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Scientific Software Component Technology
We are developing new software component technology for high-performance parallel scientific computing to address issues of complexity, re-use, and interoperability for laboratory software. Component technology enables cross-project code re-use, reduces software development costs, and provides additional simulation capabilities for massively parallel laboratory application codes. The success of our approach will be measured by its impact on DOE mathematical and scientific software efforts. Thus, we are collaborating closely with library developers and application scientists in the Common Component Architecture forum, the Equation Solver Interface forum, and other DOE mathematical software groups to gather requirements, write and adopt a variety of design specifications, and develop demonstration projects to validate our approach. Numerical simulation is essential to the science mission at the laboratory. However, it is becoming increasingly difficult to manage the complexity of modern simulation software. Computational scientists develop complex, three-dimensional, massively parallel, full-physics simulations that require the integration of diverse software packages written by outside development teams. Currently, the integration of a new software package, such as a new linear solver library, can require several months of effort. Current industry component technologies such as CORBA, JavaBeans, and COM have all been used successfully in the business domain to reduce software development costs and increase software quality. However, these existing industry component infrastructures will not scale to support massively parallel applications in science and engineering. In particular, they do not address issues related to high-performance parallel computing on ASCI-class machines, such as fast in-process connections between components, language interoperability for scientific languages such as Fortran, parallel data redistribution between components, and massively parallel components. While industrial component systems do not directly address scientific computing issues, we leverage existing industry technologies and design concepts whenever possible
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 Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review
Large-scale simulations play a central role in science and the industry.
Several challenges occur when building simulation software, because simulations
require complex software developed in a dynamic construction process. That is
why simulation software engineering (SSE) is emerging lately as a research
focus. The dichotomous trade-off between scalability and efficiency (SE) on the
one hand and maintainability and portability (MP) on the other hand is one of
the core challenges. We report on the SE/MP trade-off in the context of an
ongoing systematic literature review (SLR). After characterizing the issue of
the SE/MP trade-off using two examples from our own research, we (1) review the
33 identified articles that assess the trade-off, (2) summarize the proposed
solutions for the trade-off, and (3) discuss the findings for SSE and future
work. Overall, we see evidence for the SE/MP trade-off and first solution
approaches. However, a strong empirical foundation has yet to be established;
general quantitative metrics and methods supporting software developers in
addressing the trade-off have to be developed. We foresee considerable future
work in SSE across scientific communities.Comment: 9 pages, 2 figures. Accepted for presentation at the Fourth
International Workshop on Software Engineering for High Performance Computing
in Computational Science and Engineering (SEHPCCSE 2016
IMP Science Gateway: from the Portal to the Hub of Virtual Experimental Labs in Materials Science
"Science gateway" (SG) ideology means a user-friendly intuitive interface
between scientists (or scientific communities) and different software
components + various distributed computing infrastructures (DCIs) (like grids,
clouds, clusters), where researchers can focus on their scientific goals and
less on peculiarities of software/DCI. "IMP Science Gateway Portal"
(http://scigate.imp.kiev.ua) for complex workflow management and integration of
distributed computing resources (like clusters, service grids, desktop grids,
clouds) is presented. It is created on the basis of WS-PGRADE and gUSE
technologies, where WS-PGRADE is designed for science workflow operation and
gUSE - for smooth integration of available resources for parallel and
distributed computing in various heterogeneous distributed computing
infrastructures (DCI). The typical scientific workflows with possible scenarios
of its preparation and usage are presented. Several typical use cases for these
science applications (scientific workflows) are considered for molecular
dynamics (MD) simulations of complex behavior of various nanostructures
(nanoindentation of graphene layers, defect system relaxation in metal
nanocrystals, thermal stability of boron nitride nanotubes, etc.). The user
experience is analyzed in the context of its practical applications for MD
simulations in materials science, physics and nanotechnologies with available
heterogeneous DCIs. In conclusion, the "science gateway" approach - workflow
manager (like WS-PGRADE) + DCI resources manager (like gUSE)- gives opportunity
to use the SG portal (like "IMP Science Gateway Portal") in a very promising
way, namely, as a hub of various virtual experimental labs (different software
components + various requirements to resources) in the context of its practical
MD applications in materials science, physics, chemistry, biology, and
nanotechnologies.Comment: 6 pages, 5 figures, 3 tables; 6th International Workshop on Science
Gateways, IWSG-2014 (Dublin, Ireland, 3-5 June, 2014). arXiv admin note:
substantial text overlap with arXiv:1404.545
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
Towards a service-oriented e-infrastructure for multidisciplinary environmental research
Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures
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