Project Grant 2609203
- This five-year, $899,840 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at The Ohio State University to develop a comprehensive framework for efficient, scalable, and performance-portable tensor applications. The University will contribute expertise in algorithms, software, and hardware to address challenges in sustaining improved performance and energy efficiency as transistor resources become bounded, and in...
- This five-year, $450,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at The Ohio State University to develop a comprehensive framework for efficient, scalable, and performance-portable tensor applications. The University will collaborate with other researchers to address challenges in sustaining improved computing performance and energy efficiency given the end of Moore's Law. It will also seek to improve...
- This $250,000 Project Grant award from the National Science Foundation's Engineering Program (CFDA 47.041) supports collaborative research at The Ohio State University (OSU) focused on scalable, robust, and distributed approaches for recovering low-dimensional tensor representations from incomplete measurements. The project aims to develop computationally and statistically efficient optimization methods that can directly operate on the low-dimensional tensor structures, overcoming challenges...
- Federal Project Grant Award Summary The University of Utah received a $299,997 Project Grant award from the National Science Foundation's Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to develop infrastructure for high-performance distributed sparse tensor computations. The project, which commenced July 1, 2026, and concludes June 30, 2029, will deliver software infrastructure and computational tools designed to...
- This Project Grant award from the National Science Foundation Division of Computing and Communication Foundations provides $125,000 in funding to Oregon State University from October 1, 2022 to September 30, 2023. The award supports research into cross-layer coordination and optimization for scalable and sparse tensor networks under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). Specifically, the award will fund preliminary research exploring memory...
- The National Science Foundation awarded The Ohio State University a $600,000 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will fund development of an open-source GPU-accelerated software tool for topological data analysis applications from August 1, 2023 through July 31, 2026. Specific objectives include designing enhanced algorithms to efficiently parallelize computations and minimize data movement; enabling the...
- Federal Grant Award Summary The National Science Foundation's Office of Advanced Cyberinfrastructure awarded The Ohio State University a $584,257 Project Grant (CFDA 47.070 - Computer and Information Science and Engineering) effective July 1, 2025 through June 30, 2028 to develop AI4MPI, an artificial intelligence-driven optimization framework for the Message Passing Interface (MPI) library. The project delivers novel AI techniques and methodologies designed to automate and accelerate the...
- The National Science Foundation (NSF) awarded a $269,310 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Ohio State University to develop a unified, tunable full-stack foundation for highly expressive chain-forward programming that can be deployed at scale. The 5-year project, which commenced on August 1, 2023, aims to advance declarative reasoning capabilities by scaling to structured, higher-order, and probabilistic formulations, as well as...
- This $125,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA #47.070) supports research at the University of California, Merced on developing optimized sparse tensor network algorithms and specialized accelerator architectures. The one-year award beginning October 1, 2022 aims to address challenges in high-dimensional data computation and analytics using tensor representations by exploring memory heterogeneity-aware data...
- The National Science Foundation awarded The Ohio State University a $110,335 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to conduct research on deep sparse models from August 1, 2022 to June 30, 2023. Specifically, the university will advance the theoretical understanding of deep convolutional neural networks through analyzing and developing algorithms for multi-layered convolutional sparse models. Researchers will derive provable and...
Federal Grant Award Summary The Ohio State University received a $299,767 Project Grant from the National Science Foundation's Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering program (CFDA 47.070) for the period July 1, 2026 through June 30, 2029. This collaborative research initiative will develop software infrastructure for high-performance distributed sparse tensor computations, addressing critical gaps in computational support for scientific and engineering applications, particularly in quantum chemistry and machine learning. The project will deliver multiple integrated products: Multi-Level Intermediate Representation (MLIR) compiler infrastructure integration for enhanced sustainability and dissemination; novel hash-based data structures and parallel algorithms optimized for sparse tensors; operator fusion optimizations to reduce memory consumption and improve performance in tensor operator graphs; and distributed tensor data structures with compiler support to minimize data movement costs across multiple computing nodes. The infrastructure developed through this award will enable scientists and researchers to achieve three primary outcomes: high-performance sparse tensor computations, reduced development effort, and performance portability across distributed computing environments. By providing robust support for the specialized algorithms and data representations required for high-dimensional sparse tensors, the project will accelerate software development cycles and improve the scalability of distributed scientific computations in key research domains.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $299.8k | 4/24/26 |