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.
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.
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.
Chips and Cheese investigated Zen 5's AVX-512 behavior and found that while AVX-512 is enabled and functional, using these instructions significantly reduces clock speeds. Their testing shows a consistent frequency drop across various AVX-512 workloads, with performance ultimately worse than using AVX2 despite the higher theoretical throughput of AVX-512. This suggests that AMD likely enabled AVX-512 for compatibility rather than performance, and users shouldn't expect a performance uplift from applications leveraging these instructions on Zen 5. The power consumption also significantly increases with AVX-512 workloads, exceeding even AMD's own TDP specifications.
Hacker News users discussed the potential implications of the observed AVX-512 frequency behavior on Zen 5. Some questioned the benchmarks, suggesting they might not represent real-world workloads and pointed out the importance of considering power consumption alongside frequency. Others discussed the potential benefits of AVX-512 despite the frequency drop, especially for specific workloads. A few comments highlighted the complexity of modern CPU design and the trade-offs involved in balancing performance, power efficiency, and heat management. The practicality of disabling AVX-512 for higher clock speeds was also debated, with users considering the potential performance hit from switching instruction sets. Several users expressed interest in further benchmarks and a more in-depth understanding of the underlying architectural reasons for the observed behavior.
The blog post details the creation of an extremely fast phrase search algorithm leveraging the AVX-512 instruction set, specifically the VPCONFLICTM instruction. This instruction, designed to detect hash collisions, is repurposed to efficiently find exact occurrences of phrases within a larger text. By cleverly encoding both the search phrase and the text into a format suitable for VPCONFLICTM, the algorithm can rapidly compare multiple sections of the text against the phrase simultaneously. This approach bypasses the character-by-character comparisons typical in other string search methods, resulting in significant performance gains, particularly for short phrases. The author showcases impressive benchmarks demonstrating substantial speed improvements compared to existing techniques.
Several Hacker News commenters express skepticism about the practicality of the described AVX-512 phrase search algorithm. Concerns center around the limited availability of AVX-512 hardware, the potential for future deprecation of the instruction set, and the complexity of the code making it difficult to maintain and debug. Some question the benchmark methodology and the real-world performance gains compared to simpler SIMD approaches or existing optimized libraries. Others discuss the trade-offs between speed and portability, suggesting that the niche benefits might not outweigh the costs for most use cases. There's also a discussion of alternative approaches and the potential for GPUs to outperform CPUs in this task. Finally, some commenters express fascination with the cleverness of the algorithm despite its practical limitations.
The blog post argues that C's insistence on abstracting away hardware details makes it poorly suited for effectively leveraging SIMD instructions. While extensions like intrinsics exist, they're cumbersome, non-portable, and break C's abstraction model. The author contends that higher-level languages, potentially with compiler support for automatic vectorization, or even assembly language for critical sections, would be more appropriate for SIMD programming due to the inherent need for data layout awareness and explicit control over vector operations. Essentially, C's strengths become weaknesses when dealing with SIMD, hindering performance and programmer productivity.
Hacker News users discussed the challenges of using SIMD effectively in C. Several commenters agreed with the author's point about the difficulty of expressing SIMD operations elegantly in C and how it often leads to unmaintainable code. Some suggested alternative approaches, like using higher-level languages or libraries that provide better abstractions, such as ISPC. Others pointed out the importance of compiler optimizations and using intrinsics effectively to achieve optimal performance. One compelling comment highlighted that the issue isn't inherent to C itself, but rather the lack of suitable standard library support, suggesting that future additions to the standard library could mitigate these problems. Another commenter offered a counterpoint, arguing that C's low-level nature is exactly why it's suitable for SIMD, giving programmers fine-grained control over hardware resources.
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.