The blog post explores how to optimize std::count_if for better auto-vectorization, particularly with complex predicates. While standard implementations often struggle with branchy or function-object-based predicates, the author demonstrates a technique using a lambda and explicit bitwise operations on the boolean results to guide the compiler towards generating efficient SIMD instructions. This approach leverages the predictable size and alignment of bool within std::vector and allows the compiler to treat them as a packed array amenable to vectorized operations, outperforming the standard library implementation in specific scenarios. This optimization is particularly beneficial when the predicate involves non-trivial computations where branching would hinder vectorization gains.
Cal Bryant created a Python script to generate interlocking jigsaw puzzle pieces for 3D models, enabling the printing of objects larger than a printer's build volume. The script slices the model into customizable, interlocking chunks that can be individually printed and then assembled. The blog post details the process, including the Python code, demonstrating its use with a large articulated dragon model printed in PLA. The jigsaw approach simplifies large-scale 3D printing by removing the need for complex post-processing and allowing for greater design freedom.
HN commenters generally praised the project for its cleverness and potential applications. Several suggested improvements or alternative approaches, such as using dovetails for stronger joints, exploring different infill patterns for lighter prints, and considering kerf bends for curved surfaces. Some pointed out existing tools like OpenSCAD that could be leveraged. There was discussion about the practicality of printing large objects in pieces and the challenges of assembly, with suggestions like numbered pieces and alignment features. A few users expressed interest in using the tool for specific projects like building a kayak or a large enclosure. The creator responded to several comments, clarifying design choices and acknowledging the suggestions for future development.
Summary of Comments ( 9 )
https://news.ycombinator.com/item?id=43302394
The Hacker News comments discuss the surprising difficulty of getting
std::count_ifto auto-vectorize effectively. Several commenters point out the importance of using simple predicates for optimal compiler optimization, with one highlighting how seemingly minor changes, like usingstd::isupperinstead of a lambda, can dramatically impact performance. Another commenter notes that while the article focuses on GCC, clang often auto-vectorizes more readily. The discussion also touches on the nuances of benchmarking and the potential pitfalls of relying solely on compiler Explorer, as real-world performance can vary based on specific hardware and compiler versions. Some skepticism is expressed about the practicality of micro-optimizations like these, while others acknowledge their relevance in performance-critical scenarios. Finally, a few commenters suggest alternative approaches, like usingstd::ranges::count_if, which might offer better performance out of the box.The Hacker News post "Improving on std::count_if()'s auto-vectorization" discussing an article about optimizing
std::count_ifhas generated several interesting comments.Many commenters focus on the intricacies of compiler optimization and the difficulty in predicting or controlling auto-vectorization. One commenter points out that relying on specific compiler optimizations can be brittle, as compiler behavior can change with new versions. They suggest that while exploring these optimizations is interesting from a learning perspective, relying on them in production code can lead to unexpected performance regressions down the line. Another echoes this sentiment, noting that optimizing for one compiler might lead to de-optimizations in another. They suggest focusing on clear, concise code and letting the compiler handle the optimization unless profiling reveals a genuine bottleneck.
A recurring theme is the importance of profiling and benchmarking. Commenters stress that assumptions about performance can be misleading, and actual measurements are crucial. One user highlights the value of tools like Compiler Explorer for inspecting the generated assembly and understanding how the compiler handles different code constructs. This allows developers to see the direct impact of their code changes on the generated instructions and make more informed optimization decisions.
Several users discuss the specifics of the proposed optimizations in the article, comparing the use of
std::countwith manual loop unrolling and vectorization techniques. Some express skepticism about the magnitude of the performance gains claimed in the article, emphasizing the need for rigorous benchmarking on diverse hardware and compiler versions.There's also a discussion about the readability and maintainability of optimized code. Some commenters argue that the pursuit of extreme optimization can sometimes lead to code that is harder to understand and maintain, potentially increasing the risk of bugs. They advocate for a balanced approach where optimization efforts are focused on areas where they provide the most significant benefit without sacrificing code clarity.
Finally, some comments delve into the complexities of SIMD instructions and the challenges in effectively utilizing them. They point out that the effectiveness of SIMD can vary significantly depending on the data and the specific operations being performed. One commenter mentions that modern compilers are often quite good at auto-vectorizing simple loops, and manual vectorization might only be necessary in specific cases where the compiler fails to generate optimal code. They suggest starting with simple, clear code and only resorting to more complex optimization techniques after careful profiling reveals a genuine performance bottleneck.