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 $199,989 project grant, awarded by the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to research and develop advanced lossy data compression techniques that preserve topological features in large-scale scientific data. The project at The Ohio State University will tackle the data compression, analysis, and visualization needs of extreme-scale scientific simulations by creating a...
This Project Grant from the National Science Foundation's Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $203,483 to Indiana University to develop an objective-driven adaptive hybrid lossy compression framework for extreme-scale scientific applications. The framework aims to automatically construct the best-fit compression strategy for diverse user objectives in data-intensive scientific 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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) for $299,354 provides funding to develop a novel learning-driven framework to mitigate artifacts produced by scientific data compressors. The project aims to improve the integrity and quality of lossy-compressed scientific data, facilitating more efficient data storage, transmission, and analytics across domains including climatology, cosmology, fusion energy...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) Project Grant award of $272,992 will support a collaborative research project at Texas State University titled "SCIOPT: Toward Certifiable Compression-Aware SCIML Systems." The project aims to develop techniques to reduce the volume of data exchanged in high-performance scientific computing and scientific machine learning (SCIML) applications without...
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...
The U.S. Department of Energy's Office of Science awarded a $450,000 Project Grant to The Ohio State University to develop a "Novel Framework to Design Trustworthy Lossy Compressors for Scientific Data Approaching Lossy Compressibility Limits". This award, funded through the Office of Science Financial Assistance Program (CFDA 81.049), will support fundamental scientific research to advance U.S. energy, economic, and national security priorities through transformative discoveries in...
This $350,000 Project Grant from the National Science Foundation Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering program (CFDA 47.070), will fund the development of a systematic approach to minimize compression error propagation in high-performance computing applications. Specifically, the University of Iowa will receive funding from August 2022 through July 2025 to create an accurate and efficient fault injection infrastructure integrated...
This $819,000 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) will fund a collaborative research project titled "SCIOPT: Toward Certifiable Compression-Aware SCIML Systems" at the University of Utah. The project aims to develop techniques to reduce the volume of data exchanged in high-performance scientific simulations and scientific machine learning (SCIML) applications without sacrificing accuracy. Key...
This National Science Foundation (NSF) award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $579,943 to The Ohio State University to develop a comprehensive cyberinfrastructure framework called FZ to streamline the creation of specialized lossy data compression software for scientific applications. The key products and services include:
Creating programming interfaces and a compressor generator to enable the development of new lossy compressors from high-level languages like Python and optimize their execution.
Refactoring the existing SZ lossy compressor to enable fine-grained composability of diverse data transformation modules and integrate new preprocessing, decorrelation, approximation, and entropy coding capabilities.
Providing interactive visualization, quality assessment, and graphical user interface tools to assist users in searching for optimized lossy compression module compositions and identifying appropriate compression ratio, speed, and quality trade-offs.
The award period runs from August 1, 2023 to July 31, 2027 and aims to revolutionize the development of specialized lossy compressors for scientific data management, thereby addressing critical challenges faced by many research fields in rapidly expanding data volumes and velocities.