82,733 research outputs found
REU Site: Supercomputing Undergraduate Program in Maine (SuperMe)
This award, for a new Research Experience for Undergraduates (REU) site, builds a Supercomputing Undergraduate Program in Maine (SuperMe). This new site provides ten-week summer research experiences at the University of Maine (UMaine) for ten undergraduates each year for three years. With integrated expertise of ten faculty researchers from both computer systems and domain applications, SuperMe allows each undergraduate to conduct meaningful research, such as developing supercomputing techniques and tools, and solving cutting-edge research problems through parallel computing and scientific visualization. Besides being actively involved in research groups, students attend weekly seminars given by faculty mentors, formally report and present their research experiences and results, conduct field trips, and interact with ITEST, RET and GK-12 participants. SuperMe provides scientific exploration ranging from engineering to sciences with a coherent intellectual focus on supercomputing. It consists of four computer systems projects that aim to improve techniques in grid computing, parallel I/O data accesses, high-resolution scientific visualization and information security, and five computer modeling projects that utilize world-class supercomputing and visualization facilities housed at UMaine to perform large, complex simulation experiments and data analysis in different science domains. SuperMe provides a diversity of cutting-edge research opportunities to students from under-represented groups or from universities in rural areas with limited research opportunities. Through interacting directly with the participant of existing programs at UMaine, including ITEST, RET and GK-12, REU students disseminates their research results and experiences to middle and high school students and teachers. This site is co-funded by the Department of Defense in partnership with the NSF REU Site program
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
The Topology ToolKit
This system paper presents the Topology ToolKit (TTK), a software platform
designed for topological data analysis in scientific visualization. TTK
provides a unified, generic, efficient, and robust implementation of key
algorithms for the topological analysis of scalar data, including: critical
points, integral lines, persistence diagrams, persistence curves, merge trees,
contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots,
Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due
to a tight integration with ParaView. It is also easily accessible to
developers through a variety of bindings (Python, VTK/C++) for fast prototyping
or through direct, dependence-free, C++, to ease integration into pre-existing
complex systems. While developing TTK, we faced several algorithmic and
software engineering challenges, which we document in this paper. In
particular, we present an algorithm for the construction of a discrete gradient
that complies to the critical points extracted in the piecewise-linear setting.
This algorithm guarantees a combinatorial consistency across the topological
abstractions supported by TTK, and importantly, a unified implementation of
topological data simplification for multi-scale exploration and analysis. We
also present a cached triangulation data structure, that supports time
efficient and generic traversals, which self-adjusts its memory usage on demand
for input simplicial meshes and which implicitly emulates a triangulation for
regular grids with no memory overhead. Finally, we describe an original
software architecture, which guarantees memory efficient and direct accesses to
TTK features, while still allowing for researchers powerful and easy bindings
and extensions. TTK is open source (BSD license) and its code, online
documentation and video tutorials are available on TTK's website
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
Multi-cultural visualization : how functional programming can enrich visualization (and vice versa)
The past two decades have seen visualization flourish as a research field in its own right, with advances on the computational challenges of faster algorithms, new techniques for datasets too large for in-core processing, and advances in understanding the perceptual and cognitive processes recruited by visualization systems, and through this, how to improve the representation of data. However, progress within visualization has sometimes proceeded in parallel with that in other branches of computer science, and there is a danger that when novel solutions ossify into `accepted practice' the field can easily overlook significant advances elsewhere in the community. In this paper we describe recent advances in the design and implementation of pure functional programming languages that, significantly, contain important insights into questions raised by the recent NIH/NSF report on Visualization Challenges. We argue and demonstrate that modern functional languages combine high-level mathematically-based specifications of visualization techniques, concise implementation of algorithms through fine-grained composition, support for writing correct programs through strong type checking, and a different kind of modularity inherent in the abstractive power of these languages. And to cap it off, we have initial evidence that in some cases functional implementations are faster than their imperative counterparts
Learning Parallel Computations with ParaLab
In this paper, we present the ParaLab teachware system, which can be used for learning the parallel computation methods. ParaLab provides the tools for simulating the multiprocessor computational systems with various network topologies, for carrying out the computational experiments in the simulation mode, and for evaluating the efficiency of the parallel computation methods. The visual presentation of the parallel computations taking place in the computational experiments is the key feature of the system. ParaLab can be used for the laboratory training within various teaching courses in the field of parallel, distributed, and supercomputer computations
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