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.
Vincent McHale's 2019 blog post, "C Is Not Suited to SIMD," argues that the C programming language, in its standard form, lacks the necessary features and abstractions to effectively utilize Single Instruction, Multiple Data (SIMD) instructions, which are crucial for maximizing performance on modern processors. McHale's central thesis is not that SIMD programming is impossible in C, but rather that the language itself provides inadequate support, leading to convoluted and error-prone code compared to languages with better integrated SIMD capabilities.
He begins by highlighting the performance benefits achievable with SIMD, emphasizing its importance in computationally intensive tasks. He then proceeds to dissect the challenges encountered when attempting SIMD programming within the confines of standard C. The core issue revolves around data types: C's fundamental data types do not inherently align with SIMD registers, which operate on vectors of data. This mismatch necessitates the use of non-standard extensions, such as compiler intrinsics or third-party libraries, which fragment the portability and readability of C code. McHale elaborates on the difficulties posed by these extensions, citing the verbose and complex syntax required to express relatively simple SIMD operations. He demonstrates how even basic tasks like loading and storing data to and from SIMD registers can become cumbersome and obscure the underlying logic.
The post then delves into the complexities of handling data alignment. SIMD instructions typically require data to be aligned in memory on specific boundaries. C's lack of built-in alignment guarantees further exacerbates the problem, forcing programmers to resort to manual alignment techniques, which introduce additional complexity and potential pitfalls. McHale illustrates the fragility of these workarounds, particularly when dealing with dynamically allocated memory or data structures involving pointers.
Further compounding the issue, according to McHale, is C's limited support for vector types. While some compilers provide extensions for vector types, these lack the expressiveness and flexibility of dedicated SIMD abstractions found in other languages. Consequently, C programmers often find themselves manipulating individual elements of SIMD vectors using scalar operations, negating the performance advantages of SIMD.
McHale concludes by contrasting C's SIMD limitations with the more streamlined approaches found in languages like C++ and Fortran. He suggests that these languages offer higher-level abstractions and built-in vector types, enabling more concise and efficient SIMD programming. He reiterates that while C remains a powerful language for many purposes, its lack of native support for SIMD makes it a suboptimal choice for performance-critical applications that can benefit significantly from SIMD parallelism. The overall message is that the inherent limitations of C in dealing with SIMD necessitates moving beyond the standard language and relying on compiler-specific extensions, thereby sacrificing portability and increasing code complexity for performance gains.
Summary of Comments ( 24 )
https://news.ycombinator.com/item?id=42808027
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.
The Hacker News post "C Is Not Suited to SIMD (2019)" has generated several comments discussing the challenges and complexities of using SIMD in C. Many commenters agree with the author's general premise, pointing out various pain points.
One compelling line of discussion revolves around the difficulty of expressing SIMD operations in a portable and maintainable way using standard C. Commenters highlight the verbose nature of intrinsics and the lack of higher-level abstractions, making code difficult to read and debug. The dependence on compiler-specific extensions and the lack of cross-platform guarantees are also cited as major drawbacks. Some users suggest that languages like C++ offer better alternatives through libraries and templates, providing more expressive power and portability.
Another key point raised is the tension between SIMD optimization and code clarity. Several comments argue that squeezing out maximum performance with SIMD often leads to complex and unreadable code, which can be a significant burden for maintenance and collaboration. The cost of such optimization, in terms of developer time and potential bugs, is questioned.
The discussion also touches upon the broader issue of software complexity and the trade-offs involved in optimizing for performance. Some commenters advocate for prioritizing code readability and maintainability over raw performance, especially in scenarios where the performance gains are marginal. They emphasize the importance of profiling and targeted optimization rather than prematurely resorting to complex SIMD techniques.
Several commenters share their personal experiences with SIMD programming in C, recounting the difficulties they encountered and the lessons they learned. These anecdotes provide practical insights into the challenges of using SIMD effectively and underscore the need for better tools and abstractions. Some suggest that higher-level languages or domain-specific languages could be more suitable for SIMD programming.
Finally, some commenters discuss alternative approaches to SIMD programming, such as using vectorized libraries or relying on compiler auto-vectorization. While these approaches can simplify development, they may not always achieve the same level of performance as manual SIMD optimization.
Overall, the comments on the Hacker News post reflect a shared frustration with the current state of SIMD programming in C. They highlight the need for better language features, libraries, and tools to make SIMD more accessible and manageable for developers.