This blog post by Jeff Smits explores a specific technique for optimizing Generalized LR (GLR) parsing, known as right-nulled GLR parsing. GLR parsing is a powerful parsing method capable of handling ambiguous grammars, which are common in real-world programming languages. However, the generality of GLR comes at the cost of increased complexity and potentially significant performance overhead due to the need to maintain multiple parse states simultaneously. This overhead is particularly pronounced when dealing with rules containing nullable (or "epsilon") productions, which can derive the empty string.
The post focuses on addressing this performance bottleneck. Standard GLR parsing creates a substantial number of states and transitions, especially when faced with nullable productions on the right-hand side of grammar rules. These nullable productions lead to a proliferation of possible parsing paths that the GLR algorithm must explore, resulting in a combinatorial explosion of states in certain scenarios.
Right-nulled GLR parsing mitigates this issue by pre-computing the effects of nullable productions. Instead of explicitly representing all possible combinations of nullable derivations during parsing, the algorithm effectively "factors out" the nullable components. This allows the parser to bypass the creation and exploration of many redundant states. The blog post describes how this pre-computation is performed, illustrating the transformation of grammar rules to eliminate nullable right-hand side elements.
The core idea is to modify the grammar itself to account for the possible presence or absence of nullable symbols. This transformation involves creating new grammar rules that effectively "absorb" the nullable symbols into the preceding non-nullable symbols. This process avoids the need to constantly consider whether a nullable symbol has been derived or not during the parsing process, streamlining the state transitions and reducing the overall number of states required.
The post uses a concrete example to demonstrate the mechanics of right-nulling. It shows how a simple grammar with nullable productions can be transformed into an equivalent grammar without nullable right-hand sides. This transformed grammar allows for more efficient parsing using the GLR algorithm because it avoids the creation of numerous temporary states associated with the nullable derivations. The result is a more optimized parsing process with reduced state explosion and improved performance, particularly in grammars with a significant number of nullable productions.
The post highlights the performance benefits of right-nulled GLR parsing, implying a significant reduction in the number of states generated compared to traditional GLR. It positions this technique as a valuable optimization for parsing ambiguous grammars while mitigating the performance penalties typically associated with nullable productions within those grammars. Although not explicitly mentioned, the technique likely finds application in areas where efficient parsing of complex or ambiguous grammars is critical, such as compiler design and language processing.
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.
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.
The arXiv preprint "Compiling C to Safe Rust, Formalized" details a novel approach to automatically translating C code into memory-safe Rust code. This process aims to leverage the performance benefits of C while inheriting the robust memory safety guarantees offered by Rust, thereby mitigating the pervasive vulnerability landscape associated with C programming.
The authors introduce a sophisticated compilation pipeline founded on a formal semantic model. This model rigorously defines the behavior of both the source C code and the target Rust code, enabling a precise and verifiable translation process. The core of this pipeline utilizes a "stacked borrows" model, a memory management strategy adopted by Rust that enforces strict rules regarding shared mutable references and mutable borrows to prevent data races and memory corruption. The translation procedure systematically transforms C pointers into Rust references governed by these stacked borrows rules, ensuring that the resulting Rust code adheres to the same memory safety principles inherent in Rust's design.
A key challenge addressed by the paper is the handling of C's flexible pointer arithmetic and unrestricted memory access patterns. The authors introduce a concept of "ghost state" within the formal model. This ghost state tracks the provenance and validity of pointers throughout the C code, allowing the compiler to reason about pointer relationships and enforce memory safety during translation. This information is then leveraged to generate corresponding safe Rust constructs, such as safe references and bounds checks, that mirror the intended behavior of the original C code while respecting Rust's stricter memory model.
The paper demonstrates the effectiveness of their approach through a formalization within the Coq proof assistant. This formalization rigorously verifies the soundness of the translation process, proving that the generated Rust code preserves the semantics of the original C code while guaranteeing memory safety. This rigorous verification provides strong evidence for the correctness and reliability of the proposed compilation technique.
Furthermore, the authors outline how their approach accommodates various C language features, including function pointers, structures, and unions. They describe how these features are mapped to corresponding safe Rust equivalents, thereby expanding the scope of the translation process to cover a wider range of C code.
While the paper primarily focuses on the formal foundations and theoretical aspects of the C-to-Rust translation, it also lays the groundwork for future development of a practical compiler toolchain based on these principles. Such a toolchain could offer a valuable pathway for migrating existing C codebases to a safer environment while minimizing manual rewriting effort and preserving performance characteristics. The formal verification aspect provides a high degree of confidence in the safety of the translated code, a crucial consideration for security-critical applications.
The Hacker News post titled "Compiling C to Safe Rust, Formalized" (https://news.ycombinator.com/item?id=42476192) has generated a moderate amount of discussion, with several commenters exploring different aspects of the C to Rust transpilation process and its implications.
One of the most prominent threads revolves around the practical benefits and challenges of such a conversion. A commenter points out the potential for improved safety and maintainability by leveraging Rust's ownership and borrowing system, but also acknowledges the difficulty in translating C's undefined behavior into a Rust equivalent. This leads to a discussion about the trade-offs between preserving the original C code's semantics and enforcing Rust's stricter safety guarantees. The difficulty of handling C's reliance on pointer arithmetic and manual memory management is highlighted as a major hurdle.
Another key area of discussion centers around the performance implications of the transpilation. Commenters speculate about the potential for performance improvements due to Rust's closer-to-the-metal nature and its ability to optimize memory access. However, others raise concerns about the overhead introduced by Rust's safety checks and the potential for performance regressions if the translation isn't carefully optimized. The question of whether the generated Rust code would be idiomatic and performant is also raised.
The topic of formal verification and its role in ensuring the correctness of the translation is also touched upon. Commenters express interest in the formalization aspect, recognizing its potential to guarantee that the translated Rust code behaves equivalently to the original C code. However, some skepticism is voiced about the practicality of formally verifying complex C codebases and the potential for subtle bugs to slip through even with formal methods.
Finally, several commenters discuss alternative approaches to improving the safety and security of C code, such as using static analysis tools or employing safer subsets of C. The transpilation approach is compared to these alternatives, with varying opinions on its merits and drawbacks. The overall sentiment seems to be one of cautious optimism, with many acknowledging the potential of C to Rust transpilation but also recognizing the significant challenges involved.
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https://news.ycombinator.com/item?id=42673617
Hacker News users discuss the practicality and efficiency of GLR parsing, particularly in comparison to other parsing techniques. Some commenters highlight its theoretical power and ability to handle ambiguous grammars, while acknowledging its potential performance overhead. Others question its suitability for real-world applications, suggesting that simpler methods like PEG or recursive descent parsers are often sufficient and more efficient. A few users mention specific use cases where GLR parsing shines, such as language servers and situations requiring robust error recovery. The overall sentiment leans towards appreciating GLR's theoretical elegance but expressing reservations about its widespread adoption due to perceived complexity and performance concerns. A recurring theme is the trade-off between parsing power and practical efficiency.
The Hacker News post titled "(Right-Nulled) Generalised LR Parsing," linking to an article explaining generalized LR parsing, has a moderate number of comments, sparking a discussion primarily around the practical applications and tradeoffs of GLR parsing.
One compelling comment thread focuses on the performance characteristics of GLR parsers. A user points out that the theoretical worst-case performance of GLR parsing can be quite poor, mentioning exponential time complexity. Another user counters this by arguing that in practice, GLR parsers perform well for most grammars used in programming languages, suggesting the worst-case scenarios are rarely encountered in real-world use. They further elaborate that the perceived performance issues might stem from naive implementations or poorly designed grammars, not inherently from the GLR algorithm itself. This back-and-forth highlights the disconnect between theoretical complexity and practical performance in parsing.
Another interesting point raised is the ease of use and debugging of GLR parsers. One commenter suggests that the ability of GLR parsers to handle ambiguous grammars makes them easier to use initially, as developers don't need to meticulously eliminate all ambiguities upfront. However, another user cautions that this can lead to difficulties later on when debugging, as the parser might silently accept incorrect inputs or produce unexpected parse trees due to the inherent ambiguity. This discussion emphasizes the trade-off between initial development speed and long-term maintainability when choosing a parsing strategy.
The practicality of using GLR parsers for different languages is also debated. While acknowledged as a powerful technique, some users express skepticism about its suitability for mainstream languages like C++, citing the complexity of the grammar and the potential performance overhead. Others suggest that GLR parsing might be more appropriate for niche languages or domain-specific languages (DSLs) where expressiveness and flexibility are prioritized over raw performance.
Finally, there's a brief discussion about alternative parsing techniques, such as PEG parsers. One commenter mentions that PEG parsers can be easier to understand and implement compared to GLR parsers, offering a potentially simpler solution for certain parsing tasks. This introduces the idea that GLR parsing, while powerful, isn't the only or necessarily the best solution for all parsing problems.