Project Grant 2514035

Award Date 8/1/25
Completion Date 7/31/28
Dollars Obligated $180K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Lexington, KY 40526, USA
Similar Awards
This $240,000 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 a user-friendly, high-performance, and portable GPU-accelerated data reduction cyberinfrastructure called the Scientific GPU Compression Cyberinfrastructure (SGCC). The project aims to mitigate data challenges on GPU-equipped supercomputing systems, improve data analysis efficiency, and accelerate scientific...
This $179,988 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 a user-friendly, high-performance, and portable GPU-accelerated data reduction cyberinfrastructure called SGCC. The University of Houston System, a Hispanic Serving Institution, will build this open-source cyberinfrastructure by porting, extending, and optimizing existing GPU-based data compression...
This Project Grant award, valued at $299,999.00, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The project aims to develop a novel learning-driven framework to mitigate artifacts produced by scientific data compressors, which can distort both raw and post-hoc data analytics. The research will focus on characterizing compression artifacts, designing deep learning models to tackle artifact mitigation, and...
The National Science Foundation Office of Advanced Cyberinfrastructure awarded Indiana University $467,770 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) from January 2023 through December 2027. The Project Grant funding will support the development of a highly effective, usable, performant, and scalable data reduction framework for high-performance computing systems and applications. Specifically, the principal investigator will conduct research...
This $198,234 Project Grant awarded by the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure (CFDA 47.070 - Computer and Information Science and Engineering) supports research and development of advanced lossy data compression techniques that preserve topological features in scientific data. The project aims to develop algorithms to effectively reduce the size of large-scale scientific simulation data, such as from fusion and climate modeling, while preserving critical...
This Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA 47.070) program provides $201,765 to The University Of Kentucky Research Foundation (the Research Foundation) to develop advanced lossy compression techniques and software that preserve topological features in scientific data for in situ and post hoc analysis and visualization at extreme scales. The project aims to tackle...
This $494,701 Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to improve in-situ data analytics capabilities for high-performance computing (HPC) systems. The primary objectives are to develop compression techniques that allow queries to be executed directly on compressed data without decompression, implement these techniques efficiently on...
This Project Grant award of $299,354 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel learning-driven framework to mitigate artifacts produced by scientific data compressors. The goal is to improve the integrity and quality of lossy-compressed scientific data, facilitating efficient data storage, transmission, and analytics across domains such as climatology, cosmology, fusion energy...
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) Project Grant award of $280,699 to the University of Kentucky Research Foundation will fund the development of a high-performance and scalable compression-assisted Message Passing Interface (MPI) library. The goal is to address the growing gap between the increasing computing power of GPU accelerators and limited network bandwidth in high-end computing systems. The project will involve close collaboration with...
This $540,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a comprehensive framework called FZ to enable scientific users to intuitively research, compose, implement, and test specialized lossy data compression techniques. The project will build on existing capabilities from various open-source data compression tools to create an intuitive cyberinfrastructure for the composition of specialized...

This Project Grant award, with a total funding of $180,000, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The primary awardee is The University Of Kentucky Research Foundation.

The objective of this 3-year project, which runs from August 1, 2025 to July 31, 2028, is to develop a user-friendly, high-performance, and portable GPU-accelerated data reduction cyberinfrastructure, called SGCC, for scientific data workflows on GPU-equipped supercomputing systems. SGCC will port, extend, and optimize multiple existing GPU-based data compression capabilities, including error-bounded lossy compressors, lossless encoders, and quality assessment tools, to improve data analysis efficiency and accelerate scientific discovery across diverse scientific domains. The project aims to enhance the usability of the GPU-accelerated data reduction ecosystem by providing high-level language bindings, command line interfaces, and visualization integrations. Additionally, SGCC will enable state-of-the-art GPU-accelerated scientific data compressors on multiple heterogeneous computing platforms, including NVIDIA, AMD, and Intel architectures.

Generated 7/15/25, 9:35 AM