Honggao Liu, Ph.D. - US grants

With this project, Louisiana State University (LSU) joins the integrated, national cyberinfrastructure known as the TeraGrid, contributing high-performance computing (HPC) services and grid technology expertise to support the nation's science and engineering researchers. This project extends the computational capacity of the TeraGrid by providing access to 50% of a Dell Linux cluster that employs Intel's multi-core processor technology. In addition, the project contributes access to several data management and grid services. These services include the PetaShare distributed data archival, analysis, and visualization system, the Highly-Available Robust Co-allocator (HARC) co- scheduler for compute, network, and data services, and the Cactus Computational Toolkit. The project includes the provision of user support, with an emphasis on support for researchers wanting to develop grid applications.

The project includes a statewide outreach program anchored by Southern University, a large HBCU system. It is anticipated that the project will leverage LSU's connections with the Louisiana Optical Network Initiative (LONI) and the Southeastern University Research Association (SURA) to increase the usage of TeraGrid by under-represented groups. Project staff will work with researchers in hurricane and storm surge modeling to develop a TeraGrid Science Gateway for event-driven, ensemble modeling of storm surges when hurricanes approach US coastal regions, a project with the potential to contribute to efforts to improve national hurricane preparedness.

The net effects of this award will be: (i) to provide the science and engineering research and education community with access to a high-performance computing resource with a familiar and popular architecture; (ii) to enhance the expertise in and user support for grid services within the TeraGrid; (iii) to increase the geographical diversity of the TeraGrid partnership; and (iv) to enhance the TeraGrid's connections with universities that include significant numbers of students and faculty from under-represented groups.

The funded HPC services will permit investigators across a broad range of disciplines to conduct advanced research on topics in many areas of science and engineering. It is anticipated that this award, together with other recent awards in NSF's high-performance computing program, will collectively strengthen the provisioning, to the science and engineering community, of high-end cyberinfrastructure services - such as high-performance computing, community data collections and data analysis services, remote visualization, and science gateway services - through the nationally distributed cyberinfrastructure system that is the TeraGrid.

Project Shelob is a major effort to support the computational science research, education and training requirements of investigators using modern high performance computing systems equipped with graphical processing units, or GPUs. GPUs are nearly identical to the high-end graphics cards found in personal computers dedicated to gaming applications. The same capabilities that support games with highly realistic real-time animations can be adapted to vastly improve the speed of getting answers on complex science problems. This comes at a cost, however, as the programming becomes more difficult, a burden considering that programming modern parallel computing systems is already a difficult task. The Shelob system provides a dedicated platform to allow experimenting on a production grade system, allowing new methods to be worked out, and providing a platform that can be used for teaching which does not interrupt the critical workflow on other research production systems.
The Shelob system is a compute cluster that allows for parallel programming using distributed memory methods, and adds the ability for nodes to incorporate GPU processing. Such heterogeneous systems require programmers to understand both conventional message-passing (i.e. MPI) programming methods, and the methods specific to GPU programming. The only way researchers can determine how to mix the methods for best performance is to have system-level access to modify and adjust settings as necessary.
There are high expectations for Shelob, not the least of which is instilling excitement in high school and undergraduate students over the possibilities presented for work and research in high performance computing. Programs such as Research Experiences for Undergraduates and the Beowulf Bootcamp, provide unique opportunities to students. Multi-institutional research groups, such as Cactus and Pluto, hope to develop easier ways to make use of the power promised by the Shelob system hardware. Other groups, such as the Louisiana-wide LA-SiGMA project, hope to develop the next generation of codes to support material science research and the development of novel materials for industrial applications.

This is an award to acquire a compute cluster at LSU. The computer is a heterogeneous HPC cluster named SuperMIC containing both Intel Xeon Phi and NVIDIA Kepler K20X GPU (graphics processing unit) accelerators. The intent is to conduct research on programming such clusters while advancing projects that are dependent on HPC. The efforts range from modeling conditions which threaten coastal environments and test mitigation techniques; to simulating the motions of tumors/organs in cancer patients due to respiratory actions to aid radiotherapy planning and management. The burden of learning highly complex hybrid programming models presents an enormous software development crisis and demands a better solution. SuperMIC will serve as the development platform to extend current programming frameworks, such as Cactus, by incorporating GPU and Xeon Phi methods. Such frameworks allow users to move seamlessly from serial to multi-core to distributed parallel platforms without changing their applications, and yet achieve high performance. The SuperMIC project will include training and education at all levels, from a Beowulf boot camp for high school students to more than 20 annual LSU workshops and computational sciences distance learning courses for students at LONI (Louisiana Optical Network Initiative) and LA-SiGMA (Louisiana Alliance for Simulation-Guided Materials Applications) member institutions. These include Southern University, Xavier University, and Grambling State University - all historically black colleges and universities (HBCU) which have large underrepresented minority enrollments. The SuperMIC cluster will be used in the LSU and LA-SiGMA REU and RET programs. It will impact the national HPC community through resources committed to the NSF XSEDE program and the Southeastern Universities Research Association SURAgrid. The SuperMIC will commit 40% of the usage of the machine to the XSEDE XRAC allocation committee.

Modern scientific research often involves the marshaling of very large data sets through extensive processing pipelines before usable information can be extracted. Examples of this type of data-intensive research include the near real-time analysis of time-varying medical images, decoding the genetic structure of diverse biological systems, simulating the merger of binary star systems, modeling complex flow structures in chemical reaction tanks, and determining how to effectively interact with extraordinarily high-resolution lunar and planetary data.

The CADIS project provides the critical networking infrastructure that enables this wide range of data-intensive research activities at Louisiana State University (LSU). CADIS links high-performance computer systems located on campus and at other state and national facilities to a new state-of-the-art building at LSU -- the Louisiana Digital Media Center (LDMC). Serving as a center for diverse, data-intensive research activities, the LDMC houses high resolution display technologies and custom visualization systems and is dedicated to digital computation, media production, and advanced teaching methods. CADIS enhances student learning experiences by facilitating the use of actual data sets from experimental instruments and large-scale computational simulations, and the classroom application of novel visualization methods. CADIS also is expected to open new opportunities for collaboration between LSU researchers and the growing digital media industry across Louisiana.

Communities on modern river deltas with total populations greater than 500 million people face threats from global reductions in river sediment, land subsidence and rising sea level. Risk mitigation efforts may require intensive computer simulations that are integrated with data collection and engineering analytics for guidance. This project establishes a Coastal Resilience Collaboratory with a three-fold mission: 1) enhance the collaboration among earth scientists, computer scientists, cyberinfrastructure specialists and coastal engineers tasked with solving the sustainability issues of deltaic coasts; 2) identify risk mitigation for coastal communities subject to flooding hazards using approaches that integrate restoration and protection; and 3) leverage NSF investments in cyberinfrastructure to address problems of major national importance involving engineering design guided by coastal system responses to specific hazard mitigation projects. Effective linkages of cyberinfrastructure that enables rapid sharing and integration of available data resources and computational tools will be evaluated. The project will also evaluate how effectively these cyberinfrastructure products promote the wider use of high-performance computing and data analytics in the coastal engineering and science research community. The proposed project has a wide range of broader impacts, ranging from education and workforce development, to dissemination of research results to the general public, K-12 students, and coastal managers and decision makers.

The Coastal Resilience Collaboratory core research program builds on a recently funded Coastal SEES project (EAR-1427389), which serves as the science driver for the cyberinfrastructre development and its enabled simulation experiments. One of the grand challenges for earth system science is to characterize dynamic environmental processes at appropriate space and time scales with integrated observation networks and models. The project advances four elements: 1) A simulation management system for a high-level web-based interface, improving multiphysics model usability for coastal scientists/engineers not familiar with advanced computing resources; 2) Application packaging for cloud-computing using Docker container technology to facilitate prototype simulation experiments in two large river deltas to test a range of hypotheses; 3) Accelerator technology to achieve high performance levels aimed at making a GPU- accelerated Boussinesq code base available to coastal engineers for the design of sustainable infrastructure; and 4)Aapplications for visualization and access to toolkits on mobile devices to support decision-making and educational activities. The three simulation experiments that test system interactions in the modeling framework proposed is expected to produce foundational knowledge that can evaluate potential impacts of deltaic landscape change on coasts around the world and suggest mitigation solutions.

The project funds the acquisition of a composable high-performance data-analysis and computing instrument, named FASTER (Fostering Accelerated Scientific Transformations, Education, and Research). FASTER will enable transformative advances in scientific fields that rely on artificial intelligence and machine learning (AI/ML) techniques, big data practices, and high-performance computing (HPC) technologies. The FASTER platform removes significant bottlenecks in research computing by leveraging a technology that can dynamically allocate resources to support workflows. It will support researchers from across the Texas A&M University System and their collaborating institutions. Thirty percent of FASTER?s computing resources will also be allocated to researchers nationwide by the National Science Foundation (NSF) XSEDE (Extreme Science and Engineering Discovery Environment) program. FASTER?s composable interface allows it to simultaneously support both emerging and traditional workloads in research computing. Transformative research projects benefiting from FASTER will include the development of AI/ML models, cybersecurity, health population informatics, genomics, bioinformatics, computer-aided drug design, agricultural sciences, life sciences, oil and gas simulations, de novo materials design, climate modeling, multi-scale simulations, quantum computing architectures, biomedical imaging, geosciences, and quantum chemistry. In addition to supporting a wide-range of fields of research, the project contributes to code development, education, and the workforce development goals of several NSF Big Ideas.

FASTER adopts the innovative Liqid composable software-hardware approach combined with cutting-edge technologies such as state of the art CPUs and GPUs, NVMe (Non-Volatile Memory Express) based storage, and thigh speed interconnect. Workflows on FASTER will be able to dynamically integrate disaggregated GPUs and NVMe to compose a single node, allowing them to scale beyond traditional hardware limits. The composable and configurable techniques will allow researchers to use resources efficiently, enabling more science. Best practices gathered from managing the resource will be shared with the community. FASTER will coordinate a three-pronged effort to effectively broaden participation in computing by focusing on training, education and outreach. FASTER will leverage existing efforts that promote STEM (Science, Technology, Engineering and Mathematics) and broaden participation in computing at the K-12, collegiate, and professional levels to have a transformative impact nationally. FASTER activities are designed to expand the participation of traditionally underrepresented groups in computing and STEM, particularly at minority-serving institutions.

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.

Two-year colleges and smaller institutions of higher education play an important role in shaping the country's economic and computing workforce. Burgeoning interest in fields such as cloud computing and smart manufacturing have paved the path for adoption of advanced cyberinfrastructure practices in research and academic pursuits at these institutions. Expanding on the collective experience of groups engaged in various aspects of campus computing, the Building Research Innovation at Community Colleges (BRICCs) approach examines the research and educational needs from advanced cyberinfrastructure in such institutional settings. Led by CyberTeams, BRICCs offers an inclusive platform to develop and extend efforts in this space to the national level.

BRICCs is a unique opportunity to study campus computing characteristics at smaller institutions and community colleges. BRICCs adopts a multi-pronged approach that focuses on learning about the problems at hand, partnering with institutions to enable solutions, and finally communicating its findings to the broader research community. Fostering partnerships between knowledgeable cyberinfrastructure professionals and these institutions is a critical aspect of BRICCs. Toward achieving this, BRICCs will host virtual and in-person community workshops to explore avenues to broaden the impact of advanced cyberinfrastructure on campus computing at all levels. BRICCs will produce learning resources, workshop reports, support future funding efforts, and propose campus networking models that align with the research and academic pursuits of smaller institutions. As a collective of computing expertise, BRICCs serve as a collaborative space to address similar challenges in advancing cyberinfrastructure adoption in research and educational settings.

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.

The ever-growing complexity of Science and Engineering (S&E) workflows and expectations of Open Science have encouraged researchers to adopt new technologies, such as containerization, virtualization and composability, that enable them to respond to an increasingly complex cyberinfrastructure (CI) landscape while producing shareable, and reproducible results. ACES (Accelerating Computing for Emerging Sciences), an innovative advanced computational prototype to be developed by Texas A&M University, tries to answer a fundamental question: how does one effectively offer a holistic computing platform that can simultaneously meet the needs of a continuum of users in diverse research communities with varying levels of computing adoption? The project will allow researchers to creatively develop new programming models and workflows that utilize these architectures while simultaneously advancing HPC (High Performance Computing) and data science projects.

The ACES platform removes significant bottlenecks in advanced computing by introducing the flexibility to aggregate various components (i.e., processors, accelerators and memory) on an as-needed basis to solve problems that were previously not addressable. By letting researchers switch and run on accelerators best suited for their workflows, ACES will benefit many research and development projects in the fields of artificial intelligence and machine learning (AI/ML), cybersecurity, health population informatics, genomics and bioinformatics, human and agricultural life sciences, oil & gas simulations, de novo materials design, climate modeling, molecular dynamics, quantum computing architectures, imaging, smart and connected societies, geosciences, and quantum chemistry. Toward facilitating researcher use, ACES will offer avenues for interactive computing, portals, and cloud connectivity. ACES will support the national research community through coordination systems supported by the National Science Foundation (NSF). Finally, ACES will also leverage existing efforts that promote science and broaden participation in computing at the K-12, collegiate, and professional levels to have a transformative impact nationally by focusing on training, education and outreach. ACES activities are designed to expand the participation of traditionally underrepresented groups in computing and STEM (Science, Technology, Engineering and Mathematics), particularly at minority-serving institutions. ACES will offer fellowships to students, continue efforts to support teacher programs, and offer a number of formal and informal courses, whose materials will be offered to the national community for use free-of-charge.

This project funds the development of a dynamically composable high-performance data analysis and computing platform, named ACES. AI and ML are integrated with traditional simulation and modeling approaches in the pursuit of innovation. Edge-computing and instrumental probes have pushed the need to verify, process, store, analyze, and query vast amounts of unstructured data in real time. The coupling of analytics with closely-situated data on highly-usable web-based technologies connected to a compute backend have led to a paradigm shift in expectations from research computing environments. The ACES innovative composable hardware platform helps accelerate transformative changes in research areas that can leverage novel High Bandwidth Memory (HBM) processors and accelerators for analytics and computing. ACES leverages Liqid’s composable framework via PCIe (Peripheral Component Interconnect express) Gen5 on Intel’s HBM Sapphire Rapid processors to offer a rich accelerator testbed consisting of Intel Ponte Vecchio GPUs (Graphics Processing Units), Intel FPGAs (Field Programmable Gate Arrays), NEC Vector Engines, NextSilicon co-processors, Graphcore IPUs (Intelligence Processing Units). The accelerators are coupled with Intel Optane memory and DDN Lustre storage interconnected with Mellanox NDR 400Gbps (gigabit-per-second) InfiniBand to support workflows that benefit from optimized devices. ACES will enable applications and workflows to dynamically integrate the different accelerators, memory, and in-network computing protocols to glean new insights by rapidly processing large volumes of data, and provide researchers with a unique platform to produce complex hybrid programming models that effectively supports calculations that were not feasible before.

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.