20,757 research outputs found
The Whole is Greater than the Sum of the Parts: Optimizing the Joint Science Return from LSST, Euclid and WFIRST
The focus of this report is on the opportunities enabled by the combination
of LSST, Euclid and WFIRST, the optical surveys that will be an essential part
of the next decade's astronomy. The sum of these surveys has the potential to
be significantly greater than the contributions of the individual parts. As is
detailed in this report, the combination of these surveys should give us
multi-wavelength high-resolution images of galaxies and broadband data covering
much of the stellar energy spectrum. These stellar and galactic data have the
potential of yielding new insights into topics ranging from the formation
history of the Milky Way to the mass of the neutrino. However, enabling the
astronomy community to fully exploit this multi-instrument data set is a
challenging technical task: for much of the science, we will need to combine
the photometry across multiple wavelengths with varying spectral and spatial
resolution. We identify some of the key science enabled by the combined surveys
and the key technical challenges in achieving the synergies.Comment: Whitepaper developed at June 2014 U. Penn Workshop; 28 pages, 3
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Virtual Astronomy, Information Technology, and the New Scientific Methodology
All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transform the scientific practice, but also poses a number of common technological challenges. The Virtual Observatory concept is the astronomical community's response to these challenges: it aims to harness the progress in information technology in the service of astronomy, and at the same time provide a valuable testbed for information technology and applied computer science. Challenges broadly fall into two categories: data handling (or "data farming"), including issues such as archives, intelligent storage, databases, interoperability, fast networks, etc., and data mining, data understanding, and knowledge discovery, which include issues such as automated clustering and classification, multivariate correlation searches, pattern recognition, visualization in highly hyperdimensional parameter spaces, etc., as well as various applications of machine learning in these contexts. Such techniques are forming a methodological foundation for science with massive and complex data sets in general, and are likely to have a much broather impact on the modern society, commerce, information economy, security, etc. There is a powerful emerging synergy between the
computationally enabled science and the science-driven computing, which will drive the progress in science, scholarship, and many other venues in the 21st century
High-Throughput Computing on High-Performance Platforms: A Case Study
The computing systems used by LHC experiments has historically consisted of
the federation of hundreds to thousands of distributed resources, ranging from
small to mid-size resource. In spite of the impressive scale of the existing
distributed computing solutions, the federation of small to mid-size resources
will be insufficient to meet projected future demands. This paper is a case
study of how the ATLAS experiment has embraced Titan---a DOE leadership
facility in conjunction with traditional distributed high- throughput computing
to reach sustained production scales of approximately 52M core-hours a years.
The three main contributions of this paper are: (i) a critical evaluation of
design and operational considerations to support the sustained, scalable and
production usage of Titan; (ii) a preliminary characterization of a next
generation executor for PanDA to support new workloads and advanced execution
modes; and (iii) early lessons for how current and future experimental and
observational systems can be integrated with production supercomputers and
other platforms in a general and extensible manner
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
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
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