Scientists, engineers, and statisticians must execute domain-specific application programs many times on large collections of file-based data. This activity requires complex orchestration and data management as data is passed to, from, and among application invocations. Distributed and parallel computing resources can accelerate such processing, but their use further increases programming compl...

The NPAC kernel runtime, developed in the PCRC (Parallel Compiler Runtime Consortium) project, is a runtime library with special support for the High Performance Fortran data model. It provides array descriptors for a generalized class of HPF-like distributed arrays, support for parallel access to their elements, and a rich library of collective communication and arithmetic operations for manip...

Parallel programming is increasingly important with the advent of multicore processors. However, modern software is difficult to parallelize because of the high degree of modularization. It is unclear whether a piece of code is parallel if it calls other functions. Dynamic languages such as Ruby, Python, and Matlab represent modularization to the extreme. A program, also known as a script, requ...

Communicating Sequential Processes (CSP) [?] provide a powerful framework for the description and analysis of parallel programs. The notion has been used as the underlying model of Occam [?], an elegant language designed by D. May. However, programmers sometimes consider this language as too frugal: there are only limited data structuring mechanisms, procedures can not be recursive, and the imp...

In this paper, we propose to extend the Peril-L parallel pseudo-code language. The extensions added to the Peril-L are essential to express non-trivial parallel algorithms. We demonstrate the effectiveness of the extended Peril-L by showing the parallel algorithms for the Jacobi method for solving differential equations and the dynamic programming method for finding the optimal binary search tree.

Probably building non procedural languages is the most prospective way for parallel programming just because non procedural means no fixed way for execution. The article consists of 3 parts. In first part we consider formal systems for definition a named datasets and studying an expression power of different subclasses. In the second part we consider a complexity of algorithms of building sets ...

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. Current approaches to program GPUs are still relatively low-level programming models such as Compute Unified Device Architecture (CUDA), a programming model from NVIDIA, and Open Compute Language (OpenCL), created by Apple in cooperation with others. These two programming m...

This paper introduces VCODE, an intermediate language for data-parallel computations. VCODE is designed to allow easy porting of data-parallel languages, such as C*, PARALATION LISP, and Fortran 8x, to a wide class of parallel machines. It is designed with the joint goals of being simple, expressive, and efficiently implementable. It contains about 50 instructions, most of which manipulate arbi...

The use of logic as a programming tool has achieved a significant impact in software engineering, especially in the early stages of the software development process to support fast prototyping and early verification of costumers requirements. Logic programming languages allow the programmer to encode the costumer needs into logic programs which intuitively (declaratively) represent the requirem...

In this paper we introduce a word clustering algorithm which uses a bilingual, parallel corpus to group together words in the source and target language. Our method generalizes previous mutual information clustering algorithms for monolingual data by incorporating a statistical translation model. Preliminary experiments have shown that the algorithm can e ectively employ the constraints implici...