A JAVA application framework for scientific software development

Theory and Practice, 2012

Software, IEEE, 2008

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2013

Writing complex computer programs to study scientific problems requires careful planning and an in-depth knowledge of programming languages and tools. In this lecture the importance of using the right tool for the right problem is emphasized. Common tools to organize computer programs, as well as to debug and improve them are discussed, followed by simple data reduction strategies and visualization tools. Furthermore, some useful scientific libraries such as boost, GSL, LEDA and numerical recipes are outlined.

e-Science, 2009. e-Science'09. Fifth IEEE …, 2009

It is apparent that the challenges facing scientific software developers are quite different from those facing their commercial counterparts. Among these differences are the challenges posed by the complex and uncertain nature of the science. There is also the fact that many scientists have experience of developing their own software, albeit in a very restricted setting, leading them to have unrealistic expectations about software development in a different setting. In this paper, we explore the challenges facing scientific software developers focusing especially on molecular biology. We claim that the nature and practice of molecular biology is quite different from that of the physical sciences and pose different problems to software developers. We do not claim that this paper is the last word on the topic but hope that it serves as the inspiration for further debate.

1994

Scientific and engineering software is often produced by integration of existing software components of the size of a whole program. However, on the average, scientific software was not developed for reusability and is quite distant from the user model of the application problem: integration and retrofitting is as such a costly process. An architecture, methodology and several C++ class libraries for supporting integration are introduced. The architecture separates a software component layer, and an integration layer. The latter in based on the concept of software model, that is an abstraction of components and a representation of the system differing from its actual physical structure. The methodology is based on matching needs with existing models. The C++ class libraries are explained in some detail, The application to two major systems is analysed and the ideas behind seven other systems are briefly outlined. Some lessons learned are summarised in the conclusions.

Lecture Notes in Computer Science, 2006

Commercial component-based software engineering practices, such as the CORBA component model, Enterprise JavaBeans, and COM, are well-established in the business computing community. These practices present an approach for managing the increasing complexity of scientific software development, which has motivated the Common Component Architecture (CCA), a component specification targeted at highperformance scientific application development. The CCA is an approach to component development that is minimal in terms of the complexity of component interface requirements and imposes a minimal performance penalty. While this lightweight specification has enabled the development of a number of high-performance scientific components in several domains, the software design process for developing component-based scientific codes is not yet well defined. This fact, coupled with the fact that component-based approaches are still new to the scientific community, may lead to an ad hoc design process, potentially resulting in code that is harder to maintain, extend, and test and may negatively affect performance. We explore some concepts and approaches based on widely accepted software architecture design principles and discuss their potential application in the development of high-performance scientific component applications. We particularly emphasize those principles and approaches that contribute to making CCA-based applications easier to design, implement, and maintain, as well as enabling dynamic adaptivity with the goal of maximizing performance.

19th AIAA Computational Fluid Dynamics, 2009

Software engineering is a formal set of tools and procedures for the efficient development of reliable software. Scientific computing software differs from other types of software because the "correct" outputs for a given set of software inputs are generally not known. Since scientific computing relies on approximate numerical solutions to partial differential or integral equations, the answers produced by the software will depend on the mesh employed, the time step chosen, the iterative convergence tolerance used, the number of significant figures used in the floating point computations, etc. The goal of this paper is to provide a broad overview of modern software engineering practices, with a focus on tools and methods that are particularly relevant to scientific computing. Specific topics that are emphasized include software development models, choice of programming language, version control systems, static analysis tools, dynamic testing procedures, and the quantitative assessment of software quality and reliability. A software requirement is a "property that must be exhibited in order to solve some real-world problem" (SWEBOK, 2004). Uncertainty in requirements is a leading cause of failure in software projects (Post and Kendall, 2004). While it is certainly ideal to have all requirements rigorously specified at the beginning of a software project, this can be difficult to achieve for scientific software. Especially in the case of large scientific software development projects, complete requirements can be difficult to specify due to rapid changes in models, algorithms, and even in the specialized computer architectures used to run the software. While lack of requirements definition can adversely affect the development of scientific software, these negative effects can be mitigated somewhat if close communication is maintained between the developer of the software and the user (Post and Kendall, 2004). There are two main types of software requirements. User requirements are formulated at a high level of abstraction, usually in general terms which are easily understood by the user. Software system requirements, on the

Parameterized types, also called "templates," allow for the development of an important class of scientific libraries called "generics" or "generic libraries." The C++ Standard Template Library is a generic library containing over 70 reusable functions that are highly optimized for perfor-mance. The algorithmic complexity of all Standard Template Library algorithms is at most O(n) because at most a constant number of passes over a data set of size n is required for a worst-case scenario. Although the utility of generics for scientific applications is now accepted, the Java program-ming language has no direct support for parameterized types. Currently, two different approaches for creating generics in the Java language are in use, namely "wrappers" and "templates." The use of templates in Java is via a proposed language extension. In this paper we compare and contrast a wrapper-based approach with a template-based approach to generics...

In this paper we report results from an exploratory study of design structures in scientific research software. Dependency Structure Matrix (DSM) is used as a modelling tool to capture and analyze dependencies among system elements such as functions. We compute several architectural complexity metrics and present preliminary results from two open-source scientific computing software applications.