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.
This blog post, titled "Performance optimization, and how to do it wrong," chronicles the author's journey in optimizing a 2D convolution operation, a common image processing technique. The author initially approaches the problem with a focus on utilizing SIMD (Single Instruction, Multiple Data) instructions, a hardware-level optimization that allows for parallel processing of data. Believing that SIMD vectorization is the key to significant performance gains, they embark on refactoring their code to make it compatible with SIMD intrinsics, which are specialized functions that directly map to SIMD instructions. This refactoring involves restructuring data layouts and modifying the core convolution logic to operate on vectors of data rather than individual elements.
The author details the intricacies of this process, explaining how they carefully arranged data in memory to align with SIMD requirements and adapted the convolution algorithm to work with these vectorized data structures. They express confidence that this approach will yield substantial performance improvements, anticipating a noticeable speedup due to the inherent parallelism of SIMD.
However, upon benchmarking the optimized SIMD version against the original scalar code, the author discovers a surprising result: the SIMD implementation is actually slower. This unexpected outcome prompts a deeper investigation into the performance characteristics of both implementations. Through profiling and analysis, the author identifies a critical bottleneck in the SIMD version: memory access patterns. While the SIMD code performs calculations faster on smaller chunks of data, the non-sequential memory access required to gather data for these calculations introduces significant overhead. This overhead negates the gains achieved through SIMD parallelism, resulting in a net performance degradation.
The author then pivots their optimization strategy, shifting focus from SIMD to optimizing memory access. They recognize that minimizing cache misses and ensuring contiguous memory access is paramount for performance. By restructuring the code to operate on larger blocks of data and improving data locality, they effectively reduce the memory access overhead. This revised approach, which prioritizes efficient memory access over explicit SIMD vectorization, leads to substantial performance improvements, ultimately outperforming both the original scalar code and the initial SIMD attempt.
The blog post concludes by emphasizing the importance of holistic performance analysis and cautions against prematurely focusing on specific optimization techniques like SIMD. The author highlights the crucial role of profiling and benchmarking in identifying true performance bottlenecks and advocates for a data-driven approach to optimization, prioritizing efficient memory access and algorithm design over presumed low-level optimizations that may introduce unforeseen overheads. The experience serves as a valuable lesson in performance optimization, demonstrating that while SIMD can be a powerful tool, it is not a silver bullet and must be applied judiciously, considering the overall memory access patterns and algorithmic structure.
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.