The blog post "An epic treatise on error models for systems programming languages" explores the landscape of error handling strategies, arguing that current approaches in languages like C, C++, Go, and Rust are insufficient for robust systems programming. It criticizes unchecked exceptions for their potential to cause undefined behavior and resource leaks, while also finding fault with error codes and checked exceptions for their verbosity and tendency to hinder code flow. The author advocates for a more comprehensive error model based on "algebraic effects," which allows developers to precisely define and handle various error scenarios while maintaining control over resource management and program termination. This approach aims to combine the benefits of different error handling mechanisms while mitigating their respective drawbacks, ultimately promoting greater reliability and predictability in systems software.
This paper explores how Just-In-Time (JIT) compilers have evolved, aiming to provide a comprehensive overview for both newcomers and experienced practitioners. It covers the fundamental concepts of JIT compilation, tracing its development from early techniques like tracing JITs and method-based JITs to more modern approaches involving tiered compilation and adaptive optimization. The authors discuss key optimization techniques employed by JIT compilers, such as inlining, escape analysis, and register allocation, and analyze the trade-offs inherent in different JIT designs. Finally, the paper looks towards the future of JIT compilation, considering emerging challenges and research directions like hardware specialization, speculation, and the integration of machine learning techniques.
HN commenters generally express skepticism about the claims made in the linked paper attempting to make interpreters competitive with JIT compilers. Several doubt the benchmarks are representative of real-world workloads, suggesting they're too micro and don't capture the dynamic nature of typical programs where JITs excel. Some point out that the "interpreter" described leverages techniques like speculative execution and adaptive optimization, blurring the lines between interpretation and JIT compilation. Others note the overhead introduced by the proposed approach, particularly in terms of memory usage, might negate any performance gains. A few highlight the potential value in exploring alternative execution models but caution against overstating the current results. The lack of open-source code for the presented system also draws criticism, hindering independent verification and further exploration.
This blog post chronicles the author's weekend project of building a compiler for a simplified C-like language. It walks through the implementation of a lexical analyzer, parser (using recursive descent), and code generator targeting x86-64 assembly. The compiler handles basic arithmetic operations, variable declarations and assignments, if/else statements, and while loops. The post emphasizes simplicity and educational value over performance or completeness, providing a practical example of compiler construction principles in a digestible format. The code is available on GitHub for readers to explore and experiment with.
HN users largely praised the TinyCompiler project for its educational value, highlighting its clear code and approachable structure as beneficial for learning compiler construction. Several commenters discussed extending the compiler's functionality, such as adding support for different architectures or optimizing the generated code. Some pointed out similar projects or resources, like the "Let's Build a Compiler" tutorial and the Crafting Interpreters book. A few users questioned the "weekend" claim in the title, believing the project would take significantly longer for a novice to complete. The post also sparked discussion about the practical applications of such a compiler, with some suggesting its use for educational purposes or embedding in resource-constrained environments. Finally, there was some debate about the complexity of the compiler compared to more sophisticated tools like LLVM.
"Tiny Pointers" introduces a technique to reduce pointer size in C/C++ programs, thereby lowering memory usage without significantly impacting performance. The core idea involves restricting pointers to smaller regions of memory, enabling them to be represented with fewer bits. The paper details several methods for achieving this, including static analysis, profile-guided optimization, and dynamic recompilation. Experimental results demonstrate memory savings of up to 40% with negligible performance overhead in various benchmarks and real-world applications. This approach offers a promising solution for memory-constrained environments, particularly embedded systems and mobile devices.
HN users discuss the implications of "tiny pointers," focusing on potential performance improvements and drawbacks. Some doubt the practicality due to increased code complexity and the overhead of managing pointer metadata. Concerns are raised about compatibility with existing codebases and the potential for fragmentation in the memory allocator. Others express interest in exploring this concept further, particularly its application in specific scenarios like embedded systems or custom memory allocators where fine-grained control over memory is crucial. There's also discussion on whether the claimed benefits would outweigh the costs in real-world applications, with some suggesting that traditional optimization techniques might be more effective. A few commenters point out similar existing techniques like tagged pointers and debate the novelty of this approach.
The blog post explores various methods for generating Static Single Assignment (SSA) form, a crucial intermediate representation in compilers. It starts with the basic concepts of SSA, explaining dominance and phi functions. Then, it delves into different algorithms for SSA construction, including the classic dominance frontier algorithm and the more modern Cytron et al. algorithm. The post emphasizes the performance implications of these algorithms, highlighting how Cytron's approach optimizes placement of phi functions. It also touches upon less common methods like the iterative and memory-efficient Chaitin-Briggs algorithm. Finally, it briefly discusses register allocation and how SSA simplifies this process by providing a clear data flow representation.
HN users generally agreed with the author's premise that Single Static Assignment (SSA) form is beneficial for compiler optimization. Several commenters delved into the nuances of different SSA construction algorithms, highlighting Cytron et al.'s algorithm for its efficiency and prevalence. The discussion also touched on related concepts like minimal SSA, pruned SSA, and the challenges of handling irreducible control flow graphs. Some users pointed out practical considerations like register allocation and the trade-offs between SSA forms. One commenter questioned the necessity of SSA for modern optimization techniques, sparking a brief debate about its relevance. Others offered additional resources, including links to relevant papers and implementations.
The blog post argues for an intermediate representation (IR) layer in query compilers between the logical plan and the physical plan, called the "relational algebra IR." This layer would represent queries in a standardized, relational algebra form, enabling greater portability and reusability of optimization rules across different physical execution engines. Currently, optimization logic is often tightly coupled to specific physical plans, making it difficult to adapt to new engines or hardware. By introducing this standardized relational algebra IR, query compilers can achieve better modularity and extensibility, simplifying development and allowing for easier experimentation with new optimization strategies without needing to rewrite code for each backend. This ultimately leads to more efficient query execution across diverse environments.
HN commenters generally agree with the author's premise that a middle tier is missing in query compilers, sitting between logical optimization and physical optimization. This tier would handle "cross-physical plan" optimizations, allowing for better cost-based decisions that consider different physical plan choices holistically rather than sequentially. Some discuss the challenges in implementing this, particularly the explosion of search space and the difficulty in accurately costing plans. Others offer specific examples where such a tier would be beneficial, such as selecting join algorithms based on data distribution or optimizing for specific hardware like GPUs. A few commenters mention existing systems that implement similar concepts, though not necessarily as a distinct tier, suggesting the idea is already being explored in practice. Some debate the practicality of the proposed solution, suggesting alternative approaches like adaptive query execution or learned optimizers.
The blog post details methods for eliminating left and mutual recursion in context-free grammars, crucial for parser construction. Left recursion, where a non-terminal derives itself as the leftmost symbol, is problematic for top-down parsers. The post demonstrates how to remove direct left recursion using factorization and substitution. It then explains how to handle indirect left recursion by ordering non-terminals and systematically applying the direct recursion removal technique. Finally, it addresses mutual recursion, where two or more non-terminals derive each other, converting it into direct left recursion, which can then be eliminated using the previously described methods. The post uses concrete examples to illustrate these transformations, making it easier to understand the process of converting a grammar into a parser-friendly form.
Hacker News users discussed the potential inefficiency of the presented left-recursion elimination algorithm, particularly its reliance on repeated string concatenation. They suggested alternative approaches using stacks or accumulating results in a list for better performance. Some commenters questioned the necessity of fully eliminating left recursion in all cases, pointing out that modern parsing techniques, like packrat parsing, can handle left-recursive grammars directly. The lack of formal proofs or performance comparisons with established methods was also noted. A few users discussed the benefits and drawbacks of different parsing libraries and techniques, including ANTLR and various parser combinator libraries.
Astral is a new static type checker being developed for Python that aims to be faster and more ergonomic than existing options like MyPy. It leverages a new type inference algorithm designed for performance and boasts features like auto-completion, goto-definition, and an improved developer experience. The project is still early in development but claims significant speed improvements, with a goal of being at least 5x faster than MyPy on real-world codebases. Astral also intends to offer seamless integration with existing Python tooling and provide enhanced support for popular libraries like NumPy and Pandas.
Hacker News users discuss Astral's potential, drawing parallels to MyPy but with a focus on performance. Some express skepticism about static typing in Python, questioning its necessity and impact on the language's flexibility. Others are interested in Astral's approach to gradual typing and its ability to handle complex codebases. Performance improvements over MyPy are frequently mentioned as a key benefit. Several commenters inquire about specific features, such as handling metaclasses and integration with existing tools. Overall, there's a mix of cautious optimism and interest in seeing how Astral develops.
Mukul Rathi details his journey of creating a custom programming language, focusing on the compiler construction process. He explains the key stages involved, from lexing (converting source code into tokens) and parsing (creating an Abstract Syntax Tree) to code generation and optimization. Rathi uses his language, which he implements in OCaml, to illustrate these concepts, providing code examples and explanations of how each component works together to transform high-level code into executable machine instructions. He emphasizes the importance of understanding these foundational principles for anyone interested in building their own language or gaining a deeper appreciation for how programming languages function.
Hacker News users generally praised the article for its clarity and accessibility in explaining compiler construction. Several commenters appreciated the author's approach of building a complete, albeit simple, language instead of just a toy example. Some pointed out the project's similarity to the "Let's Build a Compiler" series, while others suggested alternative or supplementary resources like Crafting Interpreters and the LLVM tutorial. A few users discussed the tradeoffs between hand-written lexers/parsers and using parser generator tools, and the challenges of garbage collection implementation. One commenter shared their personal experience of writing a language and the surprising complexity of seemingly simple features.
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https://news.ycombinator.com/item?id=43297574
HN commenters largely praised the article for its thoroughness and clarity in explaining error handling strategies. Several appreciated the author's balanced approach, presenting the tradeoffs of each model without overtly favoring one. Some highlighted the insightful discussion of checked exceptions and their limitations, particularly in relation to algebraic error types and error-returning functions. A few commenters offered additional perspectives, including the importance of distinguishing between recoverable and unrecoverable errors, and the potential benefits of static analysis tools in managing error handling. The overall sentiment was positive, with many thanking the author for providing a valuable resource for systems programmers.
The Hacker News post titled "An epic treatise on error models for systems programming languages" (linking to an article about error handling in systems programming) has a moderate number of comments, generating a discussion around the presented error models and their practical implications.
Several commenters praise the article for its depth and clarity, calling it a "great read" and appreciating the author's systematic approach to breaking down a complex topic. One user specifically highlights the value of the article for those newer to systems programming, stating that it provides a good overview of various error handling approaches.
A significant portion of the discussion revolves around the trade-offs between different error models. Some commenters favor the "fail-fast" approach, emphasizing the importance of catching errors early to prevent cascading failures and data corruption. Others acknowledge the benefits of this approach in certain contexts but argue for more nuanced error handling in others. The discussion touches upon the complexities of handling errors in distributed systems, where immediate termination may not be feasible or desirable.
There's a back-and-forth regarding the use of exceptions. Some commenters express concerns about the performance overhead and potential for unexpected control flow disruptions associated with exceptions. Counterarguments highlight the benefits of exceptions for handling exceptional conditions and separating error handling logic from normal code flow. The discussion also touches upon the importance of careful exception handling practices to mitigate potential issues.
Specific languages and their error handling mechanisms are also brought up. Rust's
Resulttype and its approach to error handling are mentioned favorably by several commenters, who praise its ability to enforce explicit error handling at compile time. Comparisons are made to error handling in C++, Go, and other languages.One commenter raises the issue of the cognitive load imposed by different error models, arguing that simpler models can be easier to reason about and maintain. This sparks a brief discussion about the balance between robustness and complexity in error handling design.
Finally, a few commenters share personal anecdotes and experiences with different error handling approaches, offering practical insights and highlighting the challenges of dealing with errors in real-world systems. One commenter mentions the difficulties of debugging production issues caused by unexpected errors and emphasizes the importance of thorough testing and logging.