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CCRI: ENS: Chipyard: University of California-Berkeley

Krste Asanovic

[email protected]

Advanced computing systems lie at the heart of many innovative products, from intelligent earbuds to autonomous vehicles, and these are increasingly built as customized systems-on-a-chip (SoCs). Customized SoCs incorporate a complex mix of general-purpose and customized processing logic, and both software and hardware must be highly tuned to achieve the needed performance and energy efficiency for the target application. Chipyard combines and extends existing community infrastructure components to provide a rich unified framework for research into SoC architecture and implementation, supporting activities ranging from research into new software and new architecture simulation techniques all the way down to test chip fabrication.<br/><br/>Chipyard is an integrated SoC design, simulation, and implementation environment to support research and development of specialized computing systems required to meet new application demands in the face of the slowdown in technology scaling. Chipyard is based around the widely used Rocket Chip SoC generator, which includes RISC-V processors, coherent caches, interconnect, and other IP blocks written in the Chisel HDL. Due to the widespread adoption of RISC-V in both academia and industry, there is extensive software support both in upstream open-source software projects as well as increasingly from commercial software providers. Chisel has a growing community, with a series of Chisel Community Conferences and multiple commercial tapeouts of Chisel-based designs. Chipyard IP modules can also be imported from legacy HDLs. RTL designs are converted into a common intermediate representation, FIRRTL, which supports powerful circuit transformations and experimentation with new hardware design tools. For fast, accurate simulation, Chipyard generates FireSim cloud-FPGA-accelerated simulators. FireSim can simulate entire datacenter racks at the RTL level with only a 100X slowdown. FireSim includes performance monitoring, analysis, and debugging tools to allow high observability of design behavior while running at high simulation speed. Chipyard also includes FireMarshal, a software workload management system that allows complete workloads to be easily packaged and recompiled to match an SoC configuration and execution environment. Chipyard integrates the Hammer modular physical design flow, which supports plugins for different tool chains and process technologies, and automates many tapeout steps. Chipyard will also integrate the Berkeley Analog Generator for mixed-signal and analog blocks. Overall, Chipyard provides an integrated environment where a single SoC description can be used to drive conventional open-source or commercial software RTL simulators, or pushed all the way to GDSII layout using industry-standard CAD tools and/or open-source ECAD tools once available. This proposal will fund further development of Chipyard capabilities, managed releases, and community engagement and outreach.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting: Virginia Polytechnic Institute and State University

Cayelan Carey

[email protected]

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software ? FLARE: Forecasting Lake And Reservoir Ecosystems ? that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.<br/><br/>The FLARE forecasting system is novel in its architecture, as it integrates a software-defined virtual distributed infrastructure spanning resources from sensor gateway devices at the edge of the network to cloud computing and storage. FLARE will support the flexible deployment of software in close proximity to water quality sensors in lakes and reservoirs, and in cloud resources for end-to-end data acquisition and processing. FLARE interconnects its distributed resources through a virtual private network to ensure data integrity and privacy in communications, and supports a flexible model applicable across a variety of lakes and reservoirs. Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, widely-used open-source software frameworks in a manner that lowers the barrier to the deployment and management of ecological forecasting workflows by ecologists. Importantly, this project?s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forecasts will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other ecosystems. This project will build on and expand an existing program for cross-disciplinary teaching tools and research exchanges of undergraduate and graduate students to provide training at the intersection of computer science, freshwater science, and ecosystem modeling. Ultimately, this project will develop scalable, robust, secure workflows that will advance the capacity, practice, and training opportunities for ecological forecasting worldwide. Results from this project can be found at http://flare-forecast.org<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting: University of Florida

Renato Figueiredo

[email protected]

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software ? FLARE: Forecasting Lake And Reservoir Ecosystems ? that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.<br/><br/>The FLARE forecasting system is novel in its architecture, as it integrates a software-defined virtual distributed infrastructure spanning resources from sensor gateway devices at the edge of the network to cloud computing and storage. FLARE will support the flexible deployment of software in close proximity to water quality sensors in lakes and reservoirs, and in cloud resources for end-to-end data acquisition and processing. FLARE interconnects its distributed resources through a virtual private network to ensure data integrity and privacy in communications, and supports a flexible model applicable across a variety of lakes and reservoirs. Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, widely-used open-source software frameworks in a manner that lowers the barrier to the deployment and management of ecological forecasting workflows by ecologists. Importantly, this project?s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forecasts will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other ecosystems. This project will build on and expand an existing program for cross-disciplinary teaching tools and research exchanges of undergraduate and graduate students to provide training at the intersection of computer science, freshwater science, and ecosystem modeling. Ultimately, this project will develop scalable, robust, secure workflows that will advance the capacity, practice, and training opportunities for ecological forecasting worldwide. Results from this project can be found at http://flare-forecast.org<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CAREER: A Novel and Fast Open-Source Code for Global Simulation of Stratified Convection and Magnetohydrodynamics of the Sun: Clarkson University

Chunlei Liang

[email protected]

Non-technical: <br/>The goal of this project is to create a unique capability for predicting density-stratified magnetohydrodynamics of the Sun. This research is expected to lay a foundation for developing methods for predicting extreme space weather, e.g. the event of a "super solar flare" followed by an extreme geomagnetic storm. Scientific results of this research can help resolve several contradictory predictions from previous studies of the solar convection zone. The Principal Investigator (PI) will develop and disseminate a powerful open-source software package to the space weather and solar physics communities. The success of predicting severe space weather events has significant societal and economic impacts. PI will design high-order accurate computational algorithms suitable for exascale simulations that can perform a billion billion calculations per second. This software will run on massively parallel distributed-memory computers to predict coupled global and local dynamics of the sun. PI will reach out to K-12 students and demonstrate that science of the sun and high-performance computing are exciting and important to society. Furthermore, PI will leverage outreach efforts with the High Altitude Observatory of the National Center for Atmospheric Research and other research centers. This project, thus, serves the national interest as stated by NSF's mission: to promote the progress of science and to advance the national welfare.<br/><br/>Technical: <br/>The goal of this research program is to develop a novel, fully compressible model and an open-source community code for global simulations of the solar convection zone that includes the top near surface shear layer of the Sun. Current leading global simulations use an elastic approximation whose computational domains extend from the base of the solar convection zone and must stop at about 0.96 solar radius, stopping short of the top near surface shear layer where Mach number could reach unity. This research program will create a powerful open-source community code CHORUS++ to simulate magnetohydrodynamics of the solar convection zone. CHORUS stands for Compressible High-ORder Unstructured-grid Spectral difference code which has been co-developed by the PI for hydrodynamics of the solar convection zone. CHORUS++ will be equipped with variable mesh resolution capability to focus on targeted regions of interests. A fast local time-stepping algorithm will be designed and equipped for CHORUS++ for long-period time integration on massively parallel computers. These technical accomplishments can accelerate the original CHORUS code by a factor over 100. The PI will conduct a series of global simulations of magnetohydrodynamics of the solar convection zone with unprecedented resolutions for predicting the differential rotation, meridional circulation, giant cells, and super-granulation of the sun.

NSFOpen Education ResourcesThe Research University (TRU)

Empirical Macrofinance: Open-source Textbook and Data-sharing Platform: Princeton University

Atif Mian

[email protected]

Abstract<br/><br/>The importance of the linkages between finance, debt, and macroeconomic outcomes, such as growing inequality, become evident since the Great Financial Crisis. To date, no single initiative has organized this recent empirical literature into a meaningful whole. Similarly, to date, data sources used to address macro-finance research questions are not integrated. This project fills these gaps by disseminating the new techniques and datasets used in macro-financial research and by creating an open-source textbook that grounds each topic to its theoretical foundations. <br/><br/>The project will develop an open-source textbook and data-sharing platform to promote teaching and research about empirical finance and macroeconomics (macro-finance), filling the gap for an integrated approach in this important area. Data and instructional resources will be freely available to students and researchers via the project website. The codebase will be accessible on a platform for open-source software development. In addition to standardizing currently available data on macro-financial variables, the project will digitize new data on (i) US County Business Patterns, (ii) Global Sectoral Credit and National Accounts, (iii) International Loan Contracts.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Collaborative Research: Empowering Open Law and Science: University of Washington

Nicholas Weber

[email protected]

Research transparency provides immense value across all areas of scholarly inquiry by helping to reveal the rigor, reliability, and relevance of, and to make more evaluable, all types of research. Scholars who engage in qualitative inquiry sometimes find it difficult to make their work transparent, i.e. to clearly communicate the meticulous and systematic research procedures and practices that they employ to generate and analyze qualitative data, and to clearly portray the evidentiary value of those data. Annotation for Transparent Inquiry (ATI), an emerging approach to increasing the transparency of published qualitative and multi-method social science, helps to address those challenges. This project aims to develop and test a new software tool that will empower scholars to use ATI to reveal the procedures they followed to generate data, explicate the logic of their analysis, and directly link to underlying data such as interviews or archival documents. The tool will thus help researchers and the public to better understand and evaluate qualitative research and provide easier access to the rich data underlying such work. The partnership between researchers, academic data repositories, and creators of open-source software that the project represents should make a significant contribution to infrastructure for research and education. The project also encourages intellectual democratization, enhancing access to transparency practices, to key insights and findings in social science and legal scholarship, and to research data. <br/><br/>ATI empowers authors to annotate their publications using interoperable web-based annotations that add valuable details about their work?s evidentiary basis and analysis, excerpts from data sources that underlie claims, and potentially links to the data sources themselves. The prototype for a new open-source tool that the project will develop will allow scholars to Restructure, Edit and Package Annotations (Anno-REP). Anno-REP will empower scholars to create and curate web-based annotations at any point in the writing process; signal their motivation; and publish those annotations on a web page in tandem with the scholarly work that they accompany. These innovations will significantly ease the use of ATI and facilitate and encourage its seamless integration into the writing and publishing processes, promoting scientific progress through qualitative inquiry. The project will solicit feedback for Anno-REP?s continued development from ten scholars with familiarity with ATI, and will also evaluate the tool through a workshop including 20 legal scholars (faculty and graduate students). In order to promote the use and hasten the scholarly adoption of both ATI and Anno-REP, the project will encourage and help scholars to propose work that has been annotated using ATI and Anno-REP for presentation at disciplinary conferences. In addition, it will organize a symposium of articles that use, and analyze the use of, ATI for submission to, review by, and publication in a top legal journal.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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