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.
This comprehensive guide, titled "BCPL Programming on the Raspberry Pi," serves as an introduction to the BCPL programming language specifically tailored for use on the Raspberry Pi platform. It aims to provide novice programmers, particularly young individuals, with a foundational understanding of BCPL and equip them with the necessary skills to develop functional programs on their Raspberry Pi.
The document begins with a brief historical overview of BCPL, highlighting its influence as a precursor to the widely-used C programming language. This historical context establishes BCPL's significance in the evolution of programming languages. The guide then proceeds to detail the installation process of the Cintcode BCPL interpreter on a Raspberry Pi system, offering clear, step-by-step instructions to ensure a smooth setup.
Following the installation, the core concepts of BCPL programming are systematically introduced. This includes a detailed explanation of fundamental data types like integers and vectors (arrays), along with guidance on using operators for arithmetic and logical operations. Control flow mechanisms, crucial for directing program execution, are also covered comprehensively, encompassing conditional statements (IF, TEST), loops (WHILE, FOR), and switch statements (SWITCHON). The guide emphasizes the importance of structured programming techniques to promote clarity and maintainability in BCPL code.
The guide further delves into more advanced topics such as procedures (functions) and the concept of separate compilation. It elucidates how to define and call procedures, enabling modular program design and code reuse. The separate compilation feature allows developers to break down larger programs into smaller, manageable modules that can be compiled independently and then linked together. This promotes efficient development and simplifies debugging.
Input and output operations are also addressed, demonstrating how to interact with the user via the console and how to manipulate files. The guide provides examples of reading and writing data to files, enabling persistent storage of information.
Throughout the guide, numerous examples of BCPL code snippets are interspersed to illustrate the practical application of the concepts being discussed. These practical demonstrations reinforce the theoretical explanations and facilitate a deeper understanding of BCPL syntax and functionality. The document concludes with a series of suggested programming exercises designed to challenge the reader and encourage further exploration of BCPL's capabilities on the Raspberry Pi. These exercises provide hands-on experience and promote the development of practical programming skills. In essence, the document serves as a self-contained, accessible resource for anyone interested in learning BCPL programming in the context of the Raspberry Pi.
The Hacker News post titled "Young Persons Guide to BCPL Programming on the Raspberry Pi [pdf]" has several comments discussing the linked PDF and BCPL in general. A recurring theme is nostalgia and appreciation for the simplicity and elegance of BCPL.
One commenter recalls using BCPL on a Xerox Data Systems Sigma 9 in the early 1980s, highlighting its influence on C and emphasizing its small size and speed. They appreciate the document for its historical context and clear explanation of bootstrapping.
Another commenter focuses on the educational value of the document, suggesting that working through it provides valuable insight into how software works at a fundamental level, from bare metal up. They praise the clear writing style and the practical approach of using a Raspberry Pi.
A few comments delve into the history of BCPL, mentioning its relationship to CPL and C, and how it was a dominant language for systems programming before C took over. One user explains that BCPL was instrumental in the development of the original boot ROM for the Amiga. They also mention its continued use in some specialized areas due to its compact runtime.
Some comments express interest in trying BCPL on a modern platform like the Raspberry Pi. They discuss the potential benefits of learning such a foundational language and the practical experience it offers in understanding system architecture and bootstrapping.
Several commenters share personal anecdotes about their experiences with BCPL or related languages, giving the discussion a sense of historical perspective. One person talks about using BCPL in the 1970s and remembers the challenges of using paper tape. Another recounts learning C before BCPL and finding the differences fascinating.
The overall sentiment in the comments is positive, with many expressing admiration for BCPL's simplicity and power. The document is praised for being well-written, informative, and historically relevant. The discussion provides a glimpse into the enduring interest in older programming languages and the desire to understand the foundations of modern computing.
The Hacker News post introduces Zyme, a novel programming language designed with evolvability as its core principle. Zyme aims to facilitate the automatic creation and refinement of programs through evolutionary computation techniques, mimicking the process of natural selection. Instead of relying on traditional programming paradigms, Zyme utilizes a tree-based representation of code, where programs are structured as hierarchical expressions. This tree structure allows for easy manipulation and modification, making it suitable for evolutionary algorithms that operate by mutating and recombining code fragments.
The language itself is described as minimalistic, featuring a small set of primitive operations that can be combined to express complex computations. This minimalist approach reduces the search space for evolutionary algorithms, making the process of finding effective programs more efficient. The core primitives include arithmetic operations, conditional logic, and functions for manipulating the program's own tree structure, enabling self-modification. This latter feature is particularly important for evolvability, as it allows programs to adapt their own structure and behavior during the evolutionary process.
Zyme provides an interactive environment for experimentation and development. Users can define a desired behavior or task, and then employ evolutionary algorithms to automatically generate programs that exhibit that behavior. The fitness of a program is evaluated based on how well it matches the specified target behavior. Over successive generations, the population of programs evolves, with fitter individuals being more likely to reproduce and contribute to the next generation. This iterative process leads to the emergence of increasingly complex and sophisticated programs capable of solving the given task.
The post emphasizes Zyme's potential for exploring emergent behavior and solving complex problems in novel ways. By leveraging the power of evolution, Zyme offers a different approach to programming, shifting the focus from manual code creation to the design of evolutionary processes that can automatically discover efficient and effective solutions. The website includes examples and demonstrations of Zyme's capabilities, showcasing its ability to evolve programs for tasks like image processing and game playing. It also provides resources for learning the language and contributing to its development, suggesting a focus on community involvement in shaping Zyme's future.
The Hacker News post "Show HN: Zyme – An Evolvable Programming Language" sparked a discussion with several interesting comments.
Several commenters express interest in the project and its potential. One commenter mentions the connection to "Genetic Programming," acknowledging the long-standing interest in this field and Zyme's contribution to it. They also raise a question about Zyme's practical applications beyond theoretical exploration. Another commenter draws a parallel between Zyme and Wolfram Language, highlighting the shared concept of symbolic programming, but also questioning Zyme's unique contribution. This commenter seems intrigued but also cautious, prompting a need for clearer differentiation and practical examples. A different commenter focuses on the aspect of "evolvability" being central to genetic programming, subtly suggesting that the project description might benefit from emphasizing this aspect more prominently.
One commenter expresses skepticism about the feasibility of using genetic programming to solve complex problems, pointing out the challenges of defining effective fitness functions. They allude to the common issue in genetic programming where generated solutions might achieve high fitness scores in contrived examples but fail to generalize to real-world scenarios.
Furthering the discussion on practical applications, one commenter questions the current state of usability of Zyme for solving real-world problems. They express a desire to see concrete examples or success stories that would showcase the language's practical capabilities. This comment highlights a general interest in understanding how Zyme could be used beyond theoretical or academic contexts.
Another commenter requests clarification about how Zyme handles the issue of program bloat, a common problem in genetic programming where evolved programs can become excessively large and inefficient. This technical question demonstrates a deeper engagement with the technical aspects of Zyme and the challenges inherent in genetic programming.
Overall, the comments reveal a mix of curiosity, skepticism, and a desire for more concrete examples and clarification on Zyme's capabilities and differentiation. The commenters acknowledge the intriguing concept of an evolvable programming language, but also raise important questions about its practicality, usability, and potential to overcome the inherent challenges of genetic programming.
Summary of Comments ( 0 )
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.