MaTX: a high-performance programming language (interpreter and compiler) for scientific and engineering computation

Author(s):  
M. Koga ◽  
K. Furuta
2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Stergios Papadimitriou ◽  
Kirsten Schwark ◽  
Seferina Mavroudi ◽  
Kostas Theofilatos ◽  
Spiridon Likothanasis

ScalaLab and GroovyLab are both MATLAB-like environments for the Java Virtual Machine. ScalaLab is based on the Scala programming language and GroovyLab is based on the Groovy programming language. They present similar user interfaces and functionality to the user. They also share the same set of Java scientific libraries and of native code libraries. From the programmer's point of view though, they have significant differences. This paper compares some aspects of the two environments and highlights some of the strengths and weaknesses of Scala versus Groovy for scientific computing. The discussion also examines some aspects of the dilemma of using dynamic typing versus static typing for scientific programming. The performance of the Java platform is continuously improved at a fast pace. Today Java can effectively support demanding high-performance computing and scales well on multicore platforms. Thus, both systems can challenge the performance of the traditional C/C++/Fortran scientific code with an easier to use and more productive programming environment.


2002 ◽  
Vol 12 (02) ◽  
pp. 193-210 ◽  
Author(s):  
CHRISTOPH A. HERRMANN ◽  
CHRISTIAN LENGAUER

Metaprogramming is a paradigm for enhancing a general-purpose programming language with features catering for a special-purpose application domain, without a need for a reimplementation of the language. In a staged compilation, the special-purpose features are translated and optimised by a domain-specific preprocessor, which hands over to the general-purpose compiler for translation of the domain-independent part of the program. The domain we work in is high-performance parallel computing. We use metaprogramming to enhance the functional language Haskell with features for the efficient, parallel implementation of certain computational patterns, called skeletons.


2013 ◽  
Vol 5 (3) ◽  
pp. 177-188
Author(s):  
Meredith Farkas ◽  
Lisa Hinchliffe

In an environment in which libraries increasingly need to demonstrate their value to faculty and administrators, providing evidence of the library’s contribution to student learning through its instruction program is critical. However, building a culture of assessment can be a challenge, even if librarians recognize its importance. In order to lead change, coordinators of library instruction at institutions where librarians are also tenure-track faculty must build trust and collaboration, lead through influence, and garner support from administration for assessment initiatives. The purpose of this paper is to explore what it takes to build a culture of assessment in academic libraries where librarians are faculty through the High Performance Programming model of organizational change. The guidelines for building a culture of assessment will be exemplified by case studies at the authors’ libraries where instruction coordinators are using collaboration to build a culture of assessment with their colleagues.


Background/Objectives: In the field of software development, the diversity of programming languages increases dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability to detect the pattern of a programming language characteristic by using NeMo(High-performance spiking neural network simulator) of neural network and testing the ability of this toolkit to provide detailed analyzable results. Methods/Statistical analysis: the method of achieving these objectives is by using a back propagation neural network via NeMo based on pattern recognition methodology. Findings: The results show that the NeMo neural network of pattern recognition can identify and recognize the pattern of python programming language with high accuracy. It also shows the ability of the NeMo toolkit to represent the analyzable results through a percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of NeMo simulator to provide beneficial platform for studying and analyzing the complexity of the backpropagation neural network model.


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