Concurrent execution system for action languages

Author(s):  
Antti Jääskeläinen ◽  
Hannu-Matti Järvinen ◽  
Mikko Tiusanen
Author(s):  
Md Rubel Ahmed ◽  
Hao Zheng ◽  
Parijat Mukherjee ◽  
Mahesh C. Ketkar ◽  
Jin Yang

2015 ◽  
Vol 16 (2) ◽  
pp. 189-235 ◽  
Author(s):  
DANIELA INCLEZAN ◽  
MICHAEL GELFOND

AbstractThe paper introduces a new modular action language,${\mathcal ALM}$, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993,Journal of Logic Programming 17, 2–4, 301–321; 1998,Electronic Transactions on AI 3, 16, 193–210) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that requirestructuring of knowledge.$\mathcal{ALM}$is meant to remedy this problem. Structuring of knowledge in${\mathcal ALM}$is supported by the concepts ofmodule(a formal description of a specific piece of knowledge packaged as a unit),module hierarchy, andlibrary, and by the division of a system description of${\mathcal ALM}$into two parts:theoryandstructure. Atheoryconsists of one or more modules with a common theme, possibly organized into a module hierarchy based on adependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them.Structuresare used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.


2015 ◽  
Vol 30 (4) ◽  
pp. 899-922 ◽  
Author(s):  
Joseph Babb ◽  
Joohyung Lee

Abstract Action languages are formal models of parts of natural language that are designed to describe effects of actions. Many of these languages can be viewed as high-level notations of answer set programs structured to represent transition systems. However, the form of answer set programs considered in the earlier work is quite limited in comparison with the modern Answer Set Programming (ASP) language, which allows several useful constructs for knowledge representation, such as choice rules, aggregates and abstract constraint atoms. We propose a new action language called BC +, which closes the gap between action languages and the modern ASP language. The main idea is to define the semantics of BC + in terms of general stable model semantics for propositional formulas, under which many modern ASP language constructs can be identified with shorthands for propositional formulas. Language BC  + turns out to be sufficiently expressive to encompass the best features of other action languages, such as languages B , C , C + and BC . Computational methods available in ASP solvers are readily applicable to compute BC +, which led to an implementation of the language by extending system cplus2asp .


2017 ◽  
Author(s):  
Rommel Cruz ◽  
Lucia Drummond ◽  
Esteban Clua ◽  
Cristiana Bentes

GPUs have established a new baseline for power efficiency and computing power, delivering larger bandwidth and more computing units in each new generation. Modern GPUs support the concurrent execution of kernels to maximize resource utilization, allowing other kernels to better exploit idle resources. However, the decision on the simultaneous execution of different kernels is made by the hardware, and sometimes GPUs do not allow the execution of blocks from other kernels, even with the availability of resources. In this work, we present an in-depth study on the simultaneous execution of kernels on the GPU. We present the necessary conditions for executing kernels simultaneously, we define the factors that influence competition, and describe a model that can determine performance degradation. Finally, we validate the model using synthetic and real-world kernels with different computation and memory requirements.


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