizing the importance of data alignment, hardware specifics, and accurate benchmarking. The discussion moves beyond a simple comparison of two methods and explores the nuances of their behavior in different scenarios.