5,795 research outputs found
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
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
Accelerating Science: A Computing Research Agenda
The emergence of "big data" offers unprecedented opportunities for not only
accelerating scientific advances but also enabling new modes of discovery.
Scientific progress in many disciplines is increasingly enabled by our ability
to examine natural phenomena through the computational lens, i.e., using
algorithmic or information processing abstractions of the underlying processes;
and our ability to acquire, share, integrate and analyze disparate types of
data. However, there is a huge gap between our ability to acquire, store, and
process data and our ability to make effective use of the data to advance
discovery. Despite successful automation of routine aspects of data management
and analytics, most elements of the scientific process currently require
considerable human expertise and effort. Accelerating science to keep pace with
the rate of data acquisition and data processing calls for the development of
algorithmic or information processing abstractions, coupled with formal methods
and tools for modeling and simulation of natural processes as well as major
innovations in cognitive tools for scientists, i.e., computational tools that
leverage and extend the reach of human intellect, and partner with humans on a
broad range of tasks in scientific discovery (e.g., identifying, prioritizing
formulating questions, designing, prioritizing and executing experiments
designed to answer a chosen question, drawing inferences and evaluating the
results, and formulating new questions, in a closed-loop fashion). This calls
for concerted research agenda aimed at: Development, analysis, integration,
sharing, and simulation of algorithmic or information processing abstractions
of natural processes, coupled with formal methods and tools for their analyses
and simulation; Innovations in cognitive tools that augment and extend human
intellect and partner with humans in all aspects of science.Comment: Computing Community Consortium (CCC) white paper, 17 page
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