scholarly journals TriBITS lifecycle model. Version 1.0, a lean/agile software lifecycle model for research-based computational science and engineering and applied mathematical software.

2012 ◽  
Author(s):  
James M. Willenbring ◽  
Roscoe Ainsworth Bartlett ◽  
Michael Allen Heroux
Keyword(s):  
Computational Science ◽  
Mathematical Software ◽  
Science And Engineering ◽  
Model Version ◽  
Lifecycle Model ◽  
Computational Science And Engineering ◽  
Agile Software ◽  
Software Lifecycle

scholarly journals A New Overview of The Trilinos Project

2012 ◽  
Vol 20 (2) ◽  
pp. 83-88 ◽  
Author(s):  
Michael A. Heroux ◽  
James M. Willenbring
Keyword(s):  
Software Engineering ◽  
Computational Science ◽  
Science And Engineering ◽  
Engineering Applications ◽  
Software Infrastructure ◽  
Computational Science And Engineering ◽  
Agile Software Engineering ◽  
Agile Software

SinceAn Overview of the Trilinos Project[ACM Trans. Math. Softw. 31(3) (2005), 397–423] was published in 2005, Trilinos has grown significantly. It now supports the development of a broad collection of libraries for scalable computational science and engineering applications, and a full-featured software infrastructure for rigorous lean/agile software engineering. This growth has created significant opportunities and challenges. This paper focuses on some of the most notable changes to the Trilinos project in the last few years. At the time of the writing of this article, the current release version of Trilinos was 10.12.2.

High-Performance Customizable Computing

2012 ◽  
pp. 48-77
Author(s):  
Domingo Benitez
Keyword(s):  
High Performance ◽  
General Purpose ◽  
Performance Comparison ◽  
Computational Science ◽  
Science And Engineering ◽  
Current Trends ◽  
Computational Science And Engineering ◽  
Top View ◽  
Performance Computing ◽  
Hpcc Systems

Many accelerator-based computers have demonstrated that they can be faster and more energy-efficient than traditional high-performance multi-core computers. Two types of programmable accelerators are available in high-performance computing: general-purpose accelerators such as GPUs, and customizable accelerators such as FPGAs, although general-purpose accelerators have received more attention. This chapter reviews the state-of-the-art and current trends of high-performance customizable computers (HPCC) and their use in Computational Science and Engineering (CSE). A top-down approach is used to be more accessible to the non-specialists. The “top view” is provided by a taxonomy of customizable computers. This abstract view is accompanied with a performance comparison of common CSE applications on HPCC systems and high-performance microprocessor-based computers. The “down view” examines software development, describing how CSE applications are programmed on HPCC computers. Additionally, a cost analysis and an example illustrate the origin of the benefits. Finally, the future of the high-performance customizable computing is analyzed.

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