{"response":{"award":[{"abstractText":"As computing permeates nearly all fields of science and engineering, there is an exponential growth of computing needs from both the traditional computing-intensive domains and the emerging new and more diverse fields of research. The rise of machine learning and artificial intelligence applications has accelerated and broadened the use of computational resources from research in creating new and more environmentally friendly materials to improving medicine in our fight against deadly diseases. There are three main challenges to meeting this rapidly evolving landscape of national computational needs: a shortage of capacity, increasingly diverse applications, and computational literacy and training. This project aims to meet these challenges and transform the way computing is delivered by developing and deploying a composable advanced computing resource, Anvil, to the national research community to significantly increase both the computing capacity and accessibility. Anvil integrates a large-capacity high-performance computing (HPC) cluster with a comprehensive ecosystem of software, access interfaces, programming environments, and composable services to form a seamless environment able to support a broad range of current and future science and engineering applications. Through a carefully designed student training program and partnerships with regional and other universities, XSEDE, and Women in HPC programs, this project will develop computing competency in the next-generation workforce, and engage and train a broader audience including underrepresented students at minority-serving and EPSCoR (Established Program to Stimulate Competitive Research) institutions.\r\n\r\nBuilt with a forward-looking architecture with a high core count, and improved memory bandwidth and I/O, Anvil can effectively support traditional HPC with fast turnaround for high throughput, mid-scale computation jobs. Anvil consists of 1000 128-core computing nodes based on the next-generation AMD Epyc “Milan\" architecture that can deliver a total peak performance of 5.3 Petaflops. Each node has 256 GB of memory, and a 100 gigabits/second bandwidth from the Mellanox HDR InfiniBand interconnect, allowing multiple jobs of up to 1024 cores to be run at full speed over the interconnect fabric. These nodes are complemented by 32 large-memory nodes with 1 TB of RAM each, and 16 Nvidia GPU nodes with 4 “Volta Next” GPUs per node. The GPU nodes are capable of 1.57 petaflops of single-precision performance to support machine learning and a wide range of current and future science and engineering applications. Anvil’s multiple tiers of storage systems include a long-term archive, persistent file and campaign storage, a 10 PB scratch file system, a 3 PB flash burst buffer, and object storage to support a variety of workflows and storage needs. \r\n\r\nAnvil will lower the barrier to entry to advanced computing CI by providing interactive computing and desktop environments that ease the transition for users from diverse domains new to HPC. By providing feature-rich interactive environments such as Open OnDemand and ThinLinc, users can rapidly become productive on Anvil through Linux and Windows desktops, or familiar tools through their browser (e.g., Jupyter, RStudio). Complex scientific software environments and application stacks will be supported via containers orchestrated within a powerful composable subsystem. Anvil supports cloud-bursting of computational workloads as well as use of public cloud machine learning platforms including GPU and FPGA accelerators and software tools to automate hyperparameter tuning and algorithm selection for exploratory ML research. An existing production-quality science gateway at Purdue will support XSEDE researchers to share their data and tools online and facilitate easy access to Anvil and other XSEDE resources in classroom instruction and training activities.\r\n\r\nThis 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.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"PURDUE UNIVERSITY","awardeeAddress":"2550 NORTHWESTERN AVE # 1100","awardeeCity":"WEST LAFAYETTE","awardeeCountryCode":"US","awardeeDistrict":"04","awardeeDistrictCode":"IN04","awardeeName":"Purdue University","awardeePhone":"7654941055","awardeeStateCode":"IN","awardeeZipCode":"47906","cfdaNumber":"47.070","coPDPI":["Preston M Smith psmith@purdue.edu","Arman Pazouki apazouki@purdue.edu","Erik Gough goughes@purdue.edu","Xiao Zhu (Former) xiaozhu@uw.edu","Rajesh Kalyanam (Former) rkalyana@purdue.edu"],"date":"05/29/2020","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"9952154","expDate":"09/30/2027","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource, Innovative HPC","fundsObligated":["FY 2020 = $11,942,583.00","FY 2021 = $10,449,761.00","FY 2022 = $2,038,429.00","FY 2023 = $48,000.00","FY 2024 = $4,947,910.00","FY 2025 = $2,070,416.00"],"fundsObligatedAmt":"31497100","histAwd":"false","id":"2005632","initAmendmentDate":"05/29/2020","jrnl":[{"artTitl":"Providing On-Prem GenAI Inference Services to a Campus Community","auth":"Rodenbeck, Sarah and Gough, Erik and Mohana_Krishnan_Sangeetha, Athreyan and Ashish and Ahlawat, Mihir and Karunai_Kiri_Ragavan, Vivek and Muthukumar, Abhishek and Ahmad, Aanis","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3708035.3736039","jrnlYr":"2025","parPblcId":"10639609"},{"artTitl":"Defining Performance of Scientific Application Workloads on the AMD Milan Platform","auth":"Wu, Tsai-Wei and Lien Harrell, Stephen and Lentner, Geoffrey and Younts, Alex and Weekly, Sam and Mertes, Zoey and Maji, Amiya and Smith, Preston and Zhu, Xiao","dgtlObjId":"https://doi.org/10.1145/3437359.3465596","jrnlTitl":"Practice and Experience in Advanced Research Computing","jrnlYr":"2021","parPblcId":"10297281"},{"artTitl":"AIrTonomy: An Experimental Infrastructure for Testing Next-Generation Autonomous Aerial Vehicles","auth":"Brunswicker, Sabine and Goppert, James and Gough, Erik and Lercel, Damon and Hwang, Inseok and Kong, Nan and Sribunma, Worawis and Deng, Chuhao and Shreekumar, Jayanth and Zoltowski, Michael and Kasireddy, Varun and Scherer, Sebastian","authIndCode":"N","dgtlObjId":"https://doi.org/10.2514/6.2025-3047","jrnlYr":"2025","parPblcId":"10717772"},{"artTitl":"InKubeator: Pre-warming In-Memory KV Caches from Disk for Elastic LLM Serving","auth":"Muthukumar, Abhishek and Gough, Erik and Nadig, Deepak","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/ICC59461.2026.11587970","jrnlYr":"2026","parPblcId":"10717800"},{"artTitl":"AnvilOps: Increasing Kubernetes Accessibility via an Open-Source Platform-as-a-Service","auth":"Swanson, Brendan and Zheng, Emma and Lumas, LJ and Kashgarani, Haniye","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3785462.3815893","jrnlYr":"2026","parPblcId":"10715102"},{"artTitl":"Anvil - System Architecture and Experiences from Deployment and Early User Operations","auth":"Song, X. Carol and Smith, Preston and Kalyanam, Rajesh and Zhu, Xiao and Adams, Eric and Colby, Kevin and Finnegan, Patrick and Gough, Erik and Hillery, Elizabett and Irvine, Rick and Maji, Amiya and St. John, Jason","dgtlObjId":"https://doi.org/10.1145/3491418.3530766","jrnlTitl":"PEARC '22: Practice and Experience in Advanced Research Computing","jrnlYr":"2022","parPblcId":"10349289"},{"artTitl":"Application of the cyberinfrastructure production function model to R1 institutions","auth":"Smith, Preston M and Gemmill, Jill and Hancock, David Y and O'Shea, Brian W and Snapp-Childs, Winona and Wilgenbusch, James","authIndCode":"N","dgtlObjId":"https://doi.org/10.3389/frma.2025.1449996","jrnlTitl":"Frontiers in Research Metrics and Analytics","jrnlVol":"10","jrnlYr":"2025","parPblcId":"10636433"},{"artTitl":"Developing Cyberinfrastructure Professionals as Research Partners: Lessons from the First Cohort of CIPIVOT","auth":"Snapp-Childs, Winona and Hillery, Elizabett A and Wernert, Julie A and Michael, Scott and Hancock, David Y and Simon, Kosali and Smith, Preston M and Huber, Matthew and Thota, Abhinav and Schultz, Douglas and Navicky, Michael and Collins, Eric and Alshyba","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3785462.3815863","jrnlYr":"2026","parPblcId":"10716526"},{"artTitl":"A Hierarchical Deep Learning Approach for Predicting Job Queue Times in HPC Systems","auth":"Lovell, Austin and Wisniewski, Philip and Rodenbeck, Sarah and Ashish","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/SCW63240.2024.00086","jrnlYr":"2024","parPblcId":"10639606"},{"artTitl":"Understanding Factors that Influence Research Computing and Data Careers","auth":"Chaudhry, Shafaq and Pazouki, Arman and Schmitz, Patrick and Hillery, Elizabett and Kee, Kerk","dgtlObjId":"https://doi.org/10.1145/3491418.3530292","jrnlTitl":"Practice and Experience in Advanced Research Computing (PEARC22)","jrnlYr":"2022","parPblcId":"10358740"},{"artTitl":"Toward a Trustworthy and Accessible Scientific Data Workflow Platform with StreamCI","auth":"Shin, Jaewoo and Jain, Mehak and Jha, Shubham and Kim, I Luk and Appalaneni, Mohana Sravya and Hoang, Tri Minh and Fuentes_Rosado, Jorge Ivan and Barezi, Elham J and Zhao, Lan and Song, Carol X","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3785462.3815877","jrnlYr":"2026","parPblcId":"10715101"},{"artTitl":"Generating Frequently Asked Questions from Technical Support Tickets using Large Language Models","auth":"Joslin, Christina and Burns, David and Ashish, Ashish and Barezi, Elham J","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3731599.3767429","jrnlYr":"2025","parPblcId":"10639608"},{"artTitl":"Hello Computer: HPC in the Agentic Era","auth":"Lentner, Geoffrey and Ashish","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3785462.3815825","jrnlYr":"2026","parPblcId":"10717763"},{"artTitl":"DART: Extending On-Premise K8s via Automated Orchestration of Remote At-scale Testbeds","auth":"Govindegowda, Monisha and Gough, Erik and Nadig, Deepak","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/ICC59461.2026.11587365","jrnlYr":"2026","parPblcId":"10717799"},{"artTitl":"A Modular, Responsive, and Accessible HPC Dashboard Built upon Open OnDemand","auth":"Tan, Richie and Jin, Guangzhen","authIndCode":"N","dgtlObjId":"https://doi.org/10.1145/3731599.3767434","jrnlYr":"2025","parPblcId":"10717135"},{"artTitl":"An Introductory Guide to Developing GenAI Services for Higher Education","auth":"Rodenbeck, Sarah and Gough, Erik and null, Ashish and Kotha, Sathvika and Hasanth, K Meher and Dash, Durga","authIndCode":"N","dgtlObjId":"https://doi.org/10.5281/zenodo.13864403","jrnlYr":"2024","parPblcId":"10639607"},{"artTitl":"Cyberinfrastructure for sustainability sciences","auth":"Song, Carol X. and Merwade, Venkatesh and Wang, Shaowen and Witt, Michael and Kumar, Vipin and Irwin, Elena and Zhao, Lan and Walton, Amy","authIndCode":"N","dgtlObjId":"https://doi.org/10.1088/1748-9326/acd9dd","jrnlTitl":"Environmental Research Letters","jrnlVol":"18","jrnlYr":"2023","parPblcId":"10430028"}],"latestAmendmentDate":"06/03/2026","managingPec":"761900","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"YRXVL4JYCEF5","pdPIName":"Xiaohui Carol Song","perfAddress":"155 South Grant Street","perfCity":"West Lafayette","perfCountryCode":"US","perfDistrict":"04","perfDistrictCode":"IN04","perfLocation":"Purdue University","perfStateCode":"IN","perfZipCode":"479072114","pi":["Xiaohui Carol Song cxsong@purdue.edu"],"piEmail":"cxsong@purdue.edu","piFirstName":"Xiaohui Carol","piId":"269674824","piLastName":"Song","poEmail":"rchadduc@nsf.gov","poName":"Robert Chadduck","poPhone":"7032922247","primaryProgram":["01002021DB NSF RESEARCH & RELATED ACTIVIT","01002223DB NSF RESEARCH & RELATED ACTIVIT","01002526DB NSF RESEARCH & RELATED ACTIVIT","01002324DB NSF RESEARCH & RELATED ACTIVIT","01002122DB NSF RESEARCH & RELATED ACTIVIT","01002425DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00, 761900","program":"EQUIPMENT ACQUISITIONS, WOMEN, MINORITY, DISABLED, NEC, REU SUPP-Res Exp for Ugrd Supp","progRefCode":"7619, 9102, 9251","publicAccessMandate":"1","publicationResearch":["2025~Rodenbeck, Sarah and Gough, Erik and Mohana_Krishnan_Sangeetha, Athreyan and Ashish and Ahlawat, Mihir and Karunai_Kiri_Ragavan, Vivek and Muthukumar, Abhishek and Ahmad, Aanis~https://doi.org/10.1145/3708035.3736039~Providing On-Prem GenAI Inference Services to a Campus Community~N~10639609~10639609~OSTI~2025-10-01 03:51:15.74","Practice and Experience in Advanced Research Computing~2021~Wu, Tsai-Wei and Lien Harrell, Stephen and Lentner, Geoffrey and Younts, Alex and Weekly, Sam and Mertes, Zoey and Maji, Amiya and Smith, Preston and Zhu, Xiao~https://doi.org/10.1145/3437359.3465596~Defining Performance of Scientific Application Workloads on the AMD Milan Platform~1 to 4~10297281~10297281~OSTI~2021-09-30 17:03:32.796","2025~Brunswicker, Sabine and Goppert, James and Gough, Erik and Lercel, Damon and Hwang, Inseok and Kong, Nan and Sribunma, Worawis and Deng, Chuhao and Shreekumar, Jayanth and Zoltowski, Michael and Kasireddy, Varun and Scherer, Sebastian~https://doi.org/10.2514/6.2025-3047~AIrTonomy: An Experimental Infrastructure for Testing Next-Generation Autonomous Aerial Vehicles~N~10717772~10717772~OSTI~2026-10-01 17:02:44.25","2026~Muthukumar, Abhishek and Gough, Erik and Nadig, Deepak~https://doi.org/10.1109/ICC59461.2026.11587970~InKubeator: Pre-warming In-Memory KV Caches from Disk for Elastic LLM Serving~N~10717800~10717800~OSTI~2026-10-01 17:46:09.276","2026~Swanson, Brendan and Zheng, Emma and Lumas, LJ and Kashgarani, Haniye~https://doi.org/10.1145/3785462.3815893~AnvilOps: Increasing Kubernetes Accessibility via an Open-Source Platform-as-a-Service~N~10715102~10715102~OSTI~2026-09-21 23:19:31.52","PEARC '22: Practice and Experience in Advanced Research Computing~2022~Song, X. Carol and Smith, Preston and Kalyanam, Rajesh and Zhu, Xiao and Adams, Eric and Colby, Kevin and Finnegan, Patrick and Gough, Erik and Hillery, Elizabett and Irvine, Rick and Maji, Amiya and St. John, Jason~https://doi.org/10.1145/3491418.3530766~Anvil - System Architecture and Experiences from Deployment and Early User Operations~1 to 9~10349289~10349289~OSTI~2022-08-16 17:03:23.696","Frontiers in Research Metrics and Analytics~2025~10~Smith, Preston M and Gemmill, Jill and Hancock, David Y and O'Shea, Brian W and Snapp-Childs, Winona and Wilgenbusch, James~https://doi.org/10.3389/frma.2025.1449996~Application of the cyberinfrastructure production function model to R1 institutions~N~10636433~10636433~OSTI~2025-10-01 08:51:55.186","2026~Snapp-Childs, Winona and Hillery, Elizabett A and Wernert, Julie A and Michael, Scott and Hancock, David Y and Simon, Kosali and Smith, Preston M and Huber, Matthew and Thota, Abhinav and Schultz, Douglas and Navicky, Michael and Collins, Eric and Alshyba~https://doi.org/10.1145/3785462.3815863~Developing Cyberinfrastructure Professionals as Research Partners: Lessons from the First Cohort of CIPIVOT~N~10716526~10716526~OSTI~2026-10-01 17:02:37.286","2024~Lovell, Austin and Wisniewski, Philip and Rodenbeck, Sarah and Ashish~https://doi.org/10.1109/SCW63240.2024.00086~A Hierarchical Deep Learning Approach for Predicting Job Queue Times in HPC Systems~N~10639606~10639606~OSTI~2025-10-01 03:51:15.053","Practice and Experience in Advanced Research Computing (PEARC22)~2022~Chaudhry, Shafaq and Pazouki, Arman and Schmitz, Patrick and Hillery, Elizabett and Kee, Kerk~https://doi.org/10.1145/3491418.3530292~Understanding Factors that Influence Research Computing and Data Careers~1 to 9~10358740~10358740~OSTI~2023-04-24 10:06:54.443","2026~Shin, Jaewoo and Jain, Mehak and Jha, Shubham and Kim, I Luk and Appalaneni, Mohana Sravya and Hoang, Tri Minh and Fuentes_Rosado, Jorge Ivan and Barezi, Elham J and Zhao, Lan and Song, Carol X~https://doi.org/10.1145/3785462.3815877~Toward a Trustworthy and Accessible Scientific Data Workflow Platform with StreamCI~N~10715101~10715101~OSTI~2026-10-01 16:41:16.303","2025~Joslin, Christina and Burns, David and Ashish, Ashish and Barezi, Elham J~https://doi.org/10.1145/3731599.3767429~Generating Frequently Asked Questions from Technical Support Tickets using Large Language Models~N~10639608~10639608~OSTI~2025-10-01 03:51:15.573","2026~Lentner, Geoffrey and Ashish~https://doi.org/10.1145/3785462.3815825~Hello Computer: HPC in the Agentic Era~N~10717763~10717763~OSTI~2026-10-01 16:52:18.68","2026~Govindegowda, Monisha and Gough, Erik and Nadig, Deepak~https://doi.org/10.1109/ICC59461.2026.11587365~DART: Extending On-Premise K8s via Automated Orchestration of Remote At-scale Testbeds~N~10717799~10717799~OSTI~2026-10-01 17:46:09.193","2025~Tan, Richie and Jin, Guangzhen~https://doi.org/10.1145/3731599.3767434~A Modular, Responsive, and Accessible HPC Dashboard Built upon Open OnDemand~N~10717135~10717135~OSTI~2026-09-30 12:40:57.636","2024~Rodenbeck, Sarah and Gough, Erik and null, Ashish and Kotha, Sathvika and Hasanth, K Meher and Dash, Durga~https://doi.org/10.5281/zenodo.13864403~An Introductory Guide to Developing GenAI Services for Higher Education~N~10639607~10639607~OSTI~2025-10-01 03:51:15.25","Environmental Research Letters~2023~18~Song, Carol X. and Merwade, Venkatesh and Wang, Shaowen and Witt, Michael and Kumar, Vipin and Irwin, Elena and Zhao, Lan and Walton, Amy~https://doi.org/10.1088/1748-9326/acd9dd~Cyberinfrastructure for sustainability sciences~N~10430098~10430028~OSTI~2023-07-12 00:03:38.29"],"startDate":"10/01/2020","title":"Category I: Anvil - A National Composable Advanced Computational Resource for the Future of Science and Engineering","transType":"Cooperative Agreement","ueiNumber":"YRXVL4JYCEF5"}],"metadata":{"offset":0,"rpp":25,"totalCount":1}}}