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Added by
Piotr Danilewski
Publication date
2016
fields
Informatics Engineering
and research's language is
English
Authors
Piotr Danilewski
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Domain-specific languages are becoming increasingly important. Almost every application touches multiple domains. But how to define, use, and combine multiple DSLs within the same application? The most common approach is to split the project along the domain boundaries into multiple pieces and files. Each file is then compiled separately. Alternatively, multiple languages can be embedded in a flexible host language: within the same syntax a new domain semantic is provided. In this paper we follow a less explored route of metamorphic languages. These languages are able to modify their own syntax and semantics on the fly, thus becoming a more flexible host for DSLs. Our language allows for dynamic creation of grammars and switching languages where needed. We achieve this through a novel concept of Syntax-Directed Execution. A language grammar includes semantic actions that are pieces of functional code executed immediately during parsing. By avoiding additional intermediate representation, connecting actions from different languages and domains is greatly simplified. Still, actions can generate highly specialized code though lambda encapsulation and Dynamic Staging.
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Object-oriented scripting languages such as JavaScript or Python gain in popularity due to their flexibility. Still, the growing code bases written in the languages call for methods that make possible to automatically control the properties of the programs that ensure their stability in the running time. We propose a type system, called Lucretia, that makes possible to control the object structure of languages with reflection. Subject reduction and soundness of the type system with respect to the semantics of the language is proved.
A standard informal method for analyzing the asymptotic complexity of a program is to extract a recurrence that describes its cost in terms of the size of its input, and then to compute a closed-form upper bound on that recurrence. We give a formal account of that method for functional programs in a higher-order language with let-polymorphism The method consists of two phases. In the first phase, a monadic translation is performed to extract a cost-annotated version of the original program. In the second phase, the extracted program is interpreted in a model. The key feature of this second phase is that different models describe different notions of size. This plays out specifically for values of inductive type, where different notions of size may be appropriate depending on the analysis, and for polymorphic functions, where we show that the notion of size for a polymorphic function can be described formally as the data that is common to the notions of size of its instances. We give several examples of different models that formally justify various informal cost analyses to show the applicability of our approach.
This paper describes our experiences creating Tornado: a practical and efficient heterogeneous programming framework for managed languages. The novel aspect of Tornado is that it turns the programming of heterogeneous systems from an activity predominantly based on a priori knowledge into one based on a posteriori knowledge. Alternatively put, it simply means developers do not need to overcomplicate their code by catering for all possible eventualities. Instead, Tornado provides the ability to specialize each application for a specific system in situ which avoids the need for it to be pre-configured by the developer. To enable this, Tornado employs a sophisticated runtime system that can dynamically configure all aspects of the application - from selecting which parallelization scheme to apply to specifying which accelerators to use. By using this ability, the end-user, and not the developer, can transparently make use of any available multi-/many-core processor or hardware accelerator. To showcase the impact of Tornado, we implement a real-world computer vision application and deploy it across nine accelerators without having to modify the source code or even explicitly re-compile the application. Using dynamic configuration, we show that our implementation can achieve up to 124 frames per second (FPS) - up to 166x speedup over the reference implementation. Finally, our implementation is always within 21% of a hand-written OpenCL version but avoids much of the programming tedium.
There are numerous types of programming languages developed in the last decades, and most of them provide interface to call C++ or C for high efficiency implementation. The motivation of Svar is to design an efficient, light-weighted and general middle-ware for multiple languages, meanwhile, brings the dynamism features from script language to C++ in a straightforward way. Firstly, a Svar class with JSON like data structure is designed to hold everything exists in C++, including basic values, functions or user defined classes and objects. Secondly, arguments are auto cast to and from Svar efficiently with compile time pointers, references and shared_ptr detection. Thirdly, classes and functions are binded with string names to support reflection, this means all functions and classes in a shared library can be exported to a Svar object, which also calls a Svar module. The Svar modules can be accessed by different languages and this paper demonstrates how to import and use a Svar module in Python and Node.js. Moreover, the Svar modules or even a python module can also be imported by C++ at runtime, which makes C++ easier to compile and use since headers are not required anymore. We compare the performance of Svar with two state-of-the-art binding tool for Python and Node.js, and the result demonstrates that Svar is efficient, elegant and general. The core of this project is one single tiny modern C++ header with less than 5000 lines code without extra dependency. To help developers using Svar, all the source codes related are public available on http://github.com/zdzhaoyong/Svar, including documentations and benchmarks.
XML database query languages such as XQuery employ regular expression types with structural subtyping. Subtyping systems typically have two presentations, which should be equivalent: a declarative version in which the subsumption rule may be used anywhere, and an algorithmic version in which the use of subsumption is limited in order to make typechecking syntax-directed and decidable. However, the XQuery standard type system circumvents this issue by using imprecise typing rules for iteration constructs and defining only algorithmic typechecking, and another extant proposal provides more precise types for iteration constructs but ignores subtyping. In this paper, we consider a core XQuery-like language with a subsumption rule and prove the completeness of algorithmic typechecking; this is straightforward for XQuery proper but requires some care in the presence of more precise iteration typing disciplines. We extend this result to an XML update language we have introduced in earlier work.
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