16,736 research outputs found
Specializing Interpreters using Offline Partial Deduction
We present the latest version of the Logen partial evaluation system for logic programs. In particular we present new binding-types, and show how they can be used to effectively specialise a wide variety of interpreters.We show how to achieve Jones-optimality in a systematic way for several interpreters. Finally, we present and specialise a non-trivial interpreter for a small functional programming language. Experimental results are also presented, highlighting that the Logen system can be a good basis for generating compilers for high-level languages
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
Approaches to Interpreter Composition
In this paper, we compose six different Python and Prolog VMs into 4 pairwise
compositions: one using C interpreters; one running on the JVM; one using
meta-tracing interpreters; and one using a C interpreter and a meta-tracing
interpreter. We show that programs that cross the language barrier frequently
execute faster in a meta-tracing composition, and that meta-tracing imposes a
significantly lower overhead on composed programs relative to mono-language
programs.Comment: 33 pages, 1 figure, 9 table
Query Evaluation in Deductive Databases
It is desirable to answer queries posed to deductive databases by computing fixpoints because such computations are directly amenable to set-oriented fact processing. However, the classical fixpoint procedures based on bottom-up processing â the naive and semi-naive methods â are rather primitive and often inefficient. In this article, we rely on bottom-up meta-interpretation for formalizing a new fixpoint procedure that performs a different kind of reasoning: We specify a top-down query answering method, which we call the Backward Fixpoint Procedure. Then, we reconsider query evaluation methods for recursive databases. First, we show that the methods based on rewriting on the one hand, and the methods based on resolution on the other hand, implement the Backward Fixpoint Procedure. Second, we interpret the rewritings of the Alexander and Magic Set methods as specializations of the Backward Fixpoint Procedure. Finally, we argue that such a rewriting is also needed in a database context for implementing efficiently the resolution-based methods. Thus, the methods based on rewriting and the methods based on resolution implement the same top-down evaluation of the original database rules by means of auxiliary rules processed bottom-up
Fine-grained Language Composition: A Case Study
Although run-time language composition is common, it normally takes the form
of a crude Foreign Function Interface (FFI). While useful, such compositions
tend to be coarse-grained and slow. In this paper we introduce a novel
fine-grained syntactic composition of PHP and Python which allows users to
embed each language inside the other, including referencing variables across
languages. This composition raises novel design and implementation challenges.
We show that good solutions can be found to the design challenges; and that the
resulting implementation imposes an acceptable performance overhead of, at
most, 2.6x.Comment: 27 pages, 4 tables, 5 figure
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Conjoint Behavioral Consultation with Spanish-Speaking Families
Conjoint Behavioral Consultation (CBC) is a strengths-based, problem-solving, decision-making model of service delivery that involves parents and teachers working collaboratively to promote positive outcomes related to a studentâs development (Sheridan & Kratochwill, 2008). Research has indicated that CBC is an acceptable and effective model. However, very little research has been done to assess the effectiveness of CBC with minority clients, and even less work has been done that focuses on Spanish-speaking Latinx families. Additionally, the research that investigates consultation with minority clients who have limited English skills often involves interpreters. The purpose of this study is to extend the CBC literature to working with Spanish-speaking Latinx families. The present study will investigate several possible outcomes for implementing empirically supported interventions within the context of CBC for children or adolescents exhibiting externalizing behavior concerns in the school and home setting. In particular, there is interest in the extent to which conducting CBC using a Spanish-speaking consultant will result in positive outcomes on various measures for all participants
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.
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