This $162,826 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of DLTOOLKIT, a novel performance profiling and analysis infrastructure for scientific deep learning (DL) workloads. The key objectives of this 3-year project, which runs from June 2025 to May 2028, are to: (a) integrate synergistic profiling capabilities from existing DL frameworks to lower the barrier for...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $162,825 to the University of California, Merced to develop DLTOOLKIT, a performance profiling infrastructure for domain scientists to analyze and optimize scientific deep learning applications. The key objectives of this 3-year project are to create novel profiling capabilities, including synergistic tool-framework integration, just-in-time...
The National Science Foundation (NSF) awarded a $221,138 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Merced. The grant titled "REFORMING PROFILING TECHNIQUES TO GUIDE SYSTEMIC PERFORMANCE TUNING FOR GPU-ACCELERATED DEEP LEARNING WORKLOADS" aims to advance state-of-the-art profiling techniques to enable systemic performance tuning of deep learning models across multiple abstraction layers, from the...
This $174,999 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support a research project at the University of Central Missouri. The project aims to develop a unified, large language model (LLM)-empowered framework to systematically address software performance challenges. Key objectives include automating performance testing, issue localization, and optimization. The research will integrate...
The National Science Foundation (NSF) has awarded a $299,999 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, titled "COLLABORATIVE RESEARCH: CNS CORE: SMALL: A COMPILATION SYSTEM FOR MAPPING DEEP LEARNING MODELS TO TENSORIZED INSTRUCTIONS (DELITE)," will fund research to develop a compilation system that can optimize deep neural network (DNN) workloads for emerging tensorized instruction...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) in the amount of $426,459.00 aims to develop a tool that provides function-level insights with minimal overhead, enabling real-time tuning of high-performance computing (HPC) applications. The project addresses the challenge of performance monitoring on supercomputers by implementing function-level monitoring through dynamic binary instrumentation...
This Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), focuses on developing a time-sensitive large model training platform for dynamic data analytics. The primary objectives are to: Create methods to dynamically refine and adapt existing large-scale deep learning models in real-time, reducing the need for time-consuming retraining. Validate the practicality of the developed...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
This $727,999 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to develop architectural "Data Transformation Units" (DTUs) that can improve data locality and processing performance for high-dimensional data workloads. The project, led by Trustees of Boston University, aims to create a foundational science of on-the-fly data transformation, explore architectural...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CISE) program, titled "COLLABORATIVE RESEARCH: CNS CORE: SMALL: A COMPILATION SYSTEM FOR MAPPING DEEP LEARNING MODELS TO TENSORIZED INSTRUCTIONS (DELITE)", provides $299,999 in funding to the University Of Georgia Research Foundation, Inc. over a 3-year period from October 1, 2023 through September 30, 2026. The grant supports the development of a compilation system...