The blog post details a misguided attempt to optimize a 2D convolution operation. The author initially focuses on vectorization using SIMD instructions, expecting significant performance gains. However, after extensive effort, the improvements are minimal. The root cause is revealed to be memory bandwidth limitations: the optimized code, while processing data faster, is ultimately bottlenecked by the rate at which it can fetch data from memory. This highlights the importance of profiling and understanding performance bottlenecks before diving into optimization, as premature optimization targeting the wrong area can be wasted effort. The author learns a valuable lesson: focus on optimizing memory access patterns and reducing cache misses before attempting low-level optimizations like SIMD.
Refactoring, while often beneficial, should not be undertaken without careful consideration. The blog post argues against refactoring for its own sake, emphasizing that it should be driven by a clear purpose, like improving performance, adding features, or fixing bugs. Blindly pursuing "clean code" or preemptive refactoring can introduce new bugs, create unnecessary complexity, and waste valuable time. Instead, refactoring should be a strategic tool used to address specific problems and improve the maintainability of code that is actively being worked on, not a constant, isolated activity. Essentially, refactor with a goal, not just for aesthetic reasons.
Hacker News users generally disagreed with the premise of the blog post, arguing that refactoring is crucial for maintaining code quality and developer velocity. Several commenters pointed out that the article conflates refactoring with rewriting, which are distinct activities. Others suggested the author's negative experiences stemmed from poorly executed refactors, rather than refactoring itself. The top comments highlighted the long-term benefits of refactoring, including reduced technical debt, improved readability, and easier debugging. Some users shared personal anecdotes about successful refactoring efforts, while others offered practical advice on when and how to refactor effectively. A few conceded that excessive or unnecessary refactoring can be detrimental, but emphasized that this doesn't negate the value of thoughtful refactoring.
Summary of Comments ( 4 )
https://news.ycombinator.com/item?id=43257460
HN commenters largely agreed with the blog post's premise that premature optimization without profiling is counterproductive. Several pointed out the importance of understanding the problem and algorithm first, then optimizing based on measured bottlenecks. Some suggested tools like perf and VTune Amplifier for profiling. A few challenged the author's dismissal of SIMD intrinsics, arguing their usefulness in specific performance-critical scenarios, especially when compilers fail to generate optimal code. Others highlighted the trade-off between optimized code and readability/maintainability, emphasizing the importance of clear code unless absolute performance is paramount. A couple of commenters offered additional optimization techniques like loop unrolling and cache blocking.
The Hacker News post titled "Performance optimization, and how to do it wrong" (linking to an article about convolution SIMD) spawned a moderately active discussion with a mix of perspectives on optimization strategies.
Several commenters echoed the sentiment of the article, highlighting the importance of profiling and measuring before attempting optimizations. They cautioned against premature optimization and stressed that focusing on algorithmic improvements often yields more substantial gains than low-level tweaks. One commenter specifically mentioned how they once spent a week optimizing a piece of code, only to discover later that a simple algorithmic change made their optimization work irrelevant. Another pointed out that modern compilers are remarkably good at optimization, and hand-optimized code can sometimes be less efficient than compiler-generated code. This reinforces the idea of profiling first to identify genuine bottlenecks before diving into complex optimizations.
Some users discussed the value of SIMD instructions, acknowledging their potential power while also emphasizing the need for careful consideration. They pointed out that SIMD can introduce complexity and make code harder to maintain. One user argued that the performance gains from SIMD might not always justify the increased development time and potential for bugs. Another commenter added that the effectiveness of SIMD is highly architecture-dependent, meaning optimized code for one platform may not perform as well on another.
There was a thread discussing the role of domain-specific knowledge in optimization. Commenters emphasized that understanding the specific problem being solved can lead to more effective optimizations than generic techniques. They argued that optimizing for the "common case" within a specific domain can yield significant improvements.
A few commenters shared anecdotes about their experiences with performance optimization, both successful and unsuccessful. One recounted a story of dramatically improving performance by fixing a database query, illustrating how high-level optimizations can often overshadow low-level tweaks. Another mentioned the importance of considering the entire system when optimizing, as a fast component can be bottlenecked by a slow interaction with another part of the system.
Finally, a couple of comments focused on the trade-off between performance and code clarity. They argued that sometimes it's better to sacrifice a small amount of performance for more readable and maintainable code. One commenter suggested that optimization efforts should be focused on the critical sections of the codebase, leaving less performance-sensitive areas more readable.
In summary, the comments on the Hacker News post largely supported the article's premise: avoid premature optimization, profile and measure first, and consider higher-level algorithmic improvements before resorting to low-level tricks like SIMD. The discussion also touched upon the complexities of SIMD optimization, the importance of domain-specific knowledge, and the trade-offs between performance and code maintainability.