This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) Project Grant award in the amount of $325,000.00 was provided to Carnegie Mellon University to develop and test algorithms for scheduling a stream of parallelizable database queries. The goal is to create new scheduling policies that maximize the utilization of system resources such as compute and memory in order to reduce query latencies. The project targets...
The National Science Foundation (NSF) awarded a $174,200 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of North Carolina at Chapel Hill (UNC-CH) to develop new resource allocation policies for optimizing the scheduling of parallelizable machine learning (ML) training workloads on shared hardware clusters. The goal is to enable the rapid and efficient training of highly accurate ML models using limited computing resources. This...
This $564,958 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to advance fundamental knowledge and innovate resource allocation algorithms for modern computing systems serving machine learning applications. The project focuses on addressing the challenges posed by high variability and uncertainty in both demand and service capabilities in these systems. The research will explore two key thrusts:...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program totaling $149,690 will fund research at the University of Washington from February 2022 through September 2022 to develop scalable database technologies. Specifically, the grant will support the introduction of per-processor-core queues to absorb index updates in databases, eliminating synchronous updates to range indices. This innovation aims to free databases from bottlenecks that...
This $400,000 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program supports a collaborative research effort led by Carnegie Mellon University (CMU). The project aims to develop a hardware-software co-design approach for high-performance in-memory analytic data processing. Key technical thrusts include mechanisms for in-place data analytics on dynamic random-access memory (DRAM), processing-in-memory (PIM)...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) provides $954,310 to Duke University to develop practical algorithms for responsive optimization in two key application domains: database query optimization and societal decision-making. The project aims to advance a principled and systematic approach to responsive optimization, which focuses on addressing uncertainty in inputs and allowing...
The National Science Foundation awarded a $599,995 project grant to the University of Massachusetts under the Computer and Information Science and Engineering program (CFDA 47.070) for the period of September 1, 2022 through August 31, 2025. The grant funds research to develop scalable in-database prescriptive analytics capabilities for dynamic environments. Specifically, the university researchers will extend prior work on constrained optimization problems to handle data uncertainty and...
The National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program awarded a $197,992 Project Grant to The Research Foundation for the State University of New York (RF SUNY) to develop speedy and reliable approximate query processing capabilities in hybrid transactional/analytical database systems. The 5-year project, commencing on May 15, 2024, aims to enable scalable real-time data analytics on large and rapidly growing datasets by advancing approximate...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, awarded to Purdue University on April 1, 2024, provides $176,309.00 in funding over a 5-year period through March 31, 2029. The project aims to develop a new database system optimized for disaggregated hardware architectures, which will improve performance, scalability, and elasticity of database applications. Key research thrusts include innovations in managing database logs and...
This $129,536 federal Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop advanced algorithms and computational frameworks for large-scale resource allocation problems. The research will tackle three core challenges: effective preference elicitation from users, accounting for preference uncertainty, and enabling efficient computation for scaling to large problem instances. Key...