The National Science Foundation (NSF) awarded a $406,004 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Carnegie Mellon University (CMU). The goal of this 4-year project is to develop new resource allocation policies that enable efficient and timely training of machine learning models by leveraging parallelizable computing resources. The research will focus on modeling the unique characteristics of machine learning training workloads, such as...
This $275,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop and test new scheduling algorithms for improving the performance of modern database systems. The goal is to create models and policies that can efficiently allocate limited hardware resources, such as compute and memory, to handle a stream of parallelizable database queries with varying levels of parallelizability and service...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $549,999 to the University of North Texas to develop a time-sensitive large model training platform for dynamic data analytics. The key goals are to: Automate the parallelization of large model training to minimize latency, Progressively grow pre-trained small models during fine-tuning to reduce training iterations, and Validate the platform's...
The National Science Foundation (NSF) awarded a $295,548 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Illinois. The award will support the development of an "Intelligent Management of Hybrid Workloads for Extreme Scale Computing" framework. Key research activities include modeling the performance implications of diverse workloads on supercomputers, creating new intelligent multi-resource scheduling methods, and...
The National Science Foundation (NSF) Division of Computer and Network Systems awarded a $174,178 project grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the President and Board of Trustees of Santa Clara College. The project aims to develop methods and a system for deploying complex machine learning (ML) models on network processing units (NPUs) to enable ultra low-latency performance for modern applications such as self-driving, security threat...
The National Science Foundation (NSF) awarded a $299,993 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the University of Chicago. The grant supports a collaborative research project on the "Foundations of Few-Round Active Learning" in supervised machine learning. The key objectives are to advance active learning algorithms and improve understanding of their capabilities in scenarios with limited interaction rounds. The research aims...
The National Science Foundation (NSF) awarded a $449,815 Research Initiation Award under the STEM Education (CFDA 47.076) program to Florida A&M University (FAMU) to develop a robust and efficient cooperative resource allocation and scheduling system for improving resource utilization and throughput in cloud computing environments while ensuring service level objective availability. The 3-year project, running from August 2024 to July 2027, aims to incorporate machine learning techniques...
The National Science Foundation (NSF) awarded a $249,998 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the University of Georgia Research Foundation, Inc. The grant supports research and development of the "EASER" paradigm - a framework for dealing with extreme heterogeneity in high-performance computing systems. Key components include: 1) compiler-driven performance prediction models, 2) an integrated job scheduling and...
This $100,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research on machine learning-augmented algorithms that can operate on weak and sparse predictions. The project aims to study the design considerations and performance tradeoffs of such algorithms, which can be useful in real-world scenarios where obtaining abundant and accurate training data is challenging. The research seeks to...
This National Science Foundation (NSF) Project Grant awarded to North Carolina State University (NC State) under the STEM Education (CFDA 47.076) program will develop new technologies to improve student learning in machine learning (ML) education. The $226,873 award, effective October 1, 2023 through September 30, 2028, will create automated algorithms to generate personalized feedback, hints, and examples to support students in designing effective ML models, implementing them in code, and...