Manish Parashar - US grants

Astrophysical and computer science research will be carried out to bring the numerical treatment of the Einstein theory of general relativity to the astrophysics community at large. The project will produce a new community tool for research on problems involving strong-gravity astrophysical systems, such as neutron stars and black holes. It will also develop new computing technologies that allow simulation computer codes on massively parallel machines to be simultaneously developed and used by a large, multidisciplinary, and distributed community of researchers. To deal with the complexity and richness of the Einstein theory when applied to realistic astrophysical phenomena, simultaneous advances in computation, visualization, and numerical algorithms will be developed. To deal with the complexity of the resulting simulation code and the range of problems to which it can be applied, a simulation-centered "collaboratory" will be developed that can support a large user and developer community and that will reduce the barriers to experimentation and exploration. The development of this simulation collaboratory will be driven by the astrophysically significant problem of the collapse of a neutron star to a black hole induced by accretion of mass from its surroundings. The problem will be studied in the Einstein theory of general relativity with relativistic hydrodynamics and nuclear astrophysics also incorporated, and making use of all major components in the collaboratory.

The impact of the project will be in the new frontiers of gravitational-wave astronomy and high energy (gamma-ray and X-ray) astronomies. The inclusion of the full Einstein theory in the study of realistic astrophysical processes, when coupled with observational data from new high-energy satellites and imminent gravitational-wave observatories (such as the US Laser Interferometer Gravitational-Wave Observatory (LIGO) project) will lead to far-reaching scientific discoveries capable of attracting the attention of a large scientific community and the general public. The successful establishment of a new model for the collaborative development and the use of simulation computer codes, exploiting high-speed connectivity not only between computers but also between researchers, will have a significant impact on other research communities.

CAREER - Development of a Unified Data-Management and Interaction Substrate: An Integrated research and Education Program for Enabling Distributed Computational Collaboratories

Simulating complex physical systems is the fundamental goal of scientific computing. These simulations have become so advanced that they are now seen as a new mode of doing science (joining theory and experiment). However, creating, running, and sharing them is still not an easy task because of the complexities in the work. This project will study ways to improve that situation.

The project will design, develop and evaluate a unified computational and interaction engine that can drive distributed computational collaboratories, and enable a new generation of realistic, interactive and immersive simulations. The fundamental innovation is the formulation of unifying multifaceted objects that can simultaneously support adaptive computations, interaction and control, analysis, and archival storage/retrieval. The engine provides a distributed data-management and interaction substrate for these objects to enable a "seamless" integration of scalable parallel and distributed computing, interactive computational steering, collaborative visualization and analysis, and scientific databases and data archives. In the educational arena, the project will establish a comprehensive program in applied parallel and distributed computation. This will include development of a set of graduate and undergraduate courses integrating software engineering and application-oriented parallel computing and an instructional program at the high-school and college freshman level built on the technologies developed by the research component.

The goal of this project is to develop a methodology and set of prototype tools to enable "associative" mining of very large scientific data sets. The researchers will use content, such as solution features, patterns, and shapes, to examine the data and retrieve required information. Unlike other approaches that use index-based coordinates, this lets scientists answer the kinds of questions that they typically ask - such as "Have I seen this evolution before?" and "Is it similar to any experimental observations?" The tools developed by this project will operate on distributed time-varying data and will act as templates for other methods. Specific technical objectives include developing distributed multi-resolution techniques for cataloging interesting phenomena and searching both run-time and archived data for interesting phenomena. The research will specifically target two domains that are representative of other scientific areas and have a pressing need for scientific mining tools: fluid dynamics (large-scale, high-accuracy Direct Numerical Simulation of compressible turbulence) and oceanography (comparison of simulation data with acoustic observations of hydrothermal plumes).

EIA-0103674
Manish Parashar
Rutgers University

The overall research goal of this proposal is to design, develop, evaluate and deploy Pragma, the next generation adaptive runtime infrastructure capable of reactively and proactively managing and optimizing application execution using current system and application state, predictive models for system behavior and application performance, and an agent based control network. The overarching motivation for this research is to enable very large-scale, dynamically adaptive scientific and engineering simulations on widely distributed and highly heterogeneous and dynamic execution environments such as the computational "grid". The design, development and evaluation of the proposed Pragma framework will be conducted in collaboration with the Astronomy Department at the University of Arizona in the context of a real-world astrophysical hydrodynamics simulation using adaptive mesh refinement and including multigroup flux-limited diffusion, self gravity, nuclear burning, and a complex equation of state.

Collaborative Research: ITR/AP&IM Data Intense Challenge:
The Instrumented Oil Field of the Future

Mary Wheeler - University of Texas at Austin - 0121523
Alan Sussman - University of Maryland, College Park - 0121161
Joel Saltz - Ohio State University Research Foundation - 0121177
Manish Parashar - Rutgers University - 0120934


Increasing production from existing oil and natural gas reservoirs is crucial for the US economy. In order to better monitor and optimize oil and gas production, advanced technologies from field instrumentation to information technology and computational science are essential. Field technologies include time-lapse surface and borehole seismic, permanent downhole sensors, intelligent well completions, fiber optics, and remote control operations. IT technologies include data management, data visualization, parallel computing, and decision-making tools such as new wave propagation and multiphase, multi-component flow and transport computational portals. These diverse technologies can be integrated to achieve real-time monitoring and optimization of reservoir production: The Instrumented Oilfield.

A major outcome of the proposed research is a computing portal which will enable reservoir simulation and geophysical calculations to interact dynamically with the data and with each other and which will provide a variety of visual and quantitative tools. Test data will be provided by oil and service companies currently participating in UT Austin industrial affiliate programs. Since the proposed research is directed towards the general problem of modeling and characterization of the earth's subsurface, it has immediate application to other areas, including environmental remediation and storage of hazardous wastes.

This collaborative research proposal is focused on the creation of a large-scale wireless network testbed which will facilitate a broad range of experimental research on next-generation protocols and application concepts. It is recognized that powerful technology and market trends towards portable computing and communication imply an increasingly important role for wireless access in the next-generation Internet. At the same time, new sensor and pervasive computing applications are expected to drive large-scale deployments of embedded computing devices interconnected via new types of short-range wireless networks. The speed of technology innovation in the wireless networking field can be significantly increased with the development of a flexible, open-access wireless network testbed that can be shared by experimental researchers across the networking community.

The proposed ORBIT (Open Access Research Testbed for Next-Generation Wireless Networks) system is a two-tier laboratory emulator/field trial network testbed designed to achieve reproducibility of experimentation, while also supporting evaluation of protocols and applications in real-world settings. In particular, the laboratory-based wireless network emulator will be constructed using a novel approach involving a large two-dimensional grid of static and mobile 802.11x radio nodes which can be dynamically interconnected into specified topologies with reproducible wireless channel models. All radio devices in the system provide open API's that permit end-users to download radio link, MAC and network layer protocols to construct a specific networking scenario. Once the basic protocol or application concepts have been validated on the lab emulator platform, users can migrate their experiments to the field test network which provides a configurable mix of both high-speed cellular (3G) and 802.11x wireless access in a real-world setting. Extensive measurement tools will be provided to support research evaluation, including both network traffic and radio link/spectrum usage aspects.

In addition to the development of the ORBIT wireless testbed infrastructure, this project includes a comprehensive set of "experimental work packages" intended to generate design requirements and serve as end-user application drivers for the system being developed. Specific research topics to be covered during the course of this project are:

1. Ad hoc networking in 802.11x WLAN scenarios [Raychaudhuri, Seskar; Rutgers & Acharya; IBM]
2. Message-based multimedia delivery [Schulzrinne, Columbia; Yates, Rutgers]
3. XML-based content multicasting for mobile information services [Ott, Raychaudhuri; Rutgers]
4. Location-based mobile network services [Schulzrinne; Columbia]
5. Pervasive computing software models for sensor networks [Parashar, Zhang; Rutgers]
6. Security protocols for next-generation wireless networks [Kobayashi; Princeton & Trappe; Rutgers]
7. Intelligent network middleware (INM) for mobile services [Paul; Lucent Bell Labs]
8. Peer-to-peer infrastructure for VoIP and IM [Acharya, Saha; IBM Research]
9. Power/bandwidth efficient media delivery to portable platforms [Ramaswamy, Wang; Thomson R&D]

The project will be conducted as a collaborative effort between several university research groups in the NY/NJ region: Rutgers, Columbia, and Princeton, along with industrial partners Lucent Bell Labs, IBM Research and Thomson. The wireless network testbed will be developed and operated by Rutgers WINLAB, using facilities located at the Rutgers New Brunswick campus and at partner sites in the area. The testbed will be available for remote or on-site access by other research groups nationally, subject to NSF guidelines for use. Additional partners will be sought during the course of the program both for testbed infrastructure development and for research collaboration.

The scientific/technical merits of the proposed project are: advancing the state-of-the-art in design and
implementation of flexible and scalable wireless network testbeds, and experimental investigation of novel
architectures, protocols and service concepts for next-generation wireless networks. Broader impacts are in
acceleration of the R & D cycle for wireless networking by providing the research community with a shared-use experimental platform, and in fostering increased use of experimental methods in both research and teaching.

This is a proposal to organize the Autonomic Computing Workshop (ACW 2003) which will be held in conjunction with the 12th IEEE International Symposium on High Performance Distributed Computing (HPDC-12), June 25th, 2003 at Seattle, Washington and with the 8th Global Grid Forum. More information about the workshop can be found at http://www.caip.rutgers.edu/ams2003/. The proliferation of Internet technologies, services and devices, have made the current networked system designs, and management tools incapable of designing reliable, secure networked systems and services. In fact, we have reached a level of complexity, heterogeneity, and dynamism that our information infrastructure is becoming unmanageable and insecure. Furthermore, current design techniques and software tools that control and manage the information infrastructure are incapable of handling its complexity, heterogeneity, uncertainty, and security requirements. On the other hand, biological systems have developed successful strategies and techniques to handle these issues. The main goal of the Autonomic Computing Workshop is establish a world-class forum to investigate the research issues and enabling technologies toward the convergence of biological technological and information systems (called Autonomic Computing). Autonomic computing research will enable the design of the next generation of networked systems and services that are capable of managing and controlling themselves, and can anticipate their workloads and automatically adjust the configurations of their resources to meet the new loads. NSF sponsored the second through the fourth Active Middleware Services that were held in conjunction with the IEEE High Performance Distributed Computing Symposiums (HPDC-9 - HPDC-11). These three workshops were very successful, with invited and contributed research papers presented at each workshop. The proceedings of the Second AMS Workshop was published by Kluwer Academic Publishers in 2000 as a book entitled "ACTIVE MIDDLEWARE SERVICES" and edited by Salim Hariri, Craig A. Lee, and Cauligi S. Raghavendra. The Third and Fourth Annual AMS workshop proceedings were published by IEEE Computer Society. The success of the AMS workshops has motivated the organization of the present workshop which is the fifth workshop in this series . The fifth AMS workshop will focus on the research issues and challenges facing the development of autonomic computing systems that have the capabilities of being self-defining, self-configuring, self-healing, self-optimizing, self-anticipating, being contextually aware of their environments, are and open.

Intellectual Merit. Remote sensing is employed in science and engineering problems to infer material properties when these properties can not be directly sampled. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. Repeated observations over targets for environmental remediation and reservoir production has become a recognized diagnostic tool for assisting management decisions. In addition, improved optimization techniques capable of responding to large, multi-resolution, disparate, dynamic datasets in a fault tolerant and adaptive fashion are a fundamental requirement for effectively estimating and minimizing the uncertainty in any data driven application. The integrated and e_ective treatment of these issues motivates the present project. The assembled research team proposes to advance the mathematical, engineering and computational foundations necessary to enhance our understanding and extend the predictive capabilities of the physical processes that govern the subsurface phenomena at multiple temporal and spatial scales. Target applications include management of aquifers for water resources, optimizing oil and gas production, and monitoring environmental risks e.g., at waste containment sites or arising from natural hazards.

The intellectual merits of the project include: (1) development of the next generation of accurate, multi-scale, coupled chemical, uid, geomechanical, and geophysical simulators for modeling instrumented subsurface environments; (2) large scale optimization techniques (based on a hybridization of global and local approaches) to drive reliable decision-making and a dynamic symbiotic feedback between computation and data; (3) deployment of an autonomic Grid middleware for providing the adequate processing substrate and data management services for (1) and (2). The realization of the above contributions will result in the Data Driven Subsurface Simulation Framework (DDSSF).

A stochastic information fusion methodology is developed to assimilate electrical resistivity tomography, high-frequency ground penetrating radar, mid-range-frequency radar, pneumatic/gas tracer tomography, and hydraulic/tracer tomography to image fractures, characterize hydrogeophysical properties, and monitor natural processes in the vadose zone. The information technology research will develop: (1) mechanisms and algorithms for fusion of large data volumes; (2) parallel adaptive computational engines supporting parallel adaptive algorithms and multi-physics/multi-model computations; (3) adaptive runtime mechanisms for proactive and reactive runtime adaptation and optimization of geophysical and hydrological models of the subsurface; and (4) technologies and infrastructure for remote (pervasive) and collaborative access to computational capabilities for monitoring subsurface processes through interactive visualization tools.

The combination of the stochastic fusion approach and information technology can lead to a new level of capability for both hydrologists and geophysicists enabling them to "see" into the earth at greater depths and resolutions than is possible today. Furthermore, the new computing strategies will make high resolution and large-scale hydrological and geophysical modeling feasible for the private sector, scientists, and engineers who are unable to access supercomputers, i.e., it is an effective paradigm for technology transfer.

The proliferation of Internet technologies, services and devices, have made the current networked system designs, and management tools, incapable of designing reliable, secure network systems and services. In fact, we have reached a level of complexity, heterogeneity, and dynamism that our information infrastructure is becoming unmanageable and insecure. Furthermore, current design techniques and software tools that control and manage the information infrastructure are incapable of handling its complexity, heterogeneity, uncertainty, and security requirements. Autonomic computing research will enable the design of the next of the next generation of networked systems and services that are capable of managing and controlling themselves, and can anticipate their workloads and automatically adjust the configurations of their resources to meet the new loads.

The ICAC workshop will be focused on the research issues and challenges facing the development of autonomic computing systems. The goal of the CLADE workshop is to encourage innovation by addressing the complex issues that arise in large-scale applications of distributed computation and to promote the development of innovative applications that effectively use distributed resources and adapt to a wide range of heterogeneity and dynamics in space and time. This includes development, deployment, management and evaluations of large scale applications in science, engineering, medicine, business, economics, education, and other disciplines, on Grids and other distributed heterogeneous and dynamic computing environments.

The emergence of computational Grids and the potential for seamless aggregation, integration and interactions has made it possible to conceive a new generation of realistic, scientific and engineering simulations of complex physical phenomena.

These applications will symbiotically and opportunistically combine computations, experiments, observations, and realtime data, and will provide important insights into complex systems. Furthermore, the underlying Grid infrastructure is similarly heterogeneous and dynamic, globally aggregating large numbers of independent computing and communication resources, data stores and sensor networks. The combination of the two results in an application development, configuration and management complexities that break current paradigms based on passive components and static compositions. This projectwill make the following specific contributions:

1. Definition of Autonomous Components: We will define programming abstractions and supporting infrastructure that will enable the definition of autonomous components. Together, the aspects, policies, and policy engine allow autonomous components to consistently and securely configure, manage, adapt and optimize their execution.
2. Dynamic Composition of Autonomic Applications: We will develop mechanisms and supporting infrastructure to enable autonomic applications to be dynamically and opportunistically composed from autonomic components. The composition will be based on policies and constraints that are defined, deployed and executed at run time, and will be aware of available Grid resources (systems, services, storage, data) and components, and their current states, requirements, and capabilities.
3. Online Performance Modeling of Component-based Applications: We will investigate the formulation of
predictive performance functions that hierarchically combine analytical, experimental and empirical performance models for application components and elements of the Grid.
4. Autonomic Middleware Services: The project will design, develop and deploy key services on top of the emerging Grid middleware infrastructure to support autonomic applications. One of the key requirements for autonomic behavior and dynamic compositions is the ability of the components, applications and resources (systems, services, storage, data) to interact as peers. Furthermore the components should be able to sense their environment. The project will extend the Grid middleware with (1) a peer-to-peer substrate, (2) context aware services, and (3) peer-to-peer deductive engines for composition, configuration and management of autonomic applications. An active peer-to-peer control network will combine sensors, actuators and rules to configure and tune components and their execution environment at runtime and satisfy requirements, and performance and quality of service constraints.

The primary goal of the International Conference on Autonomic Computing is to establish a world-class forum to investigate the research issues and enabling technologies toward the convergence of biological technological and information systems (called Autonomic Computing). Autonomic computing research will enable the design of the next generation of networked systems and services that are capable of managing and controlling themselves, and can anticipate their workloads and automatically adjust the configurations of their resources to meet the new loads.

The objectives of this conference are two-fold: first, by bringing together researchers in this field, to further understand and address the challenging research issues facing the development and deployment of autonomic systems and applications and support dynamic, data-driven application systems, i.e. applications that dynamically interact with others by sharing processing and data in novel ways and second, to focus and crystallize the needed direction of future NSF funding to maximize the scientific and technological "returns" from those research investments.

CSR?CSI?SGER: Sensor Systems Technologies for Dynamic Data-Driven Scientific Applications

The overall goal of this research project is to develop sensor system middleware and programming support that will enable distributed networks of sensors to function, not only as passive measurement devices, but as intelligent data processing instruments, capable of data quality assurance, statistical synthesis and hypotheses testing as they stream data from the physical environment to the computational world. Specifically, this exploratory project will address the following research components: investigate a programming system that will support the development of in-network data processing mechanisms, and will enable scientific/engineering applications to discover, query, interact with, and control instrumented physical systems using semantically meaningful abstractions; investigate services for active in-network data processing that will generate appropriate data/information to drive novel algorithms for modeling, interpretation and decision making, as well as algorithms for information acquisition with dynamic qualities and properties from streams of data from the physical environment, and explore how applications can control data acquisition; and investigate middleware services for the application-driven dynamic management of sensor systems for physical instrumentation, including overlay sensor system runtime management and adaptation, computation/communication/power tradeoffs and dynamic load-balancing. The proposed research activities will be performed driven and validated with two different applications: (1) subsurface modeling and geo-system management and control and (2) contaminant management and geo-system remediation. A key deliverable of this research will be an experimental infrastructure that will build on the NSF Orbit wireless sensor network testbed at Rutgers University and develop the software infrastructure that will enable scientists and engineers to experiment with different aspects of dynamic data-driven application systems (DDDAS), and will design this simulation environment in close collaboration with the 2 different application groups so that it closely emulates their DDDAS requirements.

A planning meeting will be held to determine if a multi-university Industry/University Cooperative Research Center for Autonomic Computing will be formed at the University of Florida, Rutgers University, and the University of Arizona. The mission of the proposed center is to advance the knowledge of how to design and engineer systems that are capable of running themselves, adapting their resources and operations to current workloads and anticipating the needs of their users.

The center will not only advance the science of autonomic computing, but will also accelerate its transfer to industry by closely working with partners in the definition of projects to be pursued, and contributing to the education of a workforce capable of designing and deploying autonomic computing systems.

Large scientific applications have complex communication needs, including intra-machine point-to-point and global communications, cross-machine checkpointing, and data outputs for online validation and remote data display. Coupled scientific models for multidisciplinary investigations further add to this complexity. This multi-purpose, rich, and dynamic nature of data communications in future exascale codes presents the first challenge addressed by this research.
Further, given the many-core nature of future computer chips and the likely presence of specialized hardware accelerator cores, application-level communications and I/O face an increasingly complex set of on-chip, cross-node, and cross-machine interconnects. The complex nature of the physical communication infrastructures present in future exascale machines is the second challenge addressed by this research. In summary, the problem facing developers of future exascale applications for scientific discovery is how to effectively manage the complexity of their communication needs while protecting their most critical `core' communications from perturbation.

This project will develop higher level, explicit models for the data communications performed in future exascale codes. These models, called C-Models, will describe and implement the communications performed for I/O for purposes of online analysis, storage, and visualization, and for data movements across coupled application codes, and will also capture the interaction of the data movements implied by all of the above with the internal data communications inherent to each single application. This abstraction and encapsulation of communication complexity is key to taming the complexity of future exascale applications. The C-Model infrastructure will also help protect the critical core communication component of these applications from perturbation, helping to maximize the performance of these applications on leadership-class machines.

This award establishes the Industry/University Cooperative Research Center (I/UCRC) for Autonomic Computing at the University of Florida, University of Arizona and Rutgers University. The I/UCRC will focus on multi university research on improving the design and engineering systems that are capable of funning themselves, adapting their resources and operations to current workloads and anticipating the needs of their users. The center will work on improving hardware, networks and storage, middleware, service and information layers used by modern industry.

The research performed at this center is important for U.S. industry to help maintain its lead in the information technology field. This I/UCRC will have a broad impact on the participating students and faculty through involvement with the industrial members. This center has the potential to develop new knowledge in this area that will increase US industrial competitiveness.

Collaborative Research: Computational Models for Evaluating Long Term
CO2 Storage in Saline Aquifers


The key goal of this project is to produce a prototypical
computational system to accurately predict the fate of injected CO2
in conditions governed by multiphase flow, rock mechanics,
multicomponent transport, thermodynamic phase behavior, chemical
reactions within both the fluid and the rock, and the coupling of all
these phenomena over multiple time and spatial scales.
To tackle this grand challenge effort, a multidisciplinary research
team has been assembled of senior researchers M. F. Wheeler,
T. Arbogast, and M. Delshad of the Center for Subsurface Modeling
and I. Duncan from the Bureau of Economic Geology at The
University of Texas at Austin, as well as M. Parashar of
the Applied Software Systems Laboratory at Rutgers University. This
group has expertise in (1) applied mathematics and computational
science that includes multiscale and multiphysics algorithms, solvers,
uncertainty, and optimization (2) computer science
that includes dynamic adaptivity, model/code couplings, and data
management and transport (3) compositional modeling and
CO2 injection processes and (4) CO2 demonstration sites.
In each of the third and fourth years of the project, we will
host a two-day workshop for high school teachers, advanced high school
students, and undergraduate students with an interest in high school
teaching. We will provide training in the use of a sophisticated groundwater
simulator, to be used as a tool to engage and pique the interest of
high schoolers, perhaps leading some to careers in mathematics, the
sciences, and interdisciplinary work. In addition, two postdoctoral
students and roughly two graduate students will be supported
throughout the project.

Geologic sequestration is a proven means of permanent CO2 greenhouse gas
storage, but it is difficult to design and manage such efforts. Predictive
computational simulation may be the only means to account for the lack of
complete characterization of the subsurface environment, the multiple scales of
the various interacting processes, the large areal extent of saline aquifers,
and the need for long time predictions. This proposal will investigate high
fidelity multiscale and multiphysics algorithms necessary for simulation of
multiphase flow and transport coupled with geochemical reactions and related
mineralogy, and geomechanical deformation in porous media to predict changes in
rock properties during sequestration. The work will result in a prototypical
computational framework with advanced numerical algorithms and underlying
technology for research in CO2 applications, which has been validated and
verified against field-scale experimental tests. The multidisciplinary
research team has expertise in (1) applied mathematics and computational
science, (2) computer science and engineering, (3) compositional modeling and
CO2 injection processes, and (4) CO2 demonstration sites.
In each of the third and fourth years of the project, we will
host a two-day workshop for high school teachers, advanced high school
students, and undergraduate students with an interest in high school
teaching. We will provide training in the use of a sophisticated groundwater
simulator, to be used as a tool to engage and pique the interest of
high schoolers, perhaps leading some to careers in mathematics, the
sciences, and interdisciplinary work. In addition, two postdoctoral
students and roughly two graduate students will be supported
throughout the project.

Proposal #: CNS 08-21607
PI(s): Metaxas, Dimitris N.
Glenn, Scott M.; Kremer, Ulrich; Parashar, Manish; Schofield, Oscar
Institution: Rutgers University
New Brunswick, NJ 08901-8559
Title: MRI/Dev.: Dev. of Next Generation Collaborative Underwater Robotic Instrument
Project Proposed:
This project, developing the next generation of Collaborative Underwater Robotic Instrument (CURI), targets empirically-anchored investigations based on the deployment of a semi-Langragian network of biologically inspired autonomous robots. This new instrument consists of a collection of new underwater glider robots, a computer cluster, and monitors for Human-CURI interaction (along with novel hardware and software), capable of exhibiting biologically inspired autonomous cooperative behaviors such as swarming, maneuvering efficiently, sensing, and making decisions. Among others goals, the work aims to be able to track and map a water mass over time and to assess how a water column mixing in ocean water drives local primary productivity over time. CURI, developed as a collaborative effort between the institution and Webb Research (manufacturers of current robotics gliders), will exhibit and allow
- Biologically inspired behaviors such as swarming,
- Decision making (in uncertain conditions) based on the integration of multi-dimensional, multi-scale, and multi-sensory data,
- Human-CURI interaction to help guide the mission goals of the large number of underwater robotic vehicles, and
- Underwater communication among the robots of the CURI based on the implementation of ideas from distributed and adaptive non-fixed topology networks which include middleware, metadata, and low power protocols for underwater communications.
Leveraging significant NSF, ONR, NOAA, USGS, DHS, and other agency investments, CURI will be tested in the linked ecosystems of the densely populated NY-NJ metropolitan area, the Hudson River watershed and estuary, and the adjacent coastal ocean of the Mid-Atlantic, as well as Polar and Tropical environments. The diverse data gathered will provide foundation for computational analysis and modeling.
Broader Impacts:
This development has strong multidisciplinary components that involve control, algorithms, marine science, statistical learning, dynamic systems, human-computer interaction (HCI), and distributed systems. The work is applicable in many areas that involve large-scale, distributed modeling of coordinated behaviors of individual units and their interaction with the environment. Thus, current and future oceanographic applications are expected, including improved modeling of the Coastal Hydrologic Cycle and understanding how a human initiated act such as pollution, global warming, and over-fishing affects the coast, the atmosphere, and ultimately, the quality and security of human life in urbanized environment. CURI will influence the educational program. Courses will be developed and collaboration across disciplines will ensue.

This workshop activity gathers US and India researchers to explore collaboration opportunities in Computational and Data-Enabled Science & Engineering (CDS&E). The overall goal is to eventually establish a virtual institute as a coordinating entity for ongoing research and joint collaborations. The workshop is co-located with the International Conference on High Performance Computing (HiPC) in Bengaluru, India. Intellectual merit and broader impact are noted as in the outcomes that will focus on paths forward for joint research activities and identification of effective models for lasting and powerful collaborative research among US and India.

This project is increasing Rutgers University's capacity to produce highly trained information assurance (IA) professionals by developing new interdisciplinary degree programs at both the graduate and undergraduate levels. A unique aspect of the effort is that it addresses the dependability of the information and information services, as well as the big data and cloud computing infrastructure, in an integrated manner.

Specifically, the investigators are developing three new degree tracks:

(1) A graduate-level interdisciplinary concentration in IA is being created by leveraging the recent creation of a Professional Science Master's Program that offers a Master of Business and Science (MBS) degree. Because it includes business and management training as well as technical training, the MBS degree is particularly well-suited for training current and future professionals to ensure that information assurance is considered at all stages of IT system development and deployment.

(2) An IA track in the MS program offered by the Computer Science Department is being created. This track allows students looking for a traditional CS degree to receive an in-depth education in IA and to obtain a degree that recognizes this specialized training.

(3) IA tracks in the BS programs offered by the Industrial and Systems Engineering (ISE) and Management Science and Information Systems (MSIS) departments are being created. These degrees are well-grounded in their individual disciplines but also expose students to IA courses from other departments to ensure that the students develop a broad interdisciplinary perspective on IA.

The investigators are organizing several workshops to disseminate ideas and results of the curriculum-development activities. They are also hosting a summer camp in IA for high school students, which provides additional outreach as well as a recruitment opportunity.

Solving scientific grand challenges requires effective use of cyber infrastructure. Future computing platforms, including Field Programmable Gate Arrays (FPGAs), General Purpose Graphics Processing Units (GPGPUs), multi-core and multi-threaded processors, and Cloud computing platforms, can dramatically accelerate innovation to solve complex problems of societal importance when supported by a critical mass of sustainable software.

This project will organize scientific communities to help leverage the disruptive potential of future computing platforms through sustainable software. Grand challenge problems in biological science, social science, and security domains will be targeted based on their under-served needs and demonstrated possibilities. Users will be engaged through interdisciplinary workshops that bring together domain experts with software technologists with the goals of identifying core opportunity areas, determining critical software infrastructure, and discovering software sustainability challenges. The outcome will be an in-depth conceptual design for a Center for Sustainable Software on Future Computing Platforms, as part of the Software Infrastructure for Sustained Innovation (SI2) program. The design, scoped toward grand challenge problems, will identify common and specialized software infrastructure, research, development and outreach priorities, and coordination with the SSE and SSI components of the SI2 program. The interactions will offer a comprehensive understanding of grand challenges that best map to future computing platforms and the software infrastructure to best support scientists' needs. The workshops will enhance understanding of future platforms' potential for transformative research and lead to key insights into cross-cutting problems in leveraging their potential. Published results will help guide future research and reduce barriers to entry for under-represented groups.

The application of high performance computing to model subsurface processes occurring over multiple spatial and temporal scales is a science grand challenge that has important implications to society at large. Research on this grand challenge is at the confluence of advanced mathematics, computer science, fluid and solid mechanics and applied probability and statistics. The researchers propose a fresh new perspective by investigating and formulating rigorous error estimators for the numerical schemes employed to model multiphysics, multiscale processes in subsurface media. These error estimators when coupled with advanced computational methods can significantly speed up the task of uncertainty assessment and feedback control of subsurface processes. The uncertainty quantification scheme will use a computer framework that rigorously takes into account the dynamic and complex communication and coordination patterns resulting from multiphysics, multinumerics, multiscale and multidomain couplings. In addition, this project investigates realistic physical models such as carbon sequestration in saline aquifers with real field data from the Cranfield Mississippi demonstration site.

The ultimate transformative goal is to achieve predictive and decisional simulations, in which engineers reliably predict, control, and manage human interaction with geosystems. There are numerous applications that would benefit from a better understanding and integration of porous flow and solid deformation such as surface subsidence, pore collapse, cavity generation, hydraulic fracturing, thermal fracturing, wellbore collapse, sand production and fault reactivation. Moreover, the underlying computational technology will be available to other areas of science and engineering for many other applications. Included are the solver technology, and the algorithms to be developed involving high fidelity numerical simulation of partial differential equations. In order to foster collaboration between researchers with diverse backgrounds and technical expertise, the researchers propose a unique summer research residency program. Under this program the entire team will come together for a two week period during the summer and participate in a series of short courses and seminars presented by the senior researchers on the project. Outreach to high schools is planned through the sequestration training, outreach, research & education (STORE) initiative jointly between the Bureau of Economic Geology, UT Institute of Geophysics and the Center for Petroleum and Geosystems Engineering at UT Austin.

As the scale and complexity of computing and data infrastructures supporting science and engineering grow, power costs are becoming important concerns in terms of costs, reliability and overall sustainability. As a result, it is becoming increasingly important to understand power/performance behaviors and tradeoffs from an application perspective for emerging system configuration, i.e., those with multiple cores, deep memory hierarchies and accelerators. This project builds an instrumented experimental platform that supports such an understanding, and enables research and training activities in this area. Specifically, the proposed experimental platform is composed of nodes with a deep memory architecture that contains four different levels: DRAM, PCIe-based non-volatile memory, solid-state drive and spinning hard disk, in addition to accelerators. Power metering is deployed as part of the infrastructure.

The experimental platform enables the experimental exploration of the power/performance behaviors of large scale computing systems and datacenters as well as compute and data intensive application they support, and uniquely supports research toward understanding the management and optimization of these systems and applications. It also enables research in multiple areas, including: application-aware cross-layer management, power-performance tradeoffs for data-intensive scientific workflows and thermal implications of deep memory hierarchies in virtualized Cloud environments.

Data and compute intensive applications are becoming increasingly critical to a wide range of domains, and the ability to develop large-scale and sustainable platforms and software infrastructure to support these applications will have significant impact in driving research and innovations in these domains. The developed experimental platform enables key research activities to support this. It provides important insights that will impact the realization and sustainability of very large-scale infrastructures necessary for current and emerging data and compute intensive applications. The infrastructure also provides an important infrastructure for education and training in different areas related to power management, energy efficiency, data management, memory management, and virtualization.

Clouds abstractions and infrastructure are rapidly becoming part of the advanced research cyber-infrastructure (ACI) providing viable platforms for scientific exploration and discovery. As a result, it is important to understand how emerging data and compute intensive application workflows can effectively utilize a hybrid ACI integrating Cloud abstractions and services, and how such a hybrid ACI can enable new paradigms and practices in science and engineering. This EAGER explores innovative science and engineering application formulations that are enabled by a hybrid federated ACI that includes Clouds and HPC resources, as well as programming and middleware support for these new application formulations. Specifically, the project focuses on three key research thrusts: (1) application formulation; (2) programming models, abstractions and systems; and (3) middleware stacks and management services, and explore two applications use cases -- (i) an oil reservoir modeling application based on an Ensemble Kalman Filter (EnKF), and (ii) molecular dynamics simulations using asynchronous replica exchange. In each of these use cases activities explore how the capabilities provided by resources and services in a federated ACI can be leveraged to optimize metrics such as time-to-science, cost-to-science and/or energy-to-science.

Cloud services are integral to the NSF ACI vision. Clouds are also rapidly becoming an integral part of the ACI available to science and engineering applications, and provide complementary capabilities that can have a significant impact on a range of applications. As a result, this research can have a significant impact on a diverse set of application domains by identifying new paradigms and practices that can make effective use of a hybrid ACI to accelerate science. Furthermore, the results of this research will provide resource providers information about how to best meet the needs of science and engineering applications and how current ACI can achieve broader accessibility and higher efficiencies and productivity. The development of human resources, including the training of students, researchers and software professions, as well as outreach to minorities and underrepresented group, is integral to all aspects of this effort.

A Scalable Data Management Abstraction for Large-scale Coupled Simulation Workflows

Coupled scientific simulation workflows, integrating multiple physics and scales and running at very large scales on high-end resources, have the potential for achieving unprecedented levels of accuracy and providing dramatic insights into complex phenomena. However, the coupled component of these simulation workflows need to interact and exchange significant amounts of data at runtime, and the data often has to be transformed as it flows from source to destination. As the volumes and generation rates of this data grow, the costs (latencies and energy) associated with extracting this data and transporting it for coupling, transformation and analysis have become the dominating overheads and are dictating the level of performance and productivity that can be achieved.

The goal of this project is to address these challenges and to develop conceptual solutions as well as a software framework that can enable the large-scale data-intensive simulations. Our approach is based on the premise that given the large data volumes and associated costs, data will have to be largely processed online, ?in-situ? and ?in-transit? while it is staged using resources within the computational platform, and the programming and runtime system must provide abstractions and mechanisms that facilitate such data processing. Our effort is organized around three key research thrusts: (1) Programming abstractions for in-situ/in-transit data management; (2) Design and implementation of a scalable data staging substrate; and (3) Data-centric mapping and scheduling.

Data and compute intensive simulations are becoming increasingly critical to a wide range of science and engineering domains, and as a result, this research has the potential to drive research and innovations in these domains. The developed framework and benchmarks also provide computer scientists with a substrate to experiment with and explore data-centric research. The development of human resources, including the training of students, researchers and software professions, as well as outreach to minorities and underrepresented group, is integral to all aspects of this effort.

Advanced computing and data cyberinfrastructure (ACI) is playing an increasingly important role in all areas of computational and data-enabled science and engineering (CDS&E), and has become a key enabler of scientific insights and innovation. As a result, it is critical that researchers and students have access to adequate campus ACI (e.g., computing and data resources). However, it is also critical that such a campus ACI aligns and is integrated with national initiatives, to connect with and share advanced computing/data resources and expertise, enable collaborative and end-to-end science, and build upon and leverage existing federal investments in high end infrastructure. This EAGER focus on three main thrusts: (1) develop the expertise and experience at Rutgers for establishing a campus ACI for CDS&E research, and to align it with the national ACI by integrating this campus ACI as part of a national resource, i.e., Open Science Grid (OSG); (2) develop the necessary research partnerships based on the OSG federated ACI to address important research problems in an end-to-end manner and lead to significant insights; and (3) document processes, experiences, and lesson learnt during this process to share these findings with the broader community, providing a "cook- book".

Establishing campus ACI ecosystems is a common practice that Universities follow in order to support their research and education missions. However, these campus ACI ecosystems typically operate in isolation, restricting the access to resources and therefore the potential impact that they can offer to the community beyond their own campuses. The outcomes of this research will provide important insights into the feasibility of leveraging national investments to create resource pools where the whole is greater than the sum of its parts. As a result, it can have significant impact on the mindset of faculty, campus IT, and administrators, and on the overall ability of campus IT groups to support the advanced computing needs of researchers. The resulting models for collaboration will be seeded through shared ACI to incentivize resource and expertise sharing that can enable faculty and students to leverage a much larger and more robust set of resources. It will also help foster the creation of necessary support teams to support science and scholarship.

The WSSSPE 2 workshop is part of a community driven effort to address the many new challenges facing development, deployment and maintenance of reusable software for academic research and education. These challenges include the software development process, the support and maintenance of software, governance and business models particularly for sustainability, the role of software in building science communities, the need for software to lead to science which is verifiable and reproducible, policy issues such as how to measure software impact, choose licensing, or assign software credit, and the education and nurturing of early stage researchers in scientific software.
The workshop is building upon a successful WSSSPE 1 meeting at SC13 in Denver in November 2013; the organizers expect over 60 submitted papers and around 150 attendees at the WSSSPE 2 meeting that is anticipated to be held at SC14 in New Orleans in November 2014. As with WSSSPE 1, WSSSPE 2 will provide an open and dynamic forum for the community to discuss experiences and approaches in the areas listed above. This proposal requests funds to support attendance at WSSSPE 2 for US based researchers, particularly targeted at encouraging attendance by early stage researchers with attention to diversity.

Natural fractures act as major heterogeneities in the subsurface that controls flow and transport of subsurface fluids and chemical species. Their importance cannot be underestimated, because their transmissivity may result in undesired migration during geologic sequestration of CO2, they strongly control heat recovery from geothermal reservoirs, and they may lead to induced seismicity due to fluid injection into the subsurface. Advanced computational methods are critical to design subsurface processes in fractured media for successful environmental and energy applications. This project will address the following key BIG data and computer science challenges: (1) Computation of seismic wave propagation in fractured media; (2) BIG DATA analytics for inferring fracture characteristics; (3) High Performance Computation of flow and transport in fractured media; and (4) Integration of data from disparate sources for risk assessment and decision-making. This will enable design of technologies for addressing key societal issues such as safe energy extraction from the surface, long-term sequestration of large volumes of greenhouse gases, and safe storage of nuclear waste. The project will provide interdisciplinary training for a team of graduate students and postdoctoral fellows. Outreach to high schools teachers and minorities through a planned workshop will inspire interest in environmental green-engineering, mathematics, and computational science. Numerous applications will benefit from this research, including Computer and Information Science and Engineering (CISE), Geosciences (GEO), and Mathematical and Physical Sciences (MPS).


The proposed research will emphasize high performance computation (HPC) approaches for characterizing fractures using large subsurface seismic data sets, BIG data analytics for extraction of fracture related information from seismic inversion results and long-duration dynamic data, and advanced computational approaches for modeling flow, transport, and geomechanics in fractured subsurface systems. The specific objectives are to: Develop an efficient forward modeling algorithm for seismic wave propagation in fractured media using efficient computational schemes. Compute flow and transport in fractured media using an efficient computational scheme implemented on GPUs such as mimetic finite differences. Perform efficient multiphysics simulation of flow and geomechanics in fractured media. Integrate information from time-lapse seismic inversion and flow/transport simulation using novel statistical schemes. Joint inversion of seismic and fluid flow data and uncertainty quantification using efficient computational schemes. Develop and deploy a scalable hybrid-staging based substrate that can support targeted workflows using staging-based in-situ/in-transit approaches. Computational simulation is critical to design subsurface processes for successful environmental and energy applications. Project URL: http://csm.ices.utexas.edu/current-projects/

This project develops a virtual data collaboratory that can be accessed by researchers, educators, and entrepreneurs across institutional and geographic boundaries, fostering community engagement and accelerating interdisciplinary research. A federated data system is created, using existing components and building upon existing cyberinfrastructure and resources in New Jersey and Pennsylvania. Seven universities are directly involved (the three Rutgers University campuses, Pennsylvania State University, the University of Pennsylvania, the University of Pittsburgh, Drexel University, Temple University, and the City University of New York); indirectly, other regional schools served by the New Jersey and Pennsylvania high-speed networks also participate. The system has applicability to a several science and engineering domains, such as protein-DNA interaction and smart cities, and is likely to be extensible to other domains. The cyberinfrastructure is to be integrated into both graduate and undergraduate programs across several institutions.

The end product is a fully-developed system for collaborative use by the research and education community. A data management and sharing system is constructed, based largely on commercial off-the-shelf technology. The storage system is based on the Hadoop Distributed File System (HDFS), a Java-based file system providing scalable and reliable data storage, designed to span large clusters of commodity servers. The Fedora and VIVO object-based storage systems are used, enabling linked data approaches. The system will be integrated with existing research data repositories, such as the Ocean Observatories Initiative and Protein Data Bank repositories. Regional high-performance computing and network infrastructure is leveraged, including New Jersey's Regional Education and Research Network (NJEdge), Pennsylvania's Keystone Initiative for Network Based Education and Research (KINBER), the Extreme Science and Engineering Discovery Environment (XSEDE) computing capabilities, Open Science Grid, and other NSF Campus Cyberinfrastructure investments. The project also develops a custom site federation and data services layer; the data services layer provides services for data linking, search, and sharing; coupling to computation, analytics, and visualization; mechanisms to attach unique Digital Object Identifiers (DOIs), archive data, and broadly publish to internal and wider audiences; and manage the long-term data lifecycle, ensuring immutable and authentic data and reproducible research.

This project will enhance an experimental datacenter for sustainable computing. Datacenters consume vast amounts of energy, totaling about 1.8% of the US electricity usage in 2014. Thus, the energy efficiency, energy-related costs, and overall sustainability of datacenters are critical concerns. NSF funded an experimental green datacenter called Parasol, which has previously demonstrated that the combination of green design and intelligent software management systems can lead to significant reductions in energy consumption, carbon emission, and cost. The enhanced version of this project will update energy sources, network technologies and management software.

Running real experiments in live conditions using Parasol led to findings that were not possible in simulation. This proposal seeks to update and enhance Parasol with current and next generation power-efficient servers, improve network connectivity and integrate software-defined networking (SDN) and Wi-Fi capabilities, increase solar energy generation capacity, add a low emission fuel cell power source, diversify energy storage, and improve the cooling system to advance green computing. The investigators will update and enhance Parasol's current software stack for monitoring, programmatic control, and remote access for the new hardware enhancements. Specific research goals are resource management in green datacenters, including coordinated workload, cooling, and energy scheduling against environmental and load variability to maximize the benefits of green datacenters and to help improve grid power management. A specific goal is to leverage accelerators such as GPUs and deep learning hardware, which promise excellent performance/watt ratios.

NSF large facilities are major science and engineering research platforms that serve whole research communities. Cyberinfrastructure (CI) comprises advanced computing, data and software infrastructure, networking, related cybersecurity, and related training and workforce development -- all also designed to broadly serve the research community. NSF facilities are increasingly depending on CI -- including that within a facility as well as existing CI resources and capabilities located outside the facility. This award supports the conduct of a National Science Foundation Large Facilities Cyberinfrastructure Workshop in Alexandria, Virginia on September 6 and 7, 2017. The workshop has the overall goal of enabling direct and synergistic interactions among the NSF large facilities community and the cyberinfrastructure (CI) community to jointly address the CI needs and sustainability of existing and future large facilities.

Objectives of the workshop include developing a common understanding of current and evolving large facilities CI architectures, design and operational practices, issues and gaps; and identifying common CI requirements, solutions and opportunities for interoperability and sharing elements across facilities. Desired outcomes include disseminating practices that can aid future large facilities in developing and sustaining CI; enabling CI developers to most effectively target facility needs; exploring means for continuing and sustaining the community conversation and activities initiated at the workshop; and generating input that can inform future NSF planning for CI-related programs. The report from the workshop will be publically posted, and this activity is anticipated to have broad impacts in advancing the needs of large scale science and engineering research.

Open, large-scale scientific facilities are an essential part of science and engineering enterprise. These facilities provide shared-use infrastructure, instrumentation, and data products that are openly accessible to a broad community of researchers and/or educators. Current facilities provide increasing volumes of data and data products that have the potential to deliver new insights in a wide range of science and engineering domains. However, while these facilities provide reliable and pervasive access to the data and data products, users typically must download the data of interest and process them using local resources. Consequently, transforming these data and data products into insights requires local access to powerful computing, storage, and networking resources. On the other hand, the NSF Advanced Cyberinfrastructure (ACI) is playing an increasingly important role as an open platform for computational and data-enabled science and engineering and can provide the necessary capabilities to allow a broad user community to effectively process the data in large facilities. However, despite clearly complementing each other, large scientific facilities and NSF ACI remain largely disconnected. As a result, users are forced to actively be part of the process that moves data from large facilities to local computational resources or NSF ACI. Therefore, this data-delivery mode becomes inefficient and limits the potential utility that the data would have if processed in an automatic manner. The outcome of this research can have a significant impact on the scientific and engineering community by improving the accessibility of data and the way scientists interact with both data sources and computational infrastructures. Bringing national ACI and large scientific facilities together will democratize access to science and improve the impact of the NSF-funded infrastructure. This is especially important for small public institutions that have limited resources and do not have high bandwidth Internet connection to the Academic/Research network. The development of human resources, including the training of students, researchers and software professionals, as well as the outreach to minorities and underrepresented groups, will be an integral aspect of this effort. The project uses an open repository to disseminate research papers, prototype implementations, and associated data products to the community.

The goal of this project is to explore how NSF-funded ACI, such as the Extreme Science and Engineering Discovery Environment (XSEDE), can be integrated with large facilities generally, and the Ocean Observatories Initiative (OOI) specifically, in an automated manner to support end-to-end user workflows. Specifically, we propose to enable workflows that when triggered can seamlessly orchestrate the entire data-to-discovery pipeline. This involves executing queries on the OOI cyberinfrastructure (possibly based on the occurrence of events of interest), streaming data to appropriate ACI facilities using high bandwidth interconnects (such as Internet2) in order to stage this data close to computing/analytics resources (e.g., XSEDE JetStream), and then launching the modeling and analysis processes to transform such data into insights. In this way, the project will leverage high-performance networks that typically connect these facilities to support data movement, and process this data using state-of-the-art high-performance systems.

The increasing demands of science and engineering applications push the limits of current large-scale systems, and is expected to achieve exascale (10^18 FLOPS) performance early in the next decade. One of the lesser studied challenge at extreme scales is the reliability of the computing system itself, primarily due to the very large number of cores and components utilized and to the sharp decrease of the Mean Time Between Failures on such systems (in the order of tens of minutes). This project departs from the traditional single component fault management model, and explores how multiple software libraries (and application components) used in the context of a single parallel application can interact to provide the holistic fault management support necessary for parallel applications targeting capability computing. This exploration will not be limited to software developed using a single parallel programming paradigm, but will be extended to encompass the more challenging case where multiple programming paradigms can be combined to achieve a common goal, to simulate a set of large scale scientific applications in use today. 

The goal of this project is to depart from the current siloed resilience mechanisms, and propose cross-layer composition solutions that can fundamentally address these resilience challenges at extreme scales. This exploration will not be limited to software developed using a single parallel programming paradigm, but will be extended to encompass the more challenging case where multiple programming paradigms can be combined to achieve a common goal, to simulate a set of large scale scientific applications in use today. More specifically, this proposal will address the following research challenges: (1) development of a theoretical foundation for a deeper understanding of the challenges and opportunities arising from combining different resilience models and methodologies; (2) design of a flexible programming abstraction to allow different resilience models and mechanisms to be combined to cooperate and address resilience in a more holistic manner; and (3) development of basic, programming paradigm independent, constructs necessary to implement cross-layer and domain-specific approaches to support resilience and to understand related performance / quality trade-offs. The proposed approach will be validated by exposing these generic abstractions in two different programming paradigms (MPI and OpenSHMEM), by creating and developing specialized concepts for each of these paradigms. This will enable the assessment of the validity of the concepts and the corresponding overheads imposed by the different software layers, using few software frameworks and applications.

Engaging students in premiere scientific conferences is extremely important as we seek to increase participation in STEM and to identify and develop the next generation of scientists by engaging students in premier scientific conferences. The International Symposium on Cluster, Cloud and Grid Computing (CCGrid) is a successful series of conferences that serves as the major international forum for presenting and sharing recent research results and technological developments in the fields of Cluster, Cloud and Grid computing. The CCGrid conference series has been emphasizing a comprehensive experience for students, to better prepare students attending the conference for their professional career. The CCGrid program provides a comprehensive set of events and activities that can expose students to the state of the art in research and technologies, as well as to the leading researchers and thinkers in the field. The student program is a critical aspect of CCGrid and is anchored in this technical program and exposes students to the state of the art in research and technologies, as well as to the leading researchers and thinkers in the field. Traditionally, students have participated in all aspects of this program, including being authors and presenters on papers and posters, presented demos, and been competitors in the SCALE challenge. Additionally, the CCGrid program includes the annual CCGrid Doctoral Symposium, which provides students researchers with visibility, mentoring, and valuable feedback for their research. Participating in the conference will provide students with valuable opportunities to improve their overall research skills and planning, and thus aligned with NSF's overall mission of promoting science and advancing national prosperity.

This project will support the participation of US-based students in CCGrid 2017. Supported students will present papers or posters describing their ongoing research to the conference audience, participate in the student program and related activities, discuss their research with experts from academia and industry during the conference, and make useful contacts. Travel grants will encourage the research interests and the involvement of students in the field who are not well funded and those who are just beginning their participation in the field or are interested in entering it. Particular effort will be made to solicit applications for the travel support from female students and students from under-represented communities.