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Okay, hello and welcome to my talk.
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Today, we are going to discuss interpreting C extensions and then turning them into compiling.
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My name is Benoit Daloze. I work at Oracle Labs in Zurich and have been involved in Truffle Ruby since 2014.
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I hold a PhD in Dynamic Languages and I am the maintainer of Ruby spec and set up Ruby. I am also a committer for Truffle Ruby.
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Truffle Ruby is a high-performance Ruby implementation that uses the Graal JIT compiler to target full compatibility with C 3.2, including C extensions.
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As a point of reference, applications like Mastodon and Discourse can run on Truffle Ruby due to the high level of compatibility.
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You can find Truffle Ruby on GitHub and so on.
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Now, let's look into why Ruby has C extensions in the first place. There are two main reasons for this.
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The first reason is to bind to existing C libraries. For instance, libraries like OpenSSL, MySQL, and PG can all be linked via corresponding native libraries.
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That's a common reason for using C extensions. The alternative, which is typical, is to use the FFI gem. However, only a small proportion of gems utilize the FFI gem.
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Furthermore, the FFI gem struggles when libraries have numerous macros in their headers, as one would need to replicate extensive logic in Ruby code.
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The second purpose of C extensions is to improve performance. Sometimes Ruby code can be too slow.
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For instance, both JSON and MessagePack have C extensions to speed up serialization. However, this practice has become less encouraged over the years.
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As discussed in Maxim's talk, it's seen as less necessary now, and Truffle Ruby executes Ruby code so swiftly that writing a C extension may not have significant advantages.
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Moreover, C extensions usually require higher maintenance.
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So far, I have talked about C extensions, but what I really mean is native extensions, as they are not limited to C; you can also use C++ or Rust and other languages.
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If we look at usage, we can count among the top 10,000 gems by the number of downloads and see how many of them actually use a C extension.
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Only 4% of these gems have a C extension, which may indicate that C extensions are not that crucial for compatibility. However, if we account for gems that have a transitive dependency on a gem with a native extension, the percentage increases significantly.
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For the top 10,000 gems, it's 46% that depend transitively on a C extension.
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It's important to note that we don't count FFI in this statistic, as FFI is also treated as a C extension, but in other Ruby implementations, it isn't.
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We can also be more precise; for instance, the JSON and Rack gems have a probable fallback, allowing them to work even if the extension is not supported.
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Even with this, it still amounts to 43%, meaning that a large portion of the Ruby ecosystem relies on some extensions, making it imperative to support them for compatibility.
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Now, for JRuby, it's a bit more complicated due to Java extensions, and I will not dive into that detail here.
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If we examine the compatibility, of course, C defines the specs, ensuring compliance, while Truffle Ruby passes about 98% of the CPI specs.
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This level of compatibility is commendable. However, JRuby does not implement C extensions—at least not in the current version—so it does not pass these tests.
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Next, I'd like to delve into how we can implement C extensions.
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For the past ten years, we have been keen on implementing these extensions as they are crucial for compatibility. We have tried various approaches, each with its own set of pros and cons.
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As a bit of background, let me explain what Truffle is. Truffle is a framework designed to facilitate the creation of high-performance languages.
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The main advantage of writing an interpreter with the Truffle framework is that you essentially get a JIT compiler for free. This works because the Graal VM compiler, a language-agnostic JIT compiler, can partially evaluate any language's bytecode and generate efficient machine code from it.
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For example, suppose I have a Ruby method or block. The JIT compiler, which predominantly understands Java, interacts with the Truffle Ruby interpreter to identify operations, such as a plus operation, and JIT compiles to machine code.
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The end result is machine code optimized for that specific Ruby method or block. Essentially, it is akin to having written a JIT compiler specifically for Ruby, but without the overhead of doing so.
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This dual functionality of not needing to duplicate logic between the interpreter and the JIT compiler is a common pain point that many virtual machines suffer, leading to bugs and performance issues.
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The journey began in 2014 with an initial prototype, utilizing Truffle C. Truffle C, as its name suggests, is a Truffle interpreter for the C language.
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This approach was quite innovative and unusual, as C isn't conventionally viewed as an interpreted language.
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You might wonder why we took this route instead of simply using Clang.
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I will answer this question in more detail, but in summary, we aim for more freedom of representation. For instance, we can represent a C struct as a Ruby object.
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This seems counterintuitive, as a C struct is merely a layout in memory, while a Ruby object is more complex. However, this approach enables us to overcome limitations.
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Moreover, surprisingly, performance benefits can arise because a Graal VM JIT compiler can compile both C and Ruby together.
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Thus, it can inline functions across both languages. When we compile code, we can optimize a Ruby method that calls a C function, which subsequently calls another Ruby method.
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Inlining is crucial for performance, as shown by the early statistics, which revealed that calling C extensions with inlining led to a significant performance gain.
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When C calls Ruby, it often results in slowdowns; however, inlining mitigates this problem.
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Two years later, we see the inception of Sulong, a successor to Truffle C that allows for passing C code more efficiently. Instead of passing C directly, Sulong employs LLVM bitcode, which serves as an intermediate representation.
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This approach is faster than typical C passing, as it allows us to compile C to bitcode ahead of time with Clang. Sulong interprets this bitcode, allowing for efficient runtime behavior.
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Now, transitioning to native extension scenarios, we initially had challenges with representing structures directly.
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In C, Ruby headers define value types commonly as unsigned long. This isn't convenient, as objects in Truffle Ruby, represented as Java objects, cannot have direct memory addresses.
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Java's garbage collector can move these objects at any time, so obtaining valid addresses isn’t practical, as any address you might obtain could be invalid.
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Sulong's solution provides a method for managing representations creatively. For example, when defining a method in a C extension taking a Ruby object, we manipulate object fields directly without converting these objects to integers.
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Let’s consider redirecting access to a C struct, where commonly we would use Ruby macros to reference Ruby objects directly.
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Instead of reading memory addresses, our implementations can return Ruby objects directly, streamlining the interaction.
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Instead of adhering to traditional memory access patterns, we facilitate interoperability, allowing for Ruby and C exchanges while maintaining efficiency.
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However, when working with inter-library calls, unexpected leakage of Ruby objects into native memory can complicate matters.
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For instance, when interfacing with libraries like libuv, Ruby objects could be passed around leading to further complications.
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Inspired by these observations, we introduced handles, which allow us to associate native pointers with Ruby objects and prevent any unnecessary overhead.
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While handles facilitate this representation change, we aim to minimize their use to situations where they are absolutely necessary.
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This year, I made significant changes to Truffle Ruby, allowing it to handle extensions natively, moving away from reliance on Sulong.
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This transition provides various advantages, including faster startup and warm-up times, eliminating the costly JIT compilation step.
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By leveraging native compilation tools like Clang or GCC, we foster more trustworthy code execution.
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With direct compilation, large extensions such as gRPC, which often utilize advanced C++ features and edge cases, now become feasible, showcasing our resolve.
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That said, this new approach has drawbacks; we no longer have the flexibility to perform certain optimizations or inline caches.
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For instance, every variable must adhere to type specifications in C, and we cannot afford the same level of dynamic behavior previously enjoyed.
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Similarly, the constraints of inline caches—previously beneficial for performance—must now be adapted to suit native specifications.
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In focusing on structures, we needed to rethink how to manage specific Ruby C structs. For instance, instead of relying on RBASIC, which would have required instantiating traditional C structures, we applied macros to interface directly with desired Ruby classes.
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One such example arises with IO objects, where we also construct a pointing mechanism to facilitate access to the relevant C fields.
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Moreover, the challenge involves bulk structures, like RB Encoding, part of Ruby's core, demanding specialized consideration during implementation.
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Additionally, the use of inline caches has resurfaced as a notable tactic traditionally used in the Ruby environment.
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Despite needing a redesign, this leveraging of caches along with the existing C methodology aligns neatly with optimal performance operational principles.
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In summary, we have seen the evolution from interpreting and JIT compiling C code to running everything natively through a customizable toolchain.
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This enables extensions to be utilized effectively while preserving valuable instructions found within the standard libraries.
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While the extension API still has its pitfalls—it’s immensely better than the CPython API, which exposes a significant number of structures.
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Many developers realize that mutable design choices can contribute to stunted progress and performance.
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Therefore, ongoing research continues to explore strategies that combine the intuitiveness of C extensions with the flexibility of Ruby.
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In conclusion, around 43% of the top 10,000 gems are dependent on gems with native extensions, underscoring the importance of supporting them for compatibility.
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The Ruby extension API is flexible and can be implemented by alternative Ruby implementations.
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Truffle Ruby initially approached it by JIT compiling C extensions, which provided many advantages.
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We have now transitioned to running these natively, which yields different yet significant advantages.
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Overall, the challenges faced by C extensions revolve around managing memory representation effectively.
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With advancements in handling functional macros, we can refine and improve efficiencies further.
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If any of you would like to experiment with Truffle Ruby, recent releases are available along with early access development builds.
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Thank you for listening!
