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.
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 ( 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.