One year after the "Free the GIL" project began, significant progress has been made towards enabling true parallelism in CPython. The project, focused on making the Global Interpreter Lock (GIL) optional, has seen successful integration of the "nogil" branch, demonstrating substantial performance improvements in multi-threaded workloads. While still experimental and requiring code adaptations for full compatibility, benchmarks reveal impressive speedups, particularly in numerical and scientific computing scenarios. The project's next steps involve refinement, continued performance optimization, and addressing compatibility issues to prepare for eventual inclusion in a future CPython release. This work paves the way for a significantly faster Python, particularly beneficial for CPU-bound applications.
Project Verona's Pyrona aims to introduce a new memory management model to Python, enabling "fearless concurrency." This model uses regions, isolated memory areas owned by specific tasks, which prevents data races and simplifies concurrent programming. Instead of relying on a global interpreter lock (GIL) like CPython, Pyrona utilizes multiple, independent interpreters, each operating within their own region. Communication between regions happens via immutable messages, ensuring safe data sharing. This approach allows Python to better leverage multi-core processors and improve performance in concurrent scenarios. While still experimental, Pyrona offers a potential path toward eliminating the GIL's limitations and unlocking more efficient parallel processing in Python.
Hacker News users discussed Project Verona's approach to memory management and its potential benefits for Python. Several commenters expressed interest in how Verona's ownership and borrowing system, inspired by Rust, could mitigate concurrency bugs and improve performance. Some questioned the practicality of integrating Verona with existing Python code and libraries, highlighting the potential challenges of adopting a new memory model. The discussion also touched on the trade-offs between safety and performance, with some suggesting that the overhead introduced by Verona's checks might outweigh the benefits in certain scenarios. Finally, commenters compared Verona to other approaches to concurrency in Python, such as using multiple interpreters or asynchronous programming, and debated their respective merits.
Haskell offers a powerful and efficient approach to concurrency, leveraging lightweight threads and clear communication primitives. Its unique runtime system manages these threads, enabling high performance without the complexities of manual thread management. Instead of relying on shared mutable state and locks, which are prone to errors, Haskell uses software transactional memory (STM) for safe concurrent data access. This allows developers to write concurrent code that is more composable, easier to reason about, and less susceptible to deadlocks and race conditions. Combined with asynchronous exceptions and other features, Haskell provides a robust and elegant framework for building highly concurrent and parallel applications.
Hacker News users generally praised the article for its clarity and conciseness in explaining Haskell's concurrency model. Several commenters highlighted the elegance of software transactional memory (STM) and its ability to simplify concurrent programming compared to traditional locking mechanisms. Some discussed the practical performance characteristics of STM, acknowledging its overhead but also noting its scalability and suitability for certain workloads. A few users compared Haskell's approach to concurrency with other languages like Clojure and Rust, sparking a brief debate about the trade-offs between different concurrency models. One commenter mentioned the learning curve associated with Haskell but emphasized the long-term benefits of its powerful type system and concurrency features. Overall, the comments reflect a positive reception of the article and a general appreciation for Haskell's approach to concurrency.
Inko is a programming language designed for building reliable and efficient concurrent software. It features a static type system with algebraic data types and pattern matching, aiding in catching errors at compile time. Inko's concurrency model leverages actors and message passing to avoid shared memory and the associated complexities of mutexes and locks. This actor-based approach, coupled with automatic memory management via garbage collection, aims to simplify the development of concurrent programs and reduce the risk of data races and other concurrency bugs. Furthermore, Inko prioritizes performance and offers efficient compilation to native code. The language seeks to provide a practical and robust solution for modern concurrent programming challenges.
Hacker News users discussed Inko's features, drawing comparisons to Rust and Pony. Several commenters expressed interest in the actor model and ownership/borrowing system for concurrency. Some questioned Inko's practicality and adoption potential given the existing competition, while others were curious about its performance characteristics and real-world applications. The garbage collection aspect was a point of contention, with some viewing it as a drawback for performance-critical applications. A few users also mentioned their previous experiences with the language, highlighting both positive and negative aspects. There was general curiosity about the language's maturity and the size of its community.
Servo, a modern, high-performance browser engine built in Rust, uses Open Collective to transparently manage its finances. The project welcomes contributions to support its ongoing development, including building a sustainable ecosystem around web components and improving performance, reliability, and interoperability. Donations are used for infrastructure costs, bounties, and travel expenses for contributors. While Mozilla previously spearheaded Servo's development, it's now a community-maintained project under the Linux Foundation, focused on empowering developers with cutting-edge web technology.
HN commenters discuss Servo's move to Open Collective, expressing skepticism about its long-term viability without significant corporate backing. Several users question the project's direction and whether a truly independent, community-driven browser engine is feasible given the resources required for ongoing development and maintenance, particularly regarding security and staying current with web standards. The difficulty of competing with established browsers like Chrome and Firefox is also highlighted. Some commenters express disappointment with the project's perceived lack of progress and question the practicality of its current focus, while others hold out hope for its future and praise its technical achievements. A few users suggest potential alternative directions, such as focusing on niche use-cases or becoming a rendering engine for other applications.
The author explores several programming language design ideas centered around improving developer experience and code clarity. They propose a system for automatically managing borrowed references with implicit borrowing and optional explicit lifetimes, aiming to simplify memory management. Additionally, they suggest enhancing type inference and allowing for more flexible function signatures by enabling optional and named arguments with default values, along with improved error messages for type mismatches. Finally, they discuss the possibility of incorporating traits similar to Rust but with a focus on runtime behavior and reflection, potentially enabling more dynamic code generation and introspection.
Hacker News users generally reacted positively to the author's programming language ideas. Several commenters appreciated the focus on simplicity and the exploration of alternative approaches to common language features. The discussion centered on the trade-offs between conciseness, readability, and performance. Some expressed skepticism about the practicality of certain proposals, particularly the elimination of loops and reliance on recursion, citing potential performance issues. Others questioned the proposed module system's reliance on global mutable state. Despite some reservations, the overall sentiment leaned towards encouragement and interest in seeing further development of these ideas. Several commenters suggested exploring existing languages like Factor and Joy, which share some similarities with the author's vision.
Hillel Wayne's post dissects the concept of "nondeterminism" in computer science, arguing that it's often used ambiguously and encompasses five distinct meanings. These are: 1) Implementation-defined behavior, where the language standard allows for varied outcomes. 2) Unspecified behavior, similar to implementation-defined but offering even less predictability. 3) Error/undefined behavior, where anything could happen, often leading to crashes. 4) Heisenbugs, which are bugs whose behavior changes under observation (e.g., debugging). 5) True nondeterminism, exemplified by hardware randomness or concurrency races. The post emphasizes that these are fundamentally different concepts with distinct implications for programmers, and understanding these nuances is crucial for writing robust and predictable software.
Hacker News users discussed various aspects of nondeterminism in the context of Hillel Wayne's article. Several commenters highlighted the distinction between predictable and unpredictable nondeterminism, with some arguing the author's categorization conflated the two. The importance of distinguishing between sources of nondeterminism, such as hardware, OS scheduling, and program logic, was emphasized. One commenter pointed out the difficulty in achieving true determinism even with seemingly simple programs due to factors like garbage collection and just-in-time compilation. The practical challenges of debugging nondeterministic systems were also mentioned, along with the value of tools that can help reproduce and analyze nondeterministic behavior. A few comments delved into specific types of nondeterminism, like data races and the nuances of concurrency, while others questioned the usefulness of the proposed categorization in practice.
Cell-based architecture offers a robust approach to designing complex systems by compartmentalizing them into independent "cells". Like a walled city protecting against a zombie horde, each cell operates autonomously with its own data and logic, communicating with other cells through well-defined interfaces. This isolation prevents cascading failures; if one cell gets "infected" (compromised or buggy), the infection is contained, preventing it from spreading and bringing down the entire system. This modularity also facilitates independent development, deployment, and scaling of individual cells, making the system more adaptable and resilient to change. By sacrificing some global optimization for localized control, cell-based architecture prioritizes stability and evolvability in the face of unforeseen challenges.
Hacker News users generally praised the article for its clear and engaging explanation of cell-based architecture using the zombie analogy. Several commenters appreciated the novelty and effectiveness of the analogy, finding it memorable and helpful for understanding complex systems. Some discussed the practical applications of cell-based architecture, mentioning its use in game development and other software projects. A few users offered alternative analogies or pointed out minor inaccuracies, but the overall sentiment was positive, with many thanking the author for the insightful and entertaining read. One commenter highlighted the importance of fault tolerance, a key benefit of cell-based systems, which the zombie analogy effectively illustrates.
Bjarne Stroustrup's "21st Century C++" blog post advocates for modernizing C++ usage by focusing on safety and performance. He highlights features introduced since C++11, like ranges, concepts, modules, and coroutines, which enable simpler, safer, and more efficient code. Stroustrup emphasizes using these tools to combat complexity and vulnerabilities while retaining C++'s performance advantages. He encourages developers to embrace modern C++, utilizing static analysis and embracing a simpler, more expressive style guided by the "keep it simple" principle. By moving away from older, less safe practices and leveraging new features, developers can write robust and efficient code fit for the demands of modern software development.
Hacker News users discussed the challenges and benefits of modern C++. Several commenters pointed out the complexities introduced by new features, arguing that while powerful, they contribute to a steeper learning curve and can make code harder to maintain. The benefits of concepts, ranges, and modules were acknowledged, but some expressed skepticism about their widespread adoption and practical impact due to compiler limitations and legacy codebases. Others highlighted the ongoing tension between embracing modern C++ and maintaining compatibility with existing projects. The discussion also touched upon build systems and the difficulty of integrating new C++ features into existing workflows. Some users advocated for simpler, more focused languages like Zig and Jai, suggesting they offer a more manageable approach to systems programming. Overall, the sentiment reflected a cautious optimism towards modern C++, tempered by concerns about complexity and practicality.
The post argues that the term "thread contention" is misused in the context of Ruby's Global VM Lock (GVL). True thread contention involves multiple threads attempting to modify the same shared resource simultaneously. However, in Ruby with the GVL, only one thread can execute Ruby code at any given time. What appears as "contention" is actually just queuing: threads waiting their turn to acquire the GVL. The post emphasizes that understanding this distinction is crucial for profiling and optimizing Ruby applications. Instead of focusing on eliminating "contention," developers should concentrate on reducing the time threads hold the GVL, minimizing the queueing time and improving overall performance.
HN commenters generally agree with the author's premise that Ruby's "thread contention" is largely a misunderstanding of the GVL (Global VM Lock). Several pointed out that true contention can occur in Ruby, specifically around I/O operations and interactions with native extensions/C code that release the GVL. One commenter shared a detailed example of contention in a Rails app due to database connection pooling. Others highlighted that the article might undersell the performance impact of the GVL, particularly for CPU-bound tasks, where true parallelism is impossible. The real takeaway, according to the comments, is to understand the GVL's limitations and choose the right concurrency model (e.g., processes, async I/O) for the specific task, rather than blindly reaching for threads. Finally, a few commenters discussed the complexities of truly removing the GVL from Ruby, citing the challenges and potential breakage of existing code.
Par is a new programming language designed for exploring and understanding concurrency. It features a built-in interactive playground that visualizes program execution, making it easier to grasp complex concurrent behavior. Par's syntax is inspired by Go, emphasizing simplicity and readability. The language utilizes goroutines and channels for concurrency, offering a practical way to learn and experiment with these concepts. While currently focused on concurrency education and experimentation, the project aims to eventually expand into a general-purpose language.
Hacker News users discussed Par's simplicity and suitability for teaching concurrency concepts. Several praised the interactive playground as a valuable tool for visualization and experimentation. Some questioned its practical applications beyond educational purposes, citing limitations compared to established languages like Go. The creator responded to some comments, clarifying design choices and acknowledging potential areas for improvement, such as error handling. There was also a brief discussion about the language's syntax and comparisons to other visual programming tools.
Pyper simplifies concurrent programming in Python by providing an intuitive, decorator-based API. It leverages the power of asyncio without requiring explicit async/await syntax or complex event loop management. By simply decorating functions with @pyper.task, they become concurrently executable tasks. Pyper handles task scheduling and execution transparently, making it easier to write performant, concurrent code without the typical asyncio boilerplate. This approach aims to improve developer productivity and code readability when dealing with concurrency.
Hacker News users generally expressed interest in Pyper, praising its simplified approach to concurrency in Python. Several commenters compared it favorably to existing solutions like multiprocessing and Ray, highlighting its ease of use and seemingly lower overhead. Some questioned its performance characteristics compared to more established libraries, and a few pointed out potential limitations or areas for improvement, such as handling large data transfers between processes and clarifying the licensing situation. The discussion also touched upon potential use cases, including simplifying parallelization in scientific computing. Overall, the reception was positive, with many commenters eager to try Pyper in their own projects.
Summary of Comments ( 147 )
https://news.ycombinator.com/item?id=44003445
Hacker News users generally expressed enthusiasm for the progress of free-threaded Python and the potential benefits of faster Python code execution. Some commenters questioned the practical impact for typical Python workloads, emphasizing that GIL removal mainly benefits CPU-bound multithreaded programs, which are less common than I/O-bound ones. Others discussed the challenges of ensuring backward compatibility and the complexity of the undertaking. Several mentioned the possibility of this development ultimately leading to a Python 4 release, breaking backward compatibility for substantial performance gains. There was also discussion of alternative approaches, like subinterpreters, and comparisons to other languages and their threading models.
The Hacker News post "The first year of free-threaded Python" (linking to a Quansight Labs blog post recapping the first year of the "free-threaded Python" project) generated a moderate number of comments, mostly focusing on the complexities of achieving true parallelism in Python and the nuances of the project's approach.
Several commenters discussed the historical challenges and current state of parallelism in CPython, with mentions of the Global Interpreter Lock (GIL) and its impact on multi-threaded performance. One commenter highlighted the distinction between "free-threaded" and "parallel," emphasizing that eliminating the GIL doesn't automatically guarantee parallel execution due to other potential bottlenecks. They elaborated that true parallelism requires careful consideration of memory management and data structures.
Another commenter pointed out the trade-offs involved in removing the GIL, specifically the potential performance regressions for single-threaded code. They questioned whether the benefits of parallelism would outweigh the costs for the average Python user. This sparked a small thread discussing the target audience for this project, with the suggestion that it's primarily aimed at specific use cases with high parallelism demands, rather than general-purpose Python programming.
One comment expressed skepticism about the practicality of achieving significant performance improvements in Python, referencing previous attempts and the inherent limitations of the language's design. However, another commenter countered this by highlighting the potential of this particular project, suggesting it offers a more promising approach compared to previous efforts.
A few commenters inquired about the compatibility of this project with existing Python code and libraries, expressing concerns about potential breakage. There was also some discussion about alternative approaches to parallelism in Python, such as multiprocessing and asynchronous programming, and how they compare to the "free-threaded" approach.
Finally, some comments simply expressed interest in the project and its potential implications for the future of Python, acknowledging the complexity of the undertaking but recognizing its potential value. Overall, the comments reflect a cautious optimism tempered by an understanding of the long-standing challenges associated with Python parallelism.