Furthering Baseline Core Lucid Standard Specification in the Context of the History of Lucid, Intensional Programming, and Context-Aware Computing

Source: arXiv

ABSTRACT This work is multifold. We review the historical literature on the Lucid
programming language, its dialects, intensional logic, intensional programming,
the implementing systems, and context-oriented and context-aware computing and
so on that provide a contextual framework for the converging Core Lucid
standard programming model. We are designing a standard specification of a
baseline Lucid virtual machine for generic execution of Lucid programs. The
resulting Core Lucid language would inherit the properties of generalization
attempts of GIPL (1999-2013) and TransLucid (2008-2013) for all future and
recent Lucid implementing systems to follow. We also maintain this work across
local research group in order to foster deeper collaboration, maintain a list
of recent and historical bibliography and a reference manual and reading list
for students. We form a (for now informal) SIGLUCID group to keep track of this
standard and historical records with eventual long-term goal through iterative
revisions for this work to become a book or an encyclopedia of the referenced
topics, and perhaps, an RFC. We first begin small with this initial set of

Download full-text


Available from: Joey Paquet, Jun 27, 2015
  • [Show abstract] [Hide abstract]
    ABSTRACT: Multidimensional signal processing applications have a great deal of inherent parallelism. Two examples of these applications---MPEG encoding and computed tomography---can be implemented in parallel. With the described methodology these applications were converted into a form usable in GLU and implemented on a network of workstations. Improvements to the GLU programming environment can benefit other inherently parallel applications. 1. Introduction Multidimensional signal processing applications have a great deal of inherent parallelism. Many spatial signal processing applications are also noncausal and shiftinvariant. These applications use various mathematical transformations performed on well-defined data and have well-structured local and global communication. These properties of multidimensional signal processing applications allow most of these transformations to be calculated concurrently. We describe the parallel implementation of two multidimensional signal processing applic...
  • Source
    SIAM Journal on Computing 09/1976; 5:336-354. DOI:10.1137/0205029 · 0.76 Impact Factor