Stories with Tag CRuby

• Tiny JITs for a Faster FFI

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  Posted: 2025-02-12 22:20:19

  Verbose tl;dr

  This post explores optimizing Ruby's Foreign Function Interface (FFI) performance by using tiny Just-In-Time (JIT) compilers. The author demonstrates how generating specialized machine code for specific FFI calls can drastically reduce overhead compared to the generic FFI invocation process. They present a proof-of-concept implementation using Rust and inline assembly, showcasing significant speed improvements, especially for repeated calls with the same argument types. While acknowledging limitations and areas for future development, like handling different calling conventions and more complex types, the post concludes that tiny JITs offer a promising path toward a much faster Ruby FFI.

  The blog post "Tiny JITs for a Faster FFI" on Rails at Scale explores the performance challenges of Foreign Function Interfaces (FFIs) and introduces a novel approach using tiny Just-In-Time (JIT) compilers to mitigate these overheads. The author begins by establishing the context of FFIs, describing their role in bridging the gap between different programming languages, specifically highlighting their importance within Ruby on Rails applications for interacting with native extensions often written in C. They emphasize that FFIs are essential for leveraging performance-critical libraries and functionalities not readily available within Ruby's ecosystem.

  The core performance bottleneck with FFIs lies in the "marshaling" process, which involves converting data between the representations used by the two interacting languages. This conversion process can be computationally expensive, especially when dealing with complex data structures or frequent calls across the FFI boundary. The traditional approach to mitigating this overhead involves manually writing specialized C "shim" functions tailored for specific data types and operations. This manual optimization, however, is labor-intensive, error-prone, and difficult to maintain, especially as the complexity of the interaction grows.

  The post proposes a more automated and flexible solution: employing small, specialized JIT compilers to generate these conversion routines dynamically. These "tiny JITs" analyze the required data transformations at runtime and generate optimized machine code specifically designed for the task at hand. This eliminates the need for hand-written shims and allows for more efficient data marshaling. The authors explain their chosen implementation strategy using Rust and its procedural macro capabilities. They leverage Rust's powerful metaprogramming features to generate the necessary code at compile time, resulting in a more performant and maintainable solution.

  The post then delves into the practical application of this approach within the context of a Ruby gem named "fruity". Fruity uses this tiny JIT technique to optimize calls to C functions, demonstrating significant performance improvements in benchmark comparisons against traditional FFI methods. The authors provide concrete examples and performance data to showcase the effectiveness of their approach, emphasizing the substantial reduction in overhead achieved through JIT-generated conversion routines. They also highlight the portability of this technique, mentioning its potential applicability to other language combinations beyond Ruby and C.

  Finally, the post concludes by acknowledging the ongoing nature of the project and outlining future directions for research and development. This includes further exploration of potential optimizations, expanding support for more complex data structures and operations, and investigating the integration of this technique within other FFI frameworks. The authors express optimism about the potential of tiny JITs to significantly improve the performance and usability of FFIs in various programming environments.

  • JIT compilation
  • FFI
  • Foreign Function Interface
  • performance optimization
  • ruby
  • CRuby
  • YJIT
  • MJIT
  • interpreter
  • compiler
  • native code
  • C extensions
  • Dynamic languages
  • Runtime performance

  Summary of Comments ( 109 )
  https://news.ycombinator.com/item?id=43030388

  Verbose tl;dr

  The Hacker News comments on "Tiny JITs for a Faster FFI" express skepticism about the practicality of tiny JITs in real-world scenarios. Several commenters question the performance gains, citing the overhead of the JIT itself and the potential for optimization by the host language's runtime. They argue that a well-optimized native library, or even careful use of the host language's FFI, could often outperform a tiny JIT. One commenter notes the difficulties of debugging and maintaining such a system, and another raises security concerns related to executing untrusted code. The overall sentiment leans towards established optimization techniques rather than introducing a new layer of complexity with a tiny JIT.

  The Hacker News post "Tiny JITs for a Faster FFI" has generated a moderate discussion with several interesting comments. Many of the comments revolve around the trade-offs and nuances of using Just-In-Time (JIT) compilation for Foreign Function Interfaces (FFIs).

  One commenter points out the performance benefits observed when using a simple JIT for Lua's FFI, highlighting a significant speedup. They further discuss the inherent costs associated with traditional FFIs, such as argument marshaling and context switching, which a JIT can mitigate. The commenter's experience adds practical weight to the article's premise.

  Another comment thread delves into the complexities of implementing a truly portable JIT given the variations in Application Binary Interfaces (ABIs) across different operating systems and architectures. This discussion highlights the challenge of creating a "tiny" and efficient JIT compiler that remains universally applicable. One participant suggests focusing on specific, commonly used platforms initially to simplify the development process.

  A separate commenter mentions the potential security implications of JIT compilation, particularly in scenarios involving untrusted code. They emphasize the need for careful consideration of security risks when incorporating JIT techniques into an FFI, especially when dealing with external libraries or user-provided code. This comment serves as a valuable reminder of the security considerations associated with dynamic code generation.

  Another comment discusses the existing use of small JITs in various projects like WebKit, suggesting that the concept presented in the article is not entirely novel. They link to a relevant talk about a register-based virtual machine with a JIT compiler used for JavaScriptCore, providing further context for those interested in existing implementations.

  Some comments briefly touch upon alternative approaches to optimizing FFIs, such as using code generation during build time or employing specialized libraries. While these suggestions are not explored in detail, they offer additional perspectives on addressing FFI performance bottlenecks.

  Finally, one comment questions the necessity of a JIT compiler in some cases, arguing that careful optimization of the FFI itself can often achieve comparable performance gains without the complexity of dynamic code generation. This counterpoint adds balance to the discussion and encourages consideration of alternative optimization strategies.

  Overall, the comments on Hacker News provide valuable insights into the potential benefits, challenges, and trade-offs associated with using tiny JIT compilers for FFIs. They expand upon the article's core ideas by exploring practical experiences, security considerations, existing implementations, and alternative optimization techniques.

• Ruby “Thread Contention” Is Simply GVL Queuing

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  Posted: 2025-02-03 08:43:13

  Verbose tl;dr

  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.

  The blog post "Ruby “Thread Contention” Is Simply GVL Queuing" by Peter Cooper argues that the common phrase "thread contention" is often misused in the context of Ruby's multi-threading performance limitations, leading to confusion and misdiagnosis of performance issues. Instead of actual contention, where multiple threads are actively competing for the same shared resource simultaneously, the performance bottleneck in Ruby's multi-threaded applications typically stems from the Global Virtual Machine Lock (GVL).

  Cooper elaborates that the GVL, a core mechanism in the standard implementation of Ruby known as CRuby, serializes execution of Ruby code. While multiple operating system (OS) threads can exist within a Ruby process, only one Ruby thread can execute Ruby bytecode at any given instant due to the GVL. Other threads wishing to execute Ruby code are placed in a queue managed by the GVL. This queuing mechanism is what frequently gets misinterpreted as "thread contention."

  The author meticulously distinguishes between true contention and GVL queuing. True contention occurs when multiple threads attempt to access and modify the same shared resource, such as a shared memory location or a file, simultaneously. This leads to race conditions and requires synchronization primitives like mutexes or semaphores to ensure data integrity and prevent unexpected behavior. However, in Ruby, the GVL inherently prevents true contention within Ruby code itself because it restricts execution to a single thread at a time.

  Therefore, what appears as "contention" in Ruby profiling tools is actually the overhead of the GVL's queuing mechanism. Threads wait their turn to acquire the GVL, creating a queue of waiting threads. This queuing and dequeuing process, along with the context switching between threads managed by the operating system, contributes to the perceived performance bottleneck. Cooper emphasizes that this is not true contention for resources, but rather a queuing delay imposed by the GVL's serialization of Ruby code execution.

  The post further clarifies that true contention can still occur in Ruby in scenarios involving non-Ruby code, particularly when interacting with external libraries or system calls. For example, if multiple Ruby threads simultaneously attempt to write to the same file descriptor, true contention can occur at the operating system level, outside the scope of the GVL.

  In summary, Cooper advocates for more precise language when discussing Ruby's multi-threading performance. He argues that using the term "GVL queuing" instead of "thread contention" provides a more accurate description of the performance bottleneck in CRuby, highlighting the role of the GVL and its queuing mechanism as the primary source of performance limitations in multi-threaded Ruby applications and facilitating more effective diagnosis and optimization of such applications.

  • ruby
  • threads
  • GVL
  • Global Interpreter Lock
  • concurrency
  • Parallelism
  • performance
  • Queuing
  • programming
  • Software Development
  • CRuby
  • MRI
  • Multithreading

  Summary of Comments ( 38 )
  https://news.ycombinator.com/item?id=42916203

  Verbose tl;dr

  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.

  The Hacker News post titled "Ruby “Thread Contention” Is Simply GVL Queuing" has generated several comments discussing the nuances of Ruby's Global VM Lock (GVL) and its impact on concurrency.

  One commenter points out the distinction between "true contention" and mere queuing for the GVL. They argue that while multiple threads might appear to be contending for resources, the actual bottleneck is often the serialized execution enforced by the GVL. This commenter further emphasizes that profiling tools might misrepresent this queuing as contention, leading developers to misdiagnose performance issues. They suggest that a more accurate term would be "GVL contention" or "GVL queuing" to reflect the underlying mechanism.

  Another commenter concurs, adding that while the GVL doesn't eliminate all forms of contention (e.g., contention for shared memory), it does significantly influence how threads interact with resources. They highlight the importance of understanding this distinction when optimizing Ruby code for multi-threaded environments.

  A further comment delves into the complexities of the GVL's implementation, noting that its behavior can vary across different Ruby interpreters (e.g., MRI, JRuby, TruffleRuby). This commenter emphasizes the need to consider the specific interpreter when analyzing GVL-related performance characteristics. They also mention the potential benefits and drawbacks of using alternative concurrency models, such as fibers and actors, in Ruby.

  Another discussion thread focuses on the practical implications of the GVL for Ruby developers. Commenters share their experiences with debugging and optimizing multi-threaded Ruby applications, offering advice on how to mitigate the performance limitations imposed by the GVL. Specific techniques, such as using asynchronous I/O operations and carefully managing shared resources, are discussed.

  One commenter offers a contrasting perspective, arguing that the term "thread contention" is still relevant in the context of the GVL. They explain that even though the GVL serializes execution, threads are still competing for the opportunity to acquire the lock. This competition, they contend, can still be considered a form of contention, albeit one mediated by the GVL.

  Overall, the comments on the Hacker News post provide a rich discussion on the intricacies of the GVL in Ruby. They highlight the importance of understanding the GVL's impact on concurrency, the potential for misinterpreting profiling data, and the strategies developers can employ to optimize their multi-threaded Ruby code. The comments also reveal the ongoing debate about the appropriate terminology for describing the GVL's effects on thread behavior.