Open Programming Language Interpreters

Context: This paper presents the concept of open programming language interpreters and the implementation of a framework-level metaobject protocol (MOP) to support them. Inquiry: We address the problem of dynamic interpreter adaptation to tailor the interpreter's behavior on the task to be solved and to introduce new features to fulfill unforeseen requirements. Many languages provide a MOP that to some degree supports reflection. However, MOPs are typically language-specific, their reflective functionality is often restricted, and the adaptation and application logic are often mixed which hardens the understanding and maintenance of the source code. Our system overcomes these limitations. Approach: We designed and implemented a system to support open programming language interpreters. The prototype implementation is integrated in the Neverlang framework. The system exposes the structure, behavior and the runtime state of any Neverlang-based interpreter with the ability to modify it. Knowledge: Our system provides a complete control over interpreter's structure, behavior and its runtime state. The approach is applicable to every Neverlang-based interpreter. Adaptation code can potentially be reused across different language implementations. Grounding: Having a prototype implementation we focused on feasibility evaluation. The paper shows that our approach well addresses problems commonly found in the research literature. We have a demonstrative video and examples that illustrate our approach on dynamic software adaptation, aspect-oriented programming, debugging and context-aware interpreters. Importance: To our knowledge, our paper presents the first reflective approach targeting a general framework for language development. Our system provides full reflective support for free to any Neverlang-based interpreter. We are not aware of any prior application of open implementations to programming language interpreters in the sense defined in this paper. Rather than substituting other approaches, we believe our system can be used as a complementary technique in situations where other approaches present serious limitations

nelli: a lightweight frontend for MLIR

Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific compilers. However, since MLIR models programs at an intermediate level of abstraction, and most extant frontends are at a very high level of abstraction, the semantics and mechanics of the fundamental transformations available in MLIR are difficult to investigate and employ in and of themselves. To address these challenges, we have developed \texttt{nelli}, a lightweight, Python-embedded, domain-specific, language for generating MLIR code. \texttt{nelli} leverages existing MLIR infrastructure to develop Pythonic syntax and semantics for various MLIR features. We describe \texttt{nelli}'s design goals, discuss key details of our implementation, and demonstrate how \texttt{nelli} enables easily defining and lowering compute kernels to diverse hardware platforms

Bridging the Gap between Machine and Language using First-Class Building Blocks

High-performance virtual machines (VMs) are increasingly reused for programming languages for which they were not initially designed. Unfortunately, VMs are usually tailored to specific languages, offer only a very limited interface to running applications, and are closed to extensions. As a consequence, extensions required to support new languages often entail the construction of custom VMs, thus impacting reuse, compatibility and performance. Short of building a custom VM, the language designer has to choose between the expressiveness and the performance of the language. In this dissertation we argue that the best way to open the VM is to eliminate it. We present Pinocchio, a natively compiled Smalltalk, in which we identify and reify three basic building blocks for object-oriented languages. First we define a protocol for message passing similar to calling conventions, independent of the actual message lookup mechanism. The lookup is provided by a self-supporting runtime library written in Smalltalk and compiled to native code. Since it unifies the meta- and base-level we obtain a metaobject protocol (MOP). Then we decouple the language-level manipulation of state from the machine-level implementation by extending the structural reflective model of the language with object layouts, layout scopes and slots. Finally we reify behavior using AST nodes and first-class interpreters separate from the low-level language implementation. We describe the implementations of all three first-class building blocks. For each of the blocks we provide a series of examples illustrating how they enable typical extensions to the runtime, and we provide benchmarks validating the practicality of the approaches

Speculative Staging for Interpreter Optimization

Interpreters have a bad reputation for having lower performance than just-in-time compilers. We present a new way of building high performance interpreters that is particularly effective for executing dynamically typed programming languages. The key idea is to combine speculative staging of optimized interpreter instructions with a novel technique of incrementally and iteratively concerting them at run-time. This paper introduces the concepts behind deriving optimized instructions from existing interpreter instructions---incrementally peeling off layers of complexity. When compiling the interpreter, these optimized derivatives will be compiled along with the original interpreter instructions. Therefore, our technique is portable by construction since it leverages the existing compiler's backend. At run-time we use instruction substitution from the interpreter's original and expensive instructions to optimized instruction derivatives to speed up execution. Our technique unites high performance with the simplicity and portability of interpreters---we report that our optimization makes the CPython interpreter up to more than four times faster, where our interpreter closes the gap between and sometimes even outperforms PyPy's just-in-time compiler.Comment: 16 pages, 4 figures, 3 tables. Uses CPython 3.2.3 and PyPy 1.

Generic Reloading for Languages Based on the Truffle Framework

Programmide käitusaegset uuendamist on põhjalikult uuritud ning selle kasutamine programmeerimiskeelte implementatsioonides kogub hoogu. Senised pakutud lahendused programmide käitusaegse uuendamise osas on rakendatavad ainult konkreetsetele keeltele ja ei ole taaskasutatavad. Käesolevas lõputöös on uuritud seda, kuidas Truffle-nimeline programmeerimiskeelte loomise raamistik suudaks aidata keelte loojatel lisada käitusaegse uuendamise tuge. Autor on loonud taaskasutatava dünaamilise uuendamise lahenduse, mida erinevad Truffle raamistikus loodud keeled saavad kasutada selleks, et vähese vaevaga toetada käitusaegseid uuendusi. Antud lahendusega on võimalik uuendatavaks teha Pythoni, Ruby ja JavasScripti Truffle implementatsioone. Väljatöötatud lahendusel on peaaegu olematu mõju keele tippvõimsusele, kui on sisse lülitatud Truffle täppisajastusega (JIT) kompilaator. See lahendus teeb käitusaegse uuendamise toe lisamise uutele ja tulevastele keeltele märkimisväärselt lihtsamaks.Reloading running programs is a well-researched and increasingly popular feature of programming language implementations. There are plenty of proposed solutions for various existing programming languages, but typically the solutions target a specific language and are not reusable. In this thesis, we explored how the Truffle language implementation framework could aid language creators in adding reloading capabilities to their languages. We created a reusable reloading core that different Truffle-based languages can hook into to support dynamic updates with minimum amount of effort on their part. We demonstrate how the Truffle implementations of Python, Ruby and JavaScript can be made reloadable with the developed solution. With Truffle’s just-in-time compiler enabled, our solution incurs close to zero overhead on steady-state performance. This approach significantly reduces the effort required to add dynamic update support for existing and future languages

Are We There Yet? Simple Language Implementation Techniques for the 21st Century

Research on language implementation techniques has regained importance with the rise of domain-specific languages (DSLs). Although DSLs can help manage a domain’s complexity, building highly optimizing compilers or virtual machines is rarely affordable. So, performance remains an issue. Ideally, you would implement a simple interpreter and still be able to achieve acceptable performance. RPython and Truffle are implementation techniques based on simple interpreters; they promise to perform at the same order of magnitude as highly optimizing virtual machines. This case study compares the two techniques to identify their similarities, weaknesses, and areas for further research

Zero-Overhead Metaprogramming: Reflection and Metaobject Protocols Fast and without Compromises

Runtime metaprogramming enables many useful applications and is often a convenient solution to solve problems in a generic way, which makes it widely used in frameworks, middleware, and domain-specific languages. However, powerful metaobject protocols are rarely supported and even common concepts such as reflective method invocation or dynamic proxies are not optimized. Solutions proposed in literature either restrict the metaprogramming capabilities or require application or library developers to apply performance improving techniques. For overhead-free runtime metaprogramming, we demonstrate that dispatch chains, a generalized form of polymorphic inline caches common to self-optimizing interpreters, are a simple optimization at the language-implementation level. Our evaluation with self-optimizing interpreters shows that unrestricted metaobject protocols can be realized for the first time without runtime overhead, and that this optimization is applicable for just-in-time compilation of interpreters based on meta-tracing as well as partial evaluation. In this context, we also demonstrate that optimizing common reflective operations can lead to significant performance improvements for existing applications