33,208 research outputs found
Chaste: a test-driven approach to software development for biological modelling
Chaste (āCancer, heart and soft-tissue environmentā) is a software library and a set of test suites for computational simulations in the domain of biology. Current functionality has arisen from modelling in the fields of cancer, cardiac physiology and soft-tissue mechanics. It is released under the LGPL 2.1 licence.\ud
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Chaste has been developed using agile programming methods. The project began in 2005 when it was reasoned that the modelling of a variety of physiological phenomena required both a generic mathematical modelling framework, and a generic computational/simulation framework. The Chaste project evolved from the Integrative Biology (IB) e-Science Project, an inter-institutional project aimed at developing a suitable IT infrastructure to support physiome-level computational modelling, with a primary focus on cardiac and cancer modelling
The C++0x "Concepts" Effort
C++0x is the working title for the revision of the ISO standard of the C++
programming language that was originally planned for release in 2009 but that
was delayed to 2011. The largest language extension in C++0x was "concepts",
that is, a collection of features for constraining template parameters. In
September of 2008, the C++ standards committee voted the concepts extension
into C++0x, but then in July of 2009, the committee voted the concepts
extension back out of C++0x.
This article is my account of the technical challenges and debates within the
"concepts" effort in the years 2003 to 2009. To provide some background, the
article also describes the design space for constrained parametric
polymorphism, or what is colloquially know as constrained generics. While this
article is meant to be generally accessible, the writing is aimed toward
readers with background in functional programming and programming language
theory. This article grew out of a lecture at the Spring School on Generic and
Indexed Programming at the University of Oxford, March 2010
DOLFIN: Automated Finite Element Computing
We describe here a library aimed at automating the solution of partial differential equations using the finite element method. By employing novel techniques for automated code generation, the library combines a high level of expressiveness with efficient computation. Finite element variational forms may be expressed in near mathematical notation, from which low-level code is automatically generated, compiled and seamlessly integrated with efficient implementations of
computational meshes and high-performance linear algebra. Easy-to-use object-oriented interfaces to the library are provided in the form of a C++ library and a Python module. This paper discusses the mathematical abstractions and methods used in the design of the library and its implementation. A number of examples are presented to demonstrate the use of the library in application code
A C++11 implementation of arbitrary-rank tensors for high-performance computing
This article discusses an efficient implementation of tensors of arbitrary
rank by using some of the idioms introduced by the recently published C++ ISO
Standard (C++11). With the aims at providing a basic building block for
high-performance computing, a single Array class template is carefully crafted,
from which vectors, matrices, and even higher-order tensors can be created. An
expression template facility is also built around the array class template to
provide convenient mathematical syntax. As a result, by using templates, an
extra high-level layer is added to the C++ language when dealing with algebraic
objects and their operations, without compromising performance. The
implementation is tested running on both CPU and GPU.Comment: 21 pages, 6 figures, 1 tabl
Python bindings for the open source electromagnetic simulator Meep
Meep is a broadly used open source package for finite-difference time-domain electromagnetic simulations. Python bindings for Meep make it easier to use for researchers and open promising opportunities for integration with other packages in the Python ecosystem. As this project shows, implementing Python-Meep offers benefits for specific disciplines and for the wider research community
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