This paper demonstrates how seemingly harmless data races in C/C++ programs, specifically involving non-atomic operations on padding bytes, can lead to miscompilation by optimizing compilers. The authors show that compilers can exploit the assumption of data-race freedom to perform transformations that change program behavior when races are actually present. They provide concrete examples where races on padding bytes within structures cause compilers like GCC and Clang to generate incorrect code, leading to unexpected outputs or crashes. This highlights the subtle ways in which undefined behavior due to data races can manifest, even when the races appear to involve data irrelevant to program logic. Ultimately, the paper reinforces the importance of avoiding data races entirely, even those that might seem benign, to ensure predictable program behavior.
Hans-J. Boehm's paper, "How to miscompile programs with 'benign' data races," presented at HotPar 2011, explores the potential for seemingly harmless data races in multithreaded C or C++ programs to lead to unexpected and incorrect compiled code. The core issue stems from the compiler's aggressive optimizations, which are valid under the strict aliasing rules of the language standards but become problematic in the presence of data races. These optimizations, intended to improve performance, can rearrange or eliminate memory accesses based on the assumption that no other thread is concurrently modifying the same memory location.
The paper meticulously details how these "benign" data races, races that might not cause noticeable data corruption at runtime due to the specific values involved or the timing of operations, can interact with compiler optimizations to produce drastically different program behavior than intended. This occurs because the compiler, unaware of the potential for concurrent modification, may transform the code in ways that are invalid when a race is actually present.
Boehm illustrates this phenomenon through several compelling examples. These examples demonstrate how common compiler optimizations, such as code motion (reordering instructions), dead code elimination (removing seemingly unused code), and common subexpression elimination (replacing multiple identical calculations with a single instance), can interact with benign races to produce incorrect results. One illustrative scenario involves a loop counter being incorrectly optimized away due to a race condition, resulting in premature loop termination. Another example highlights how a compiler might incorrectly infer that a variable's value remains constant within a loop, leading to unexpected behavior when another thread concurrently modifies that variable.
The paper emphasizes that these issues arise not from compiler bugs, but from the inherent conflict between the standard's definition of undefined behavior in the presence of data races and the reality of multithreaded programming. While the standards permit compilers to make sweeping assumptions about the absence of data races, these assumptions are frequently violated in practice, even in code that appears to function correctly.
Boehm argues that the current approach of relying on programmers to avoid all data races is unrealistic and proposes alternative approaches. One suggestion is to restrict the scope of compiler optimizations in the presence of potentially shared variables, effectively limiting the compiler's ability to make assumptions about the absence of races. Another proposed approach involves modifying the memory model to explicitly define the behavior of data races in a more predictable manner. This would require a more relaxed memory model, potentially affecting performance, but offering greater robustness in the face of unintentional races.
The paper concludes by highlighting the seriousness of this problem, emphasizing the difficulty in diagnosing and debugging such issues, and advocating for a reassessment of the current approach to data races in C and C++ to ensure the reliability and predictability of multithreaded code. The overarching message is that even seemingly innocuous data races can have severe consequences on the correctness of compiled code due to the interaction with compiler optimizations, and that addressing this issue requires a fundamental rethinking of how data races are handled within the language standards and compiler implementations.
Summary of Comments ( 3 )
https://news.ycombinator.com/item?id=42661336
Hacker News users discussed the implications of Boehm's paper on benign data races. Several commenters pointed out the difficulty in truly defining "benign," as seemingly harmless races can lead to unexpected behavior in complex systems, especially with compiler optimizations. Some highlighted the importance of tools and methodologies to detect and prevent data races, even if deemed benign. One commenter questioned the practical applicability of the paper's proposed relaxed memory model, expressing concern that relying on "benign" races would make debugging significantly harder. Others focused on the performance implications, suggesting that allowing benign races could offer speed improvements but might not be worth the potential instability. The overall sentiment leans towards caution regarding the exploitation of benign data races, despite acknowledging the potential benefits.
The Hacker News post titled "How to miscompile programs with "benign" data races [pdf]" (linking to a PDF of Hans Boehm's presentation at HotPar '11) has several comments discussing the implications of the paper and its relevance to modern programming.
One commenter points out the significance of Boehm's work, particularly given his deep involvement in garbage collection. They note that even seemingly harmless data races, the kind often dismissed as benign, can lead to surprising and difficult-to-debug compiler optimizations gone awry. This highlights the importance of understanding the subtle ways data races can interact with compiler behavior.
Another commenter expresses concern about the implications for C++, a language where data races are undefined behavior. They suggest that, according to the paper, C++ compilers are allowed to make optimizations that could break code even with seemingly harmless data races. This reinforces the danger of undefined behavior and the importance of avoiding data races altogether, even those that appear benign at first glance.
A further comment emphasizes the importance of formal specifications for memory models, especially given the complexity introduced by multithreading and compiler optimizations. They highlight that without rigorous definitions of how memory operations behave in a concurrent environment, compiler writers are left with considerable leeway, which can lead to unexpected results. This ties back to the core issue of the paper, where seemingly benign data races expose this ambiguity.
Several commenters discuss the difficulty of reasoning about concurrency and the challenges of writing correct concurrent code. They note that the paper serves as a good reminder of these complexities and reinforces the need for careful consideration of memory ordering and synchronization primitives.
One commenter even speculates whether it is possible to write truly correct, high-performance concurrent C++ without relying on library abstractions like those found in Java's
java.util.concurrent. They suggest that the complexities highlighted in the paper make it exceptionally difficult to manage concurrency manually in C++.The overall sentiment in the comments reflects an appreciation for Boehm's work and its implications for concurrent programming. The commenters acknowledge the difficulty of writing correct concurrent code and the subtle ways in which seemingly innocuous data races can lead to unexpected and difficult-to-debug problems. They emphasize the importance of understanding memory models, compiler optimizations, and the need for robust synchronization mechanisms.