157,169 research outputs found
Challenging the Computational Metaphor: Implications for How We Think
This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Überblick zur Softwareentwicklung in Wissenschaftlichen Anwendungen
Viele wissenschaftliche Disziplinen müssen heute immer komplexer werdende numerische Probleme lösen. Die Komplexität der benutzten wissenschaftlichen Software steigt dabei kontinuierlich an. Diese Komplexitätssteigerung wird durch eine ganze Reihe sich ändernder Anforderungen verursacht: Die Betrachtung gekoppelter Phänomene gewinnt Aufmerksamkeit und gleichzeitig müssen neue Technologien wie das Grid-Computing oder neue Multiprozessorarchitekturen genutzt werden, um weiterhin in angemessener Zeit zu Berechnungsergebnissen zu kommen. Diese Fülle an neuen Anforderungen kann nicht mehr von kleinen spezialisierten Wissenschaftlergruppen in Isolation bewältigt werden. Die Entwicklung wissenschaftlicher Software muss vielmehr in interdisziplinären Gruppen geschehen, was neue Herausforderungen in der Softwareentwicklung induziert. Ein Paradigmenwechsel zu einer stärkeren Separation von Verantwortlichkeiten innerhalb interdisziplinärer Entwicklergruppen ist bis jetzt in vielen Fällen nur in Ansätzen erkennbar. Die Kopplung partitioniert durchgeführter Simulationen physikalischer Phänomene ist ein wichtiges Beispiel für softwaretechnisch herausfordernde Aufgaben im Gebiet des wissenschaftlichen Rechnens. In diesem Kontext modellieren verschiedene Simulationsprogramme unterschiedliche Teile eines komplexeren gekoppelten Systems. Die vorliegende Arbeit gibt einen Überblick über Paradigmen, die darauf abzielen Softwareentwicklung für Berechnungsprogramme verlässlicher und weniger abhängig voneinander zu machen. Ein spezielles Augenmerk liegt auf der Entwicklung gekoppelter Simulationen.Fields of modern science and engineering are in need of solving more and more complex numerical problems. The complexity of scientific software thereby rises continuously. This growth is caused by a number of changing requirements. Coupled phenomena gain importance and new technologies like the computational Grid, graphical and heterogeneous multi-core processors have to be used to achieve high-performance. The amount of additional complexity can not be handled by small groups of specialised scientists. The interdiciplinary nature of scientific software thereby presents new challanges for software engineering. A paradigm shift towards a stronger separation of concerns becomes necessary in the development of future scientific software. The coupling of independently simulated physical phenomena is an important example for a software-engineering concern in the domain of computational science. In this context, different simulation-programs model only a part of a more complex coupled system. The present work gives overview on paradigms which aim at making software-development in computational sciences more reliable and less interdependent. A special focus is put on the development of coupled simulations
Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system
Biology has taken strong steps towards becoming a computer science aiming at
reprogramming nature after the realisation that nature herself has reprogrammed
organisms by harnessing the power of natural selection and the digital
prescriptive nature of replicating DNA. Here we further unpack ideas related to
computability, algorithmic information theory and software engineering, in the
context of the extent to which biology can be (re)programmed, and with how we
may go about doing so in a more systematic way with all the tools and concepts
offered by theoretical computer science in a translation exercise from
computing to molecular biology and back. These concepts provide a means to a
hierarchical organization thereby blurring previously clear-cut lines between
concepts like matter and life, or between tumour types that are otherwise taken
as different and may not have however a different cause. This does not diminish
the properties of life or make its components and functions less interesting.
On the contrary, this approach makes for a more encompassing and integrated
view of nature, one that subsumes observer and observed within the same system,
and can generate new perspectives and tools with which to view complex diseases
like cancer, approaching them afresh from a software-engineering viewpoint that
casts evolution in the role of programmer, cells as computing machines, DNA and
genes as instructions and computer programs, viruses as hacking devices, the
immune system as a software debugging tool, and diseases as an
information-theoretic battlefield where all these forces deploy. We show how
information theory and algorithmic programming may explain fundamental
mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and
Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George
Ellis (eds.), Cambridge University Pres
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