INFOSYMBIOTIC SYSTEMS The Power of Dynamic Data Driven Application Systems The Power of Dynamic Data Driven Application Systems Report of a Workshop held at Arlington VA August 30-31, 2010 Funded by AFOSR xxxx, NSF yyyy Table of Contents EXECUTIVE SUMMARY ........................................................................................................................... 3 1. INTRODUCTION AND MOTIVATION ............................................................................................ 5 1.0 DDDAS INFOSYMBIOTIC SYSTEMS ............................................................................................................... 5 1.2 WHAT NATIONAL AND INTERNATIONAL CRITICAL CHALLENGES REQUIRE DDDAS CAPABILITIES? .7 1.3 WHAT ARE THE SCIENCE AND TECHNOLOGY CHALLENGES AND WHAT ONGOING RESEARCH ADVANCES ARE NEEDED TO ENABLE DDDAS? ...................................................................................................9 1.4 WHAT KINDS OF PROCESSES, VENUES AND MECHANISMS ARE OPTIMAL TO FACILITATE THE MULTIDISCIPLINARY NATURE OF THE RESEARCH NEEDED IN ENABLING SUCH CAPABILITIES? ............... 11 1.5 WHAT PAST OR EXISTING INITIATIVES CAN CONTRIBUTE, AND WHAT NEW ONES SHOULD BE CREATED TO SYSTEMATICALLY SUPPORT SUCH EFFORTS? ............................................................................. 11 1.6 WHAT ARE THE BENEFITS OF COORDINATION AND JOINT EFFORTS ACROSS AGENCIES, NATIONALLY AND IN SUPPORTING SYNERGISTICALLY SUCH EFFORTS? ................................................................................ 12 1.7 WHAT KINDS OF CONNECTIONS WITH THE INDUSTRIAL SECTOR CAN BE BENEFICIAL? HOW CAN THESE BE FOSTERED EFFECTIVELY TO FOCUS RESEARCH EFFORTS AND EXPEDITE TECHNOLOGY TRANSFER? ............................................................................................................................................................. 12 1.8 HOW CAN THESE NEW RESEARCH DIRECTIONS BE USED TO CREATE EXCITING NEW OPPORTUNITIES FOR UNDERGRADUATE, GRADUATE AND POSTDOCTORAL EDUCATION AND TRAINING? ........................... 13 1.9 WHAT NOVEL AND COMPETITIVE WORKFORCE DEVELOPMENT OPPORTUNITIES CAN SUE?............. 13 2. ALGORITHMS, UNCERTAINTY QUANTIFICATION, MULTISCALE MODELING & DATA ASSIMILATION ....................................................................................................................................... 13 2.1 DYNAMIC DATA ASSIMILATION …………………………………………………………………………………………12 2.2 LARGE SCALE MODELING………………...……………………………………………………….12 2.3 UNCERTAINTY QUANTIFICATION (UQ) AND MULTISCALE MODELING …… 14 2.4 KEY CHALLENGES ........................................................................................................................................... 17 3. BUILDING AN INFRASTRUCTURE FOR DDDAS....................................................................... 19 3.2 EXISTING INFRASTRUCTURE ........................................................................................................................ 19 3.3 DYNAMIC RESOURCE MANAGEMENT ......................................................................................................... 20 3.4 RESEARCH NEEDS .......................................................................................................................................... 21 4. SYSTEMS SOFTWARE ...................................................................................................................... 24 4.1 DDDAS & SYSTEMS SOFTWARE ................................................................................................................. 24 4.2 PROGRAMMING ENVIRONMENT .................................................................................................................. 25 4.3 AUTONOMOUS SYSTEMS & RUNTIME APPLICATIONS SUPPORT ............................................................ 25 5. SUMMARY OF FINDINGS AND RECOMMENDATIONS ........................................................... 27 WORKS CITED ........................................................................................................................................ 28 APPENDIX A APPLICATIONS ............................................................................................................. 29 A.1 DYNAMIC DATA-DRIVEN COMPUTATIONAL INFRASTRUCTURE FOR REAL-TIME PATIENT-SPECIﬁC LASER TREATMENT OF CANCER ......................................................................................................................... 29 Executive summary Over the last decade the Dynamic Data Driven Applications Systems (DDDAS) paradigm has put forward a vision of dynamic integration of simulation with observation and actuation, in a feed-back control loop, engendering new scientific and engineering capabilities; thus, this vision, coupled with other recent disruptive technological and methodological drivers and advances such as the advent of ubiquitous sensoring capabilities and multicore systems, is poised to transform all areas where information systems impact human activity. Such integration can impact and transform many domains, including critical infrastructure, defense and homeland security and mitigation of natural and anthropomorphic hazards. Two recently released studies, the Air Force Technology Horizons Report and the National Science Foundation Foundation CynerInfrastructure for the 21st Century (CF21) Report, put forth visions for science and technology that highlight the need for such integration of sensing, data, modeling and decision making. The challenges for enabling the capabilities envisioned by DDDAS were articulated from the outset, starting with the 2000 DDDAS Workshop, and advances needed along several research directions, namely in applications modeling under conditions of dynamic data inputs streamed into the executing model, in algorithms that are stable under perturbations from dynamic data inputs, interfaces of executing applications models with observation and actuation systems, and support for the dynamic execution requirements of such environments; it was also recognized that efforts in these directions needed to be pursued in the context synergistic, multidisciplinary research. Such efforts for enabling the DDDAS vision have started under governmental support, and progress has been made, together with the increasing recognition of the power of the DDDAS concept. However, before such dynamic integration can be created and supported in robust ways, further efforts needed to fully address the challenges articulated above. Moreover it has also become recognized that, while seeding-level and some limited collaborative government sponsorship has been highly fruitful, the multiple scientific (in computing, networks and systems software, large and streaming data, error and uncertainty, sensor networks and data fusion and visualization) that need to be overcome, require sustained and systematic support. Surmounting these challenges requires multi-disciplinary teams and multi-agency sponsorship at stable and adequate levels to sustain the necessary extended and extensive inquiry over the next decade. . In this report … InfoSymbiotic Systems and InfoSymbiotics - the power of Dynamic Data Driven Applications Systems … Essentialcharacteristics of DDDAS environments are the dynamic nature of the data streamed into the application, typically large-scale and complexity of the applications, and the analysis and likely feedback mechanisms. Ensemble Kalman/particle filters, methods for non-Gaussian dynamical systems, large scale parallel solution methods and tools for deterministic and stochastic PDEs like those encapsulated in the PeTSc library and stochastic Galerkin/collocation methods, new algorithms for large-scale inverse and parameter estimation problems and advances in large-scale computational statistics and high-dimensional signal analysis are enabling application of DDDAS to many realistic large scale systems. Key challenges remain in integrating the loop from measurements to predictions and feedback for highly complex systems, dealing with large, often unstructured and streaming data and complex new computer architectures, developing resource aware and resource adaptive methodology and application independent algorithms for model analysis and selection. Infrastructure capable of supporting DDDAS need to support complex, intelligent applications using new programming abstractions and environments able to ingest and react to dynamic data. Components of the infrastructure include sensors, actuators, resource providers or decision makers. Data flows among them may be streamed in real-time, historical, filtered, fused, or metadata. Research challenges include architecture to support the complex and adaptive applications, data and networks, tools to manage the workflows and execution environments and integration and interoperability issues. Test beds (hardware and software) are needed for advancing methodology and theory research. Systems software must evolve to support DDDAS components need to execute on heterogeneous platforms with widely varying capabilities fed by real-time sensing. Algorithms and platforms must evolve symbiotically to effectively utilize each other’s capabilities. Research challenges in systems software remain in runtime systems support to support program adaptation, faulttolerance, new retargeting compilers that can generate efficient code from a high level mathematical or algorithmic description of the problem and rapid proliferation of heterogeneous architectures. 1. Introduction and Motivation 1.0 DDDAS InfoSymbiotic Systems The core ideas of vision engendered by the dynamic data driven application systems (DDDAS) concept have been well articulated and illustrated in a series of workshop reports and research projects presentations (Douglas and Darema, DDDAS Report 2000, Douglas and Darema, DDDAS Report 2006, Douglas 2000) and Series of International DDDAS Workshops – www.dddas.org ). InfoSymbiotic Systems embody the power of the DDDAS paradigm, where data are dynamically integrated into an executing simulation to update or augment the application model and conversely the simulation steers the measurement (instrumentation and control) process. Work on DDDAS supported through seeding has accomplished much but the confluence of technological and methodological advances in the last decade has produced added opportunities for integrating simulation with observation and actuation, in ways that can transform all areas where information systems impact human activity. Starting with the NSF 2000 DDDAS Report, efforts for enabling the DDDAS vision have commenced under governmental support, in the form of seeding-level projects and a 2005 across agencies proposal solicitation. Under this initial support, progress has started to be made, together with the increasing recognition of the power of the DDDAS concept. The 2005 NSF Blue Ribbon Panel characterized DDDAS as visionary and revolutionary concept. The recently enunciated National Science Foundation vision for Cyberinfrastructure for the 21st Century (CF21) (NSF 2010) lays out “a revolutionary new approach to scientific discovery in which advanced computational facilities (e.g., data systems, computing hardware, high speed networks) and instruments (e.g., telescopes, sensor networks, sequencers) are coupled to the development of quantifiable models, algorithms, software and other tools and services to provide unique insights into complex problems in science and engineering.” The DDDAS-IS paradigm is well aligned and enhances this vision. Several task forces set up by NSF have also reported back with recommendations reinforcing this thrust. In a similar if more focused and futuristic vision the recent Technology Horizons Report developed under the leadership of Dr. Werner Dahm, as Chief Scientist of the Air Force, declares that “Highly adaptable, autonomous systems that can make intelligent decisions about their battle space capabilities … making greater use of autonomous systems, reasoning and processes ...developing new ways of letting systems learn about their situations to decide how they can adapt to best meet the operator's intent” are among the technologies that will transform the Air Force 20 (“10+10”) years from now (Technology Horizons 2010, Dahm 2010). In essence a DDDAS is one in which data is used in updating an executing simulation and conversely simulation outcomes are used to steer the observation process. Capitalizing on the promise of the DDDAS concept, a workshop was convened to address further opportunities that can be pursued and derived from DDDAS-IS approaches and advances. The Workshop was attended by over 100 representatives from academia, government and industry and co-sponsored by the Air Force Office of Scientific Research and the National Science Foundation explored these issues on Aug., 30-31, 2010. The Workshop was organized into Plenary Presentations and Working Groups sessions, and outbriefs of the Working Groups. The plenary presentations addressed several key application areas, addressing the impact for new capabilities enabled through DDDAS, progress made by researchers in advancing several research areas contributing towards enabling DDDAS capabilities for the particular application at hand. Prior to the workshop, a number of questions had been developed by the workshop co-chairs together with the working groups co-chairs and participating agencies program officials. The working group discussions addressed these questions posed to the attendees, as well as items that were brought-up during the discussions. This report summarizes the deliberations at and subsequent to the workshop. In the first chapter of this report are addressed key questions related to new opportunities, key challenges and impacts in pursuing research on DDDASIS. Subsequent chapters are organized around the questions posed to the participants of the address more specific issues related to research on algorithms and dynamic data assimilation, uncertainty quantification, data management, systems software and supporting cyberinfrastructure that DDDAS-IS environments entail. 1.1 Why is now the right time for fostering this kind of research? 1.1.1. Scale and Complexity of Natural, Engineered and Societal Systems: The increase in both complexity and degree of interconnectivity of systems, including natural and engineered systems, large national infrastructure systems (“smart grids”) such as electric power delivery systems, and threat and defense systems has provided unprecedented capabilities, yet at the same time this complexity has added fragility to the systems, and the interconnectivity across multiple systems has tremendously increased the impact of cascading effects across the entire set systems of even small failures in a subset of any of the component systems. This new Modern electric reality has led to the need for more adaptive analysis of power grids use systems, with methods that go beyond the static modeling and complex control simulation methods of the past, to new methods such as those systems to guide that can benefit from augmenting the system models through power production monitoring and control/feedback aspects of the systems, thus from distributed creating the need for DDDAS approaches for designing and energy sources managing such systems. In this report we state this are today’s and distribute complex systems are DDDAS or InfoSymbiotic in nature. While power more preliminary efforts in DDDAS (such s those created through the efficiently, yet new 2005 cross agencies DDDAS Program Solicitation) brought vulnerabilities advances in DDDAS techniques in some applications before arise that may (including for example for management and fault-tolerant allow massive electric power-grids), many current systems have complexity power outages and dynamicity in their state space, that make the use of DDDAS illustrating the approaches essential and imperative. complexity and fragility. 1.1.2: Algorithmic Advances A second factor that favors acting now is the advance in a number of algorithms that enable DDDAS technologies, including non-parametric statistics that allows us inference for non-Gaussian systems, uncertainty quantification and advances in numerics of schocastic differential equations (SDEs), parallel forward/inverse/adjoint solvers, smarter data assimilation (that is: dynamic assimilation of data with feed-back control to the observation and actuation system), math-based programming languages and hybrid modeling systems. Simulations of a system are becoming synergistic partners with observation and control (the “measurement” aspects of a system). 1.1.3 Ubiquitous Sensors A third factor is the increasing ubiquity of sensors – low cost, distributed intelligent sensors have become the norm. Some, like phone geo-location information and instruments in automobiles, are paid for and already in place, collecting and/or transmitting data without the user’s knowledge or involvement. There are tradeoffs between data and bandwidth, but in general there is a flood of data that needs to be filtered, transferred to applications that require the data, and possibly partially archived. 1.1.4 Transformational Computational Capabilities A fourth factor is the disruptive transformation of the computing and networking environment with multicore/manycore chips, heterogeneous architectures like GPUs, cloud computing, and embedded computing leading to an unparalleled levels of computing at minimal cost. Network bandwidths have also undergone transformative advances – for e.g. the ESNET network of the DOE advertises the ability to transfer 1TB in less than 8 hour (Dept. of Energy 2010). Commercial networks expect to provide 100Gbps in the near future (Telecom 2010). In summary, new technology advances drive the world and research has to stay ahead of trends. We are in the midst of a leap-frogging phenomena due to simultaneous changes in sensors, data collection and analysis, networking and computing. These create new platforms and environments for supporting the complex systems of interest here providing further motivation for embarking on comprehensive efforts for creating DDDAS capabilities 1.2 What National and International critical challenges require DDDAS capabilities? National and international critical challenges that need DDDAS capabilities include the Big Data problem advancing weather and climate prediction technology mitigation, forecasting, and response to natural hazards (hurricanes/typhoons, floods, wildland fires, tornadoes, thunderstorms, and other severe weather) homeland and national security detection of network intrusions transportation (surface, sea, rail, air) water management environmental monitoring protection of space assets critical infrastructure like power delivery systems, reinvigorating longstanding power sources (nuclear), current issues with the power grid, and renewable energy (e.g., solar, water, and wind power) searching in visualizations medical and pharmaceutical applications – cancer treatment, surgery treatments, pill identification and delivery, misuse of medications, and gene and proteomics industrial applications – manufacturing, medical, aerospace, telecommunications, information technology/computer industry DDDAS has direct application for decision-making in anti-terrorism, homeland security and real battlefields. For example, in real, dense, cluttered battlefields with fixed and moving objects, myriad of sensor types (radar, EO/IR, acoustic, ELINT, HUMINT etc.) that need to be fused in real time. It contains deluge of data like video data which is uncorrelated with radar, SIGNIT, HUMINT and other non-optical data. Thence, Lt. Gen. Deptula stated ``swimming in sensors and drowning in data’’. These data are incomplete, with errors and needs to be optimally processed to give unambiguous and target state vectors, including time. As another example in civilian critical infrastructure environemnts,the recent oil spill in the Gulf of Mexico showed the need of better predictions of the spread of the oil in order to take more effective mitigating actions, and moreover address the issue of the aftermath which created a new problem, that of determining the residual oil and its locations. The observations of residual oil involve a large set of heterogeneous sources of data, from satellites and to physical inspection and ocean water sampling, measurements that are dynamic in nature as well as at different scales requiring data fusion to combine the data. Similar autonomous river basin simulation multi-scale data fusion, physical and surgical control management. We are in the midst of a leapfrogging phenomena due to simultaneous changes in sensors, data collection and analysis, networking and computing. applications exist in problems with vehicles, protection of space assets, real-time management, structural health monitoring, assisted surgery, space weather prediction/modeling with swarm of satellite, dynamic gene expression and proteomics intelligent search machines for searching in virtual environments, image-guided real-time and in production planning and supply chain 1.3 What are the Science and Technology Challenges and what ongoing research advances are needed to enable DDDAS? 1.3.1 Cyberinfrastructure Advances in the mathematical modeling, algorithms and understanding errors and uncertainty invoke additional pressures on the need for efficient infrastructure (e.g., operating at scale, with concomitant increase in failures at all levels and failsafe implementation requirements). Multiple coordination strategies in the infrastructure in a single DDDAS is essential to ensure successful results. The infrastructures for DDDAS need to support complex, intelligent applications using new programming abstractions and environments able to ingest and react to dynamic data. Different infrastructures will be needed for different application types. National, persistent DDDAS infrastructure connecting new Petascale compute resources via 100 Gbps networks to special purpose data devices could support a range of large scale applications. Easily deployable and reliable systems will be needed to be deployed over ad-hoc networks in the field to support medical, military, and other applications operating in special conditions. The majority of researchers operating in university and national or industrial laboratories will require DDDAS systems that securely connect external data sources to institutional and distributed resources. 1.3.2 Dataology and Big Data A new definition of what is data needs to be developed. Digital, analog, symbolic, picture, and computational data are just the beginning of things that encompass data. A whole new field called Dataology is being developed, both in academia and industry. There have been impressive recent advances in commercial and academic capabilities for the Big Data problem (Berman 2010). However efficient, scalable, robust, generalpurpose infrastructure for DDDAS has to address the Big Data problem (particularly for Clouds and Grids) as well the Dynamic Data problem – characterized by either (i) spatialtemporal specific information, (ii) varying distribution, or (iii) data that can be changed in some way, e.g., either operated upon in-transit or by the destination of data so that the load can be changed for advanced scheduling purposes. The Big Data problem is now a near catastrophe. Sensors streaming data and supercomputers generate vast amounts of data. In some cases, nothing ever uses the stored data after archiving. It is imperative that means be developed to dynamically handle this flood of data so that train-loads of disk drives and tapes are not wasted and that the computations are useful. Annotating data with ontologies is one approach so that data and models are matched. By identifying multiple models, different ones can be compared to see which ones are better in the same context. 1.3.3. Streaming Data Typical algorithms today deal with persistent data, but not streaming data. New algorithms and software are needed for streaming data that allow on the fly, situation-driven decisions about what data is needed now and to reconfigure the data collection sensors in real-time to push or pull in more useful data (rather than just pull in more data). The granularity, modality, and field of view should all be targeted. The data mining part of a DDDAS requires similar advances, too. Data security and privacy issues frequently arise in the data collection and must be addressed. Smart data collection means faster results that are useful. 1.3.4 Error and Uncertainty Data integrity and quality are essential to DDDAS. Uncertainty quantification (UQ) is mathematical field that has made great strides in recent years and now positioned to lead improvements in DDDAS. Data in applications is essentially worthless unless the error in the data is known or estimated well. Both systematic and random errors in the data must be found, identified, and catalogued. There is a cost for using UQ, which must be part of an optimization process of time versus quality of results. Reducing the quantity of data is essential. This comes back to the Big Data problem. We need to develop a formal methodology and software for general problems to specify what is important in data (i.e., data pattern recognition through templates or some other system) and what to do when something important is found along with a measure of uncertainty. Reducing redundancy and describing data by features instead of quantity is essential. A common method in game stations is that only the data changes are transmitted, not whole scenes. Similar strategies need to be developed for DDDAS. Scaling the models computationally to reduced data means faster results. 1.3.5 Sensor Networks and Data Fusion Searching and discovering sensors and data must become a simple function, both algorithmically and by software. Different stakeholders will benefit from the ability to detect content on the fly and to couple sensor data with domain knowledge. Fusing data from multiple sensors and models dynamically will have to be developed that is on demand, context dependent, actionable, and fast. A good example is identifying when someone is stressed in a manner that would be of interest to homeland security at transportation or building sites. New strategies are needed for sensor, computing, and network scheduling. Scheduling should be quasi-optimal, intelligent and automatic, similar to what is expected when using a batch system on a supercomputer. Where, when, and how to do the processing must be decided so that data can be delivered and reconfigured, models changed, and symbiotically make the DDDAS work. Where and how include locally, centrally, distributed geographically through networks, or some combination. When and how include now or later and must evaluate if the results are critical in nature or not. 1.3.6 Visualization Very large-scale data visualization is an area of interest in DDDAS. What is now visualized in a CAVE environment will be visualized in a few years using flat panel screens. Already there is a new area of research in 3D visualization on power wall that does not require special glasses. Where a person stands to see in 3D is dependent on features of the person’s eyes. Since people are different, a modest number of people can see the material together by standing in different locations. More research in this area is needed and will make DDDAS more useful. Tools are needed to disambiguiate semantic non-orthogonality in data and models (time, space, resolution, fidelity, science, etc.). We need to also bridge the gap between the differential rates of innovation in data capture, computation, bandwidth, and hardware. 1.4 What kinds of processes, venues and mechanisms are optimal to facilitate the multidisciplinary nature of the research needed in enabling such capabilities? Numerous processes, venues and mechanisms exist to facilitate the multidisciplinary nature of the research. Multidisciplinary, cross-directorate programs sometimes including participation with other agencies have been extremely popular, leading to overwhelming response of proposals from the research community, initiated crossdisciplinary teams. The NSF IGERT program is an opportunity to educate students in multidisciplinary education. A challenge is to establish stable, long-term funding on a regular basis. An advance in the past five years has been more journals and networking venues for presenting multidisciplinary research results, some specifically for DDDASrelated work. 1.5 What past or existing initiatives can contribute, and what new ones should be created to systematically support such efforts? Past and existing initiatives such as DDDAS, ITR, CDI, and IGERT can contribute to facilitating the multidisciplinary nature of the research. These have largely been discontinuous programs, enabling continuity of nucleated collaborations remains a challenge. The vast response of researchers to the calls for proposals have led to a low success rates (below 5%) highlighting the need for further significant continuous investment from other agencies. Long term continuity is particularly important for DDDAS teams, as each problem requires the participation of people from multiple fields – a domain/application specialist, computer science specialist, and an algorithm specialist. Several academy reports + interdisciplinary report + Academy report 2000 + new NSF report each make the point that interdisciplinary projects require a longer gestation/spin up period/incubation, because a necessary part of spin up for each project has been seen to be developing ability to communicate across fields, they cannot get 5 disparate people together and produce results in a few months. This issue is expected to continue as DDDAS projects advance to the point where they ‘close the loop’, as previous funding linked at least 2 of several components needed for a DDDAS system, more mature projects encompass the breadth of more problems. 1.6 What are the benefits of coordination and joint efforts across agencies, nationally and in supporting synergistically such efforts? There are numerous benefits of coordination and joint efforts across agencies, nationally, and in supporting synergistically such efforts. Mission-oriented agencies can provide well-defined problems, clarity on the specific decision information needed, feedback, access to key datasets, sensors, or personnel (who may need this formal partnership, even through a Memorandum of Understanding) to spend time on these interactions, leading to higher impact results. Moreover, buy-in from agencies as one of several sponsors of a project, leads to ownership in the result. Finally, sponsorship across agencies contributes to continuity and stability of funding. In recent years, there have been several initiatives from various funding agencies to support research related to various components of DDDAS. These include the ITR, CDI, and CPS programs of NSF, the PSAAP program of DOE, and UQ MURI of AFOSR. DOE also had calls on multi-scale research and a recent UQ program. Some of the NIH RO1’s have certain flavor of multi-scale and data-driven research. There is a need for direct DDDAS specific calls. Finally, having multiple agencies involved will foster the creation of new research fields that will lead to new industries, jobs, wealth creation, and tax revenues as a payback. New tools will be created after integrating tools from divergent fields that normally would not work together. Solving these types of new problems is only possibly by integrating computer science, mathematics, and statistics with researchers from the application areas. 1.7 What kinds of connections with the industrial sector can be beneficial? How can these be fostered effectively to focus research efforts and expedite technology transfer? Connections with the industrial sector can be beneficial and can be fostered effectively to focus research efforts and expedite technology. Some obvious partners include energy sector, manufacturing, medical, aerospace, telecommunications, information technology/computer industry. The immediate effect could be another source for funding for research as part of partnerships with academia - sponsorship creates ownership that enhances interest and participation in research and results. Participation in such joint workshops and other methods to enhance communication and exchange of information are recommended. 1.8 How can these new research directions be used to create exciting new opportunities for undergraduate, graduate and postdoctoral education and training? These new research directions can be used to create exciting new opportunities for undergraduate, graduate, and postdoctoral education and training by providing exciting multidisciplinary problems that can excite students and draw them into this field. This work educates people who bridge academia and industry, providing better employment flexibility and opportunities than one specific program. These programs are helping universities modernize and adapt to the interconnected, complex environment. It is creating new alliances within departments, connecting alliances between national laboratories, universities, and industry, nurturing relationships that may endure as the students graduate and seek employment. DDDAS projects help universities modernize, creating new programs. They reinvigorate departments with interdisciplinary programs that create links between departments. 1.9 What novel and competitive workforce development opportunities can ensue? Novel and competitive workplace development opportunities can evolve from this field. An example is adult education programs to retrain analysts for DDDAS problems. This work educates people who bridge academia and industry, providing better employment flexibility and more opportunities than just one specific program. This program will provide training to achieve multidisciplinary workers by creating new curriculum and degrees in fields such as UQ, bio-engineering, and HPC. It will also foster collaboration between federal labs and universities. Graduate fellowships and REU programs on DDDAS are necessary. This way it will create multidisciplinary researchers who will be indispensable in government, industry, and academia. 2. Algorithms, Uncertainty Quantification, Multiscale Modeling & Data Assimilation Algorithms for integration of measurements, models, towards predictions and feedback mechanisms are Findings & Recommendations a key 1. Disruptive technological and methodological advances in the last decade have component of produced an opportunity to integrate observation, simulation and actuation in DDDAS ways that can transform all areas where information systems impact human activity. Such integration will transform many domains including critical technologies. infrastructure, defense and homeland security and mitigation of natural and Essential characteristics anthropomorphic hazards. 2. Many challenges in cyberinfrastructure (computing, networks and software), large of DDDAS and streaming data, error and uncertainty, sensor networks and data fusion and visualization have to be overcome. 3. Surmounting these challenges needs multi-disciplinary teams and multi-agency sponsorship at stable and adequate levels to sustain extended and extensive inquiry over the next decade. environments are the dynamic nature of the data flow, large-scale and complexity of the applications, and the analysis and potential feedback mechanisms. The primary challenges are the development of integrated DDDAS systems and closing the loop from measurements to feedback mechanisms and decision-making. We will now examine these in the context of three major areas – data assimilation, uncertainty quantification and multiscale modeling. *- 2.1 Dynamic Data Assimilation In data assimilation, ensemble Kalman/particle filters and variational methods have found their way into operational weather prediction codes and their use has been adopted in other application areas, such as hydrology, chemical transport and dispersion, and discrete event simulation wildfire models. Furthermore, research activities in filtering methods for non-Gaussian dynamical systems have intensified significantly in the last decade though it is still an open field of research. 2.2 Large Scale Modeling In modeling and simulation, parallel solvers for partial differential equations have allowed rapid solution of problems with billions of unknowns. This enables us to consider applying real-time DDDAS approaches to very complex phenomena that involve multiple spatial and temporal scales. Furthermore, libraries like Deal.II, Trilinos, and PETSc provide frameworks for the discretization and scalable solution of new applications. Similar advances have been realized in software tools for optimization solvers, which are a critical component of DDDAS technologies: from denoising data, to solving estimation problems we need to solve different flavors of optimization problems (least squares, mixed-integer programs, or nonlinear programs). Open source tools like DAKOTA, APPSPACK, and IPOPT have enabled the solution of very complex problems. …. And … Advances in algorithms for large-scale inverse and parameter estimation problems will enable DDDAS technologies to unprecedented scales. Although theory and basic algorithms for inverse problems is a very mature field, the emphasis has been on theory and numerics for small-scale problems and there was very little in large-scale parallel algorithms with optimal convergence properties. However, in the last decade significant breakthroughs in adjoint/Hessian-based, matrix-free large-scale Newton methods, regularization methods, adaptive methods, and multigrid preconditioners for compact operators have enabled solution of large scale inverse problems with millions of unknown parameters using thousands of computer cores. It remains to be seen if the methods can be extended to 1,000 to 1,000,000 times the size of current problems. …. And … 2.3 Uncertainty Quantification (UQ) and Multiscale Models Most, if not all, existing research efforts on UQ and multi-scale modeling are on the models and algorithm design, achieving significant progress. Yet, the availability of data presents new challenges and opportunities. This is mostly due to the fact that sensors and hardware for data acquisition are becoming increasingly cost effective. Consequently the size of data is exploding in many fields, and in many cases, real-time data are abundantly available. This presents a unique opportunity to conduct datadriven UQ and multi-scale analysis: real-time analysis by integrating computation and sensors, efficient decision-making and optimization utilizing the growth of networking capability, and much more. In addition to the existing difficulties in UQ and multi-scale modeling, the incorporation of data introduces new challenges. For example, the majority of real-time data is nonGaussian, and often multi-modal. In this area the traditional stochastic analysis and UQ study are severely lacking. The ability to handle and process large amount of high dimensional data is also insufficient. Additionally, there are extreme data, not necessarily of small/rare probability, that are difficult to analyze and to predict in the current framework. These unique difficulties can intertwine with the existing difficulties in UQ and multi-scale modeling and significantly increase the research challenges. However, it must be recognized that the presence of data also presents a unique opportunity to address the existing difficulties in UQ and multi-scale modeling. Most notably, one of the major goals of UQ and multi-scale modeling is to produce high fidelity predictions of complex systems. As these predictions are of modeling and simulation nature, observational data, though often corrupted by noises, are also (partially) faithful reflections of the systems under study. Therefore it is natural to combine the two kinds of reflections, simulation-based and measurement-based, to achieve more reliable and accurate predictions of the systems. In the broader context of UQ, one of the persistent challenges is the issue of long-term integration. This refers to the fact that stochastic simulations over long-term may produce results with large variations that require finer resolution and produce larger error bounds. Though none of the existing techniques is able to address the issue in a general manner, it is possible to apply the DDDAS concept of augmenting the model through on-line additional data injected into targeted aspects of the phase-space of the model in-order to reduce the solution uncertainty by utilizing data. Other notable challenges include effective modeling of epistemic and aleatory uncertainties, particularly epistemic uncertainty where few studies exist. In multi-scale modeling, a major challenge is to determine and validate models at different scales and their interfaces. Since most, if not all, multi-scale models are problem specific, it is crucial to utilize observational data to effectively quantify the validity of the models and to conduct model selection. It must be recognized that data may arrive from different sources at different scales. Thus successful analysis and integration of such data into the modeling and decision-making process is crucial. The main and unique challenges of DDDAS research hinge on real-time setting. These include uncertainty fusion of both simulation and observational data in dynamic systems, design of low-dimensional and/or reduced order models for online computing, decision-making and model selection under dynamic uncertainty. To address these challenges, we need to take advantage of the existing tools in UQ and multi-scale modeling. Notable tools include generalized polynomial chaos methodology for UQ, Bayesian analysis for statistical inference and parameter estimation (particularly to develop efficient sampling methods as standard Markov Chain Monte-Carlo (MCMC) does not work in real time), filtering methods (ensemble Kalman filter, particle filter, etc) for data assimilation, equation-free, multi-scale finite element methods, scalebridging methods for multi-scale modeling, sensitivity analysis for reduction of the complexity of stochastic systems, etc. These methods have been widely used. And their capabilities need to be extended to DDDAS domain, especially in the context of incorporating real-time data. And their properties need to be thoroughly examined. Equally important is the need to develop new tools for UQ and multi-scale modeling of DDDAS. For example, methods for adaptive control of complex stochastic and multiscale systems, efficient means to predict rare events and maximize model fidelity, methods for resource allocation in dynamic settings, tools to reduce uncertainty (if possible) and mitigate its impact. Major advances have taken place in numerical methods for large-scale stochastic differential equations. In particular, stochastic Galerkin and collocation methods have been studied and applied to forward uncertainty propagation, uncertainty estimation for inverse and control problems, adaptive methods for non-Gaussian random fields, and data assimilation methods. Once again, it is unknown if the methods work for the much larger problems that is interesting in the DDDAS context. Furthermore, advances in large-scale computational statistics and high-dimensional signal analysis are enabling tackling of complex uncertainty estimation problems. New algorithms in large-scale kernel density estimation algorithms, on reduced order models and model reduction, interpolation on high-dimensional manifolds, multiscale interpolation, and manifold discovery for large sample sizes are examples of major breakthroughs in the last decade. …. And … Developments in computing hardware have at the same time enabled solution of complex problems in real-time and created opportunities and needs for novel algorithms and software tools. Such developments include accelerators like GP-GPUs, embedded chips, cloud computing, and Petaflop-scale HPC platforms. …. And … 2.4 Key challenges Integrating the loop from measurements to predictions and feedback for highly complex applications with incomplete and possibly low quality data. Dealing with new architectures (on embedded, cloud, manycore, and HPC platforms). We need new distributed/parallel, fault-tolerant algorithms. We need better resource aware/resource adaptive algorithms. How do we make sure we can make sense from Terabytes of data? Such data often do not come from well-planned experimental protocols. We need more efficient new Bayesian inversion methods for uncertainty estimation to do inversion on the larger data sets needed. Inverse methods for streaming data with robust algorithms for incomplete highly corrupted data; We will be doing high-dimensional sampling in constrained manifolds, which is an open area of research. We currently have many HPC and manycore algorithms and libraries for existing tools that are not scalable. This needs to be addressed. Algorithms for model analysis and selection (model error, model verification validation, model reduction for highly-nonlinear forward problems, data-driven models) still need more research to create an application independent formulation. Test beds and DDDAS software tools for workflows must be available in open source format. Novel assimilation for categorical/uncertain data (graphical models, SVMs) must be developed. Theoretical analysis for DDDAS for bilinear and hybrid systems. Discrete, combinatorial algorithms must be scaled up to be useful on Exaflopscale computers before the computers exist. For stochastic systems and multi-scale systems, it is crucial to: determine the quantity of interest (QoI), develop tools and metrics to validate models at different scales and the aggregation procedures to link them under uncertainty, conduct model selection at different scales, quantify the relative importance of models at different scales, and calibrate the “actions” and interfaces between scales under uncertainty. In particular, for problems such as social network modeling where physical models (e.g., those based on conservation laws) do not exist, these issues become even more challenging. Decision-making and resource allocation methods with dynamic data on different scales will make significant impact in DDDAS. (#3. Below: Key challenges remain… Findings & Recommendations 1. Essential characteristics of DDDAS environments are the dynamic nature of the data flow, large-scale and complexity of the applications, and the analysis and potential feedback mechanisms. 2. Ensemble Kalman/particle filters and filters for non-Gaussian dynamical systems, large scale parallel solution methods and tools for deterministic and stochastic PDEs like those encapsulated in the PeTSc library and stochastic Galerkin/collocation methods, new algorithms for large-scale inverse and parameter estimation problems and advances in large-scale computational statistics and high-dimensional signal analysis are enabling application of DDDAS type ideas to many realistic large scale systems. 3. Key challenges remain in integrating the loop from measurements to predictions and feedback for highly complex systems, dealing with large, often unstructured and streaming data and complex new computer architectures, developing resource aware and resource adaptive methodology and application independent algorithms for model analysis and selection. 4. Test beds (hardware and software) are needed for advancing methodology and theory research. 3. Building an Infrastructure for DDDAS (the) 3.1 Adaptive & Intelligent Infrastructure DDDAS connects real-time measurement devices and special purpose data processing systems with distributed applications executing on a range of resources from mobile devices operating in ad-hoc networks to high end platforms connected to national and international high-speed networks. Supporting infrastructure for these environments must go beyond static computational grids and include the instrumentation systems (integrated and autonomous components that ingest data and drive adoption at all levels). Here such instrumentation components can be sensors, actuators, resource providers or decision makers. Data can be streamed in real-time, orfrom archival storage, and can be filtered, fused, or metadata. Adaptation can be applied at all levels such as choosing resources or mediating between data sources. Infrastructures for DDDAS need to support complex, intelligent applications using new programming abstractions and environments able to ingest and react to dynamic data. Different infrastructures will be needed for different application types. National, persistent DDDAS infrastructure connecting new Petascale and beyond compute resources via 100+ Gbps networks to special purpose data devices could support a range of large-scale applications. Easily deployable and reliable systems must be deployed over ad-hoc networks in the field to support medical, military, and other applications operating in special conditions. The majority of researchers operating in university, national, or industrial laboratories will require DDDAS systems that connect external data sources to institutional and distributed resources. General infrastructure for DDDAS is thus seen as focusing on extensible, general application-agnostic capabilities. 3.2 Existing Infrastructure In thinking about a future DDDAS infrastructure it is important first to review the existing landscape. Broadly speaking, DDDAS applications can be seen as operating in the following environments: High Performance Computing Resources: The NSF TeraGrid and planned XD and Blue Waters facilities are prime targets for DDDAS. Such environments are targeted at high-end users with the highest levels of concurrency. Usage of these resources is typically highly contested by scientists whose research agendas are dependent on CPU cycles. Traditionally, policy restrictions have hindered the broad and regular use of shared HPC environments for DDDAS applications (e.g., through the use of static batch queues), although supercomputing centers are now beginning to embrace the new demands of data intensive science. High Throughput Computing Resources: Open Science Grid. Research Testbeds: Experimental environments to support DDDAS computing are available at different levels of production use. The NSF sponsored Global Environment for Network Innovations (GENI) provides exploratory environments for research and innovation in emerging global networks, the EAVIV Testbed project provides a dynamically configurable network testbed that provides high speed connectivity connected end-to-end with TeraGrid resources. More recently, the NSF Future Grid is being deployed to allow researchers to tackle complex research challenges in computer science related to the use and security of grids and clouds. Cloud Computing: Cloud computing is an emerging infrastructure that builds upon recent advances in virtualization and data centers at scale to provide an on-demand capability. There are both commercial clouds (EC2, Azure,and IBM Deep-Cloud) and academic clouds (DoE Science-Cloud and NSF FutureGrid) that are viable infrastructure for DDDAS applications. They provide different models for data transfer, localization, and data affinity. An open question is how different data capabilities needed for DDDAS interact in different on-demand cloud computing environments. 3.3 Dynamic Resource Management Infrastructure will need to address myriad issues arising from diverse, dynamic data from different sources. Integrating sensors into the DDDAS infrastructure will necessitate rethinking network architectures to support new protocols for push-based data, and two-way communication to configure sensors. Data in the DDDAS infrastructure will be stored and accessed in new hierarchies based on locality, filtering, quality control, and other features. Underlying hardware needs to be elastic and able to respond to dynamic requirements. Persistent national infrastructure is envisioned, as well as infrastructure that is portable and that can be quickly deployed in the field to support medical, military, and other application scenarios. End user connectivity must be addressed, connecting national infrastructure to researchers in academic laboratories as well as to mobile users and devices in the field. Infrastructure itself thus needs to be dynamically configurable. A fundamental need for end resources supporting DDDAS, whether storage, compute, network, or data collecting, is that they support dynamic provisioning which is flexible, adaptive, and fine grained. This issue involves both technical developments (e.g., the ION dynamic network protocols) along with appropriate policies to allow dynamic use of resources. Production resources focused on CPU utilization have the technologies to provide dynamic use, but their usage models do not typically allow for dynamic usage policies. Once dynamic behavior is provided at all levels of the infrastructure the question becomes how can resources be provisioned and used by applications and middleware. A common definition is needed to describe the quality of service (QoS) provided by the resource. This description needs to include the capabilities provided by the resource (e.g., bandwidth, memory, and available storage) along with usage characteristics (e.g., cost, security, reliability, and performance). Requirements for DDDAS systems overlap with known needs for many complex end-to-end scientific applications. Additional fundamental requirements are introduced to support dynamic data scenarios, such as the ability to handle events, and the integration of temporal and spatial awareness into the system at all levels necessary to support decision-making. Systems need to react swiftly and reliably to deal with faults and failure to provide a guaranteed quality of service. Autonomic capabilities are important at all levels to respond to the content of dynamic data or changing environments. The need for autonomic capabilities arises at many levels of DDDAS. For example, wherever dynamic execution and adaptivity is required – models and algorithms, the software and systems services, infrastructure capabilities – autonomic capabilities (such as behaviors based upon planning and policy) provide an effective approach to manage the adaptations and mechanics of dynamical behavior. In many DDDAS scenarios, application workflows need to be dynamically composed and enacted based on real-time data and changing objectives. An example includes an instrumented hurricane modeling, which can achieve efficient and robust control and management of diverse model by dynamically completing the symbiotic feedback loop between measured data and a set of computational models. 3.4 Research Needs Research is needed to provide persistent and fully featured infrastructure, integrating frameworks, programming abstractions, and deployment methods into an overall architecture, developing common APIs and schemas around which powerful tools can be provided, providing methods for decomposing applications to take advantage of emerging environments such as Clouds or GP-GPUs in an integrated infrastructure, and deploying persistent DDDAS infrastructure for research and production use. Specific research challenges include: Architecture Application scenarios, characteristics, and canonical problems to drive infrastructure research and development. Network architectures to support new protocols for sensor data (push, pull, and subscribe). Architecture of data hierarchy for dynamic data processing and access. Integration of location and time awareness. Tools Dynamic workflow tools building on above capabilities (unique demands: run time environment with changing services, event controlled workflows, discovery, etc.). Visualization, analysis and steering of large and dynamic data (e.g., haptics) for closed loop scenarios, real-time data, and changing characteristics. Security issues for o sensors and autonomy and o generally for new software. Execution environment supporting collaboration and decision making (social networking), crowd-sourcing, and citizen engineering. Integration and Interoperability Findings and Recommendations 1. Infrastructures for DDDAS need to support complex, intelligent applications using new programming abstractions and environments able to ingest and react to dynamic data. 2. Components of the infrastructure include sensors, actuators, resource providers or decision makers. Data flows among them may be streamed in real-time, historical, filtered, fused, or metadata. 3. Research challenges include architecture to support the complex and adaptive applications, data and networks, tools to manage the workflows and execution environments and integration and interoperability issues. How to define, carry, and operate on provenance information. Generalized interoperability, collaboration, and negotiation in decentralized decision-making. Generalization of allocation across different resources (networks, data, etc.) combined with new methodologies of allocation. Negotiation mechanisms between applications and infrastructure. Description for QoS (includes cost, availability, security, performance, and reliability). More effective integration of computable semantics throughout the infrastructure (e.g., tradeoff between simplicity and expressiveness). Policies/cost models for dynamic resource allocation and contention (e.g., for different applications). Integration with cloud computing to take advantage of business models and scalability and collaboration, virtualization, and mutual collaboration between cloud computing and DDDAS. 4. Systems Software 4.1 DDDAS & Systems Software In the context of DDDAS, systems software involves specification languages, programming abstractions and environments, software platforms, and execution environments, including runtimes that stitch together dynamically reconfigurable applications. Given the vast diversity of DDAS application areas, platforms of interest encompass the range from distributed and parallel systems to mobile and/or energy efficient platforms that assimilate sensors inputs. Core DDDAS components by definition have evolved from executing on static platforms with fixed inputs to executing on heterogeneous platforms with widely varying capabilities fed by real-time sensing. Algorithms and platforms must evolve symbiotically to effectively utilize each other’s capabilities. Algorithmically, we need to develop along three axes in a complementary manner: Specification languages that can be used to define the performance characteristics of algorithms. Methodologies for algorithms to adapt to changing resource availability or heterogeneity resource availability. Methodologies for algorithms to change behavior predictably, based on data and control inputs. Similarly, advances are need in execution platforms to support dynamically adapting applications. Platforms capabilities and interfaces need to be extended to include: Interfaces to define and specify the performance characteristics of the execution platform. Ability to reallocate resources in response to the changing needs of algorithms. DDDAS algorithms stress dynamicity – symbiotically, DDDAS platforms should expose interfaces that enable applications to sense and respond to resource availability. Interfaces that expose control inputs and monitoring of DDDAS application behavior to ensure their observability and controllability. 4.2 Programming Environment A programming environment consists of programming abstractions, interfaces that support co-development of components, and runtime systems that handle the nonfunctional requirements of DDDAS applications. A core challenge in dynamically adapting algorithmic components lies in developing one or more programming abstractions that simplify the process of decomposing and reasoning about such compositions, particularly as execution platforms are rapidly evolving from homogenous collections of processors to include GP-GPUs, FPGAs, and application specific accelerators. Furthermore, DDDAS applications are frequently composed from components that model different domain physics with multiple interface boundaries. Software abstractions are needed that aid the coupling between components of a DDDAS application without tying the interfaces to programming languages. Over the last few years, the rapid proliferation of heterogeneous architectures presents new challenges in developing efficient algorithmic components without tying them to an underlying platform. Resolving this issue requires new retargeting compilers that can generate efficient code from a high level mathematical or algorithmic description of the problem. Such compilers will be instrumental in enabling just in time compilation based on specific deployment decisions. Non-functional requirements such as fault tolerance and adaptivity continue to be challenges. Given the range of DDDAS applications from soft real-time to best effort, a range of fault tolerance methods are needed that provide performance fault tolerance under resource constraints. DDDAS applications can provide guidelines based on the criticality of the computation and data that can guide the methods used to achieve fault tolerance. Anticipatory fault tolerance should be investigated for time-critical applications. 4.3 Autonomous Systems & Runtime Applications Support New runtime systems support is needed to support program adaptation with the goal of achieving a desired application level quality of service (QoS). This work involves developing methods to determine the delivered quality of service, interfaces to determine available resources, and runtimes that support program adaptivity. Adaptivity is needed at multiple levels of granularity – for processes, threads, and a suite of communicating processes. New data abstractions must be developed that are dynamic and hidden from the user/programmer that adapts to different hardware configurations that can change over the course of a DDDAS. Having to rewrite a code to take advantage of a hardware accelerator like a GP-GPU is unacceptable and currently wastes too much time, particularly of recent Ph.D.s and graduate students. Just in time compilers should be intelligent enough to do the optimizations automatically. System management tools like computational steering have been used in the past that are quite complicated and require significant resources. Improvements are needed so that steering can be achieved using smart phones and iPad-like devices that are becoming more and more like small, but very effective computers. We must use the symbiotic relationship between applications and systems more effectively. This includes interfaces and knowledge bases that enable applications to monitor and manage their environment and vice-versa, with or without a human in the loop. The system needs to be easy enough to use so that alerts can be devised by nonprogrammers, i.e., actual DDDAS users. In fact, what we really need is to develop and use a set of best practices for DDDAS as a Findings and Recommendation 1. Systems software must evolve to support DDDAS components need to execute on heterogeneous platforms with widely varying capabilities fed by real-time sensing. Algorithms and platforms must evolve symbiotically to effectively utilize each other’s capabilities. 2. Research challenges in systems software remain in runtime systems support to support program adaptation, fault-tolerance, new retargeting compilers that can generate efficient code from a high level mathematical or algorithmic description of the problem and rapid proliferation of heterogeneous architectures. major thrust in future research. I 5. Summary of Findings and Recommendations 1. Disruptive technological and methodological advances in the last decade have 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. produced an opportunity to integrate observation, simulation and actuation in ways that can transform all areas where information systems impact human activity. Such integration will transform many domains including critical infrastructure, defense and homeland security and mitigation of natural and anthropomorphic hazards. Many challenges in computing, networks and software, large and streaming data, error and uncertainty, sensor networks and data fusion and visualization have to be overcome. Surmounting these challenges needs multi-disciplinary teams and multi-agency sponsorship at stable and adequate levels to sustain the necessary extended and extensive inquiry over the next decade. Essential characteristics of DDDAS environments are the dynamic nature of the data flow, large-scale and complexity of the applications, and the analysis and potential feedback mechanisms. Ensemble Kalman/particle filters, methods for non-Gaussian dynamical systems, large scale parallel solution methods and tools for deterministic and stochastic PDEs like those encapsulated in the PeTSc library and stochastic Galerkin/collocation methods, new algorithms for large-scale inverse and parameter estimation problems and advances in large-scale computational statistics and high-dimensional signal analysis are enabling application of DDDAS to many realistic large scale systems. Key challenges remain in integrating the loop from measurements to predictions and feedback for highly complex systems, dealing with large, often unstructured and streaming data and complex new computer architectures, developing resource aware and resource adaptive methodology and application independent algorithms for model analysis and selection. Test beds (hardware and software) are needed for advancing methodology and theory research. Infrastructures for DDDAS need to support complex, intelligent applications using new programming abstractions and environments able to ingest and react to dynamic data. Components of the infrastructure include sensors, actuators, resource providers or decision makers. Data flows among them may be streamed in real-time, historical, filtered, fused, or metadata. Research challenges include architecture to support the complex and adaptive applications, data and networks, tools to manage the workflows and execution environments and integration and interoperability issues. Systems software must evolve to support DDDAS components need to execute on heterogeneous platforms with widely varying capabilities fed by real-time sensing. Algorithms and platforms must evolve symbiotically to effectively utilize each other’s capabilities. Research challenges in systems software remain in runtime systems support to support program adaptation, fault-tolerance, new retargeting compilers that can generate efficient code from a high level mathematical or algorithmic description of the problem and rapid proliferation of heterogeneous architectures. Works Cited Berman, F. Sustainable Economics for a Digital Planet . Blue Ribbon Task Froce on Data, http://brtf.sdsc.edu/biblio/BRTF_Final_Report.pdf, 2010. Dahm. 2010. http://www.af.mil/news/story.asp?id=123213717 (accessed November 10, 2010). Dept. of Energy. ESnet Network Performance Knowledge. November 10, 2010. http://fasterdata.es.net/ (accessed November 10, 2010). Douglas. C. 2000. www.dddas.org (accessed September 2010). Douglas, C., and F. Darema. DDDAS Report. Arlington, VA: NSF, 2000. Douglas, C., and F. Darema. DDDAS Report. Arlington VA: NSF, 2006. NSF. Cyberinfrastructure Vision for the 21st Century. 2010. http://www.nsf.gov/pubs/2010/nsf10015/nsf10015.pdf (accessed November 10, 2010). Technology Horizons. Technology Horizons. Air Force, 2010. Telecom, Fierce. FierceTelecom 2010 Prediction: Forget 40 Gbps, I want 100 Gbps Read more: FierceTelecom 2010 Prediction: Forget 40 Gbps, I want 100 Gbps FierceTelecom http://www.fiercetelecom.com/story/fiercetelecom-2010-predictionforget-40-gbps-i-want-100-gbps/2010-01-03#ixzz14xt5q4X2 Subscribe: http://www.fiercetelecom.com/signup?sourceform=Viral-Tynt-FierceTelecomFierceTelecom. January 3, 2010. http://www.fiercetelecom.com/story/fiercetelecom-2010-prediction-forget-40gbps-i-want-100-gbps/2010-01-03 (accessed November 10, 2010). Appendix A Applications A.1 Dynamic Data-Driven Computational Infrastructure for Real-Time PatientSpeciﬁc Laser Treatment of Cancer The objective of this research project was to develop a dynamic data-driven infrastructure to seamlessly integrate high-performance computing with imaging feedback for optimal planning, monitoring, and control of laser therapy for the treatment of prostate cancer. The project involved the development of computational models of bioheat transfer and cell damage for the prediction of tumor ablation during treatment as well as the set-up of a real-time feedback control system based on the integration of observations from magnetic resonance imaging (MRI) and MRTI (T for temperature) devices at M.D. Anderson Cancer Center in Houston and ﬁnite element simulations performed at The University of Texas at Austin, all connected by a high-bandwidth network, as shown in Figure 1. The laser source for ablation of the cancerous region was thus controlled by a model-based predictive control system with near real-time patient-speciﬁc calibration using MRTI imaging data. The research outcome successfully demonstrated a viable proof-ofconcept for a dynamic data-driven application but still fell short of being fully operational, as the simulations and optimization loops were all treated deterministically and did not include the various sources of uncertainties in the data Figure1: Schematic of the dynamic and in the computer models. Including the data-driven infrastructure for real- uncertainty quantiﬁcation within the process time patient-speciﬁc laser treatment will certainly enhance the usefulness of the of prostate cancer. results for critical decision-making. For illustration, Figure 2 shows a threedimensional computer representation of a canine prostate in the pretreatment stage. The anatomical MRI data has been color-rendered to illustrate the geometries of the diﬀerent anatomic organs. In the treatment stage, a stainless steel stylet (applicator) is used to insert the laser catheter, which consists of a diﬀusing-tip silica ﬁber within a water-cooled catheter. Figure 3 shows images describing the delivery of energy during therapy. In particular, the ﬁgure illustrates two important features of the computer modeling of laser therapy. The top picture of the ﬁgure displays the temperature ﬁeld obtained from thermal imaging data at a given time of the therapy. The bottom pictures of the ﬁgure show the predicted temperature ﬁelds in a region of a canine prostate containing a laser supplying energy through a catheter using an uncalibrated model of homogeneous tissue properties (left) and a calibrated model that takes into account heterogeneous heat-transfer properties of the tissue (right). Excellent quantitative agreement is attained between the temperature ﬁelds predicted by the calibrated model and those detected by MRTI. This example shows that calibration of the mathematical model for bio-heat transfer, followed by a validation of the model, represent key features for predictive modeling of the thermal environment in the tissue and should be essential components of a dynamic data-driven infrastructure. Fgure 2: Three-dimensional computer Figure3: Temperature predictions using either representation of a canine prostate in the pretreatment stage. Uncalibrated homogeneous or calibrated heterogeneous model parameters of the tissue properties.