The core of scientific computing is designing, writing, testing, debugging and modifying numerical software for application to a vast range of areas: from graphics, meteorology and chemistry to engineering, biology and finance. Scientists, engineers and computer scientists need to write good code, for speed, clarity, flexibility and ease of re-use. Oliveira and Stewart's style guide for numerical software points out good practices to follow, and pitfalls to avoid. By following their advice, readers will learn how to write efficient software, and how to test it for bugs, accuracy and performance. Techniques are explained with a variety of programming languages, and illustrated with two extensive design examples, one in Fortran 90 and one in C++: other examples in C, C++, Fortran 90 and Java are scattered throughout the book. This manual of scientific computing style will be an essential addition to the bookshelf and lab of everyone who writes numerical software.

This manual of scientific computing style will prove to be an essential addition to the bookshelf and lab of everyone who writes numerical software. Scientists, engineers and computer scientists who follow its advice will learn how to write good software, and how to test it for bugs, accuracy and performance.

A manual and guide to good scientific computing style, explaining how to write good software and how to test it for bugs, accuracy and performance.

Looking back at the years that have passed since the realization of the very first electronic, multi-purpose computers, one observes a tremendous growth in hardware and software performance. Today, researchers and engi neers have access to computing power and software that can solve numerical problems which are not fully understood in terms of existing mathemati cal theory. Thus, computational sciences must in many respects be viewed as experimental disciplines. As a consequence, there is a demand for high quality, flexible software that allows, and even encourages, experimentation with alternative numerical strategies and mathematical models. Extensibil ity is then a key issue; the software must provide an efficient environment for incorporation of new methods and models that will be required in fu ture problem scenarios. The development of such kind of flexible software is a challenging and expensive task. One way to achieve these goals is to in vest much work in the design and implementation of generic software tools which can be used in a wide range of application fields. In order to provide a forum where researchers could present and discuss their contributions to the described development, an International Work shop on Modern Software Tools for Scientific Computing was arranged in Oslo, Norway, September 16-18, 1996. This workshop, informally referred to as Sci Tools '96, was a collaboration between SINTEF Applied Mathe matics and the Departments of Informatics and Mathematics at the Uni versity of Oslo.

A variety of programming models relevant to scientists explained, with an emphasis on how programming constructs map to parts of the computer. What makes computer programs fast or slow? To answer this question, we have to get behind the abstractions of programming languages and look at how a computer really works. This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Two themes emerge: program speed and program modularity. Throughout this book, the premise is to "get under the hood," and the discussion is tied to specific programs. The book digs into linkers, compilers, operating systems, and computer architecture to understand how the different parts of the computer interact with programs. It begins with a review of C/C++ and explanations of how libraries, linkers, and Makefiles work. Programming models covered include Pthreads, OpenMP, MPI, TCP/IP, and CUDA.The emphasis on how computers work leads the reader into computer architecture and occasionally into the operating system kernel. The operating system studied is Linux, the preferred platform for scientific computing. Linux is also open source, which allows users to peer into its inner workings. A brief appendix provides a useful table of machines used to time programs. The book's website (https://github.com/divakarvi/bk-spca) has all the programs described in the book as well as a link to the html text.

The book teaches students to model a scientific problem and write a computer program in C language to solve that problem. It introduces the basics of C language, and then describes and discusses algorithms commonly used in scientific applications (e.g. searching, graphs, statistics, equation solving, Monte Carlo methods etc.).

The third edition of this book aims to equip both young and experienced researchers with all the tools and strategy they will need for their papers to not just be accepted, but stand out in the crowded field of academic publishing. It seeks to question and deconstruct the legacy of existing science writing, replacing or supporting historically existing practices with principle- and evidence-driven styles of effective writing. It encourages a reader-centric approach to writing, satisfying reader-scientists at large, but also the paper's most powerful readers, the reviewer and editor. Going beyond the baseline of well-structured scientific writing, this book leverages an understanding of human physiological limitations (memory, attention, time) to help the author craft a document that is optimized for readability. Through real and fictional examples, hands-on exercises, and entertaining stories, this book breaks down the critical parts of a typical scientific paper (Title, Abstract, Introduction, Visuals, Structure, and Conclusions). It shows at great depth how to achieve the essential qualities required in scientific writing, namely being clear, concise, convincing, fluid, interesting, and organized. To enable the writer to assess whether these parts are well written from a reader's perspective, the book also offers practical metrics in the form of six checklists, and even an original Java application to assist in the evaluation.

This book concerns software design. Although many current discussions of scientific programming focus on scalable performance, this book focuses on scalable design. The authors analyze how the structure of a package determines its developmental complexity according to such measures as bug search times and documentation information content. The work presents arguments for why these issues impact solution cost and time more than does scalable performance. The final chapter explores the question of scalable execution and shows how scalable design relates to scalable execution. The book's focus is on program organization, which has received considerable attention in the broader software engineering community, where graphical description standards for modeling software structure and behavior have been developed by computer scientists. These discussions might be enriched by engineers who write scientific codes. This book aims to bring such scientific programmers into discussion with computer scientists. The authors do so by introducing object-oriented software design patterns in the context of scientific simulation.

Gábor Lövei’s scientific communication course for students and scientists explores the intricacies involved in publishing primary scientific papers, and has been taught in more than twenty countries. Writing and Publishing Scientific Papers is the distillation of Lövei’s lecture notes and experience gathered over two decades; it is the coursebook many have been waiting for. The book’s three main sections correspond with the three main stages of a paper’s journey from idea to print: planning, writing, and publishing. Within the book’s chapters, complex questions such as ‘How to write the introduction?’ or ‘How to submit a manuscript?’ are broken down into smaller, more manageable problems that are then discussed in a straightforward, conversational manner, providing an easy and enjoyable reading experience. Writing and Publishing Scientific Papers stands out from its field by targeting scientists whose first language is not English. While also touching on matters of style and grammar, the book’s main goal is to advise on first principles of communication. This book is an excellent resource for any student or scientist wishing to learn more about the scientific publishing process and scientific communication. It will be especially useful to those coming from outside the English-speaking world and looking for a comprehensive guide for publishing their work in English.

This easy-to-read textbook/reference presents an essential guide to object-oriented C++ programming for scientific computing. With a practical focus on learning by example, the theory is supported by numerous exercises. Features: provides a specific focus on the application of C++ to scientific computing, including parallel computing using MPI; stresses the importance of a clear programming style to minimize the introduction of errors into code; presents a practical introduction to procedural programming in C++, covering variables, flow of control, input and output, pointers, functions, and reference variables; exhibits the efficacy of classes, highlighting the main features of object-orientation; examines more advanced C++ features, such as templates and exceptions; supplies useful tips and examples throughout the text, together with chapter-ending exercises, and code available to download from Springer.