Project Grant DESC0025228

This two-year, $174,693 National Science Foundation project grant funds the development of a parallel and distributed framework for graph mining on graphics processing units (GPUs) at Rowan University from June 2023 to May 2025. The Computer and Information Science and Engineering program aims to advance computing and communications research and education. The project will create a new framework to accelerate fundamental graph computations across multiple GPUs for applications such as...

This $1,091,988 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework to systematically design and optimize high-performance graph analytics algorithms. The researchers at the University of California, Davis (UC Davis) will create an open-source software platform that allows for automated exploration of implementation choices for graph computations using a...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering program (CFDA 47.070) supports a collaborative research effort to accelerate the execution of large-scale graph problems on distributed computing systems. The $2,656,268 award, active from August 1, 2023 to July 31, 2028, aims to develop new algorithms, software frameworks, and hardware accelerators to efficiently process large graph datasets in domains like computational...

This Project Grant award of $203,796 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to accelerate the execution of large graph problems on large, distributed machines. The project, led by Texas A&M Engineering Experiment Station (Tees), aims to develop new algorithms, software frameworks, and hardware accelerators to enable efficient processing of graph-based computations in domains such as...

This $274,765 project grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) establishes a foundation for efficient and intelligent unified memory design to harness the power of advanced GPU accelerators. The key products and services to be delivered include: Developing an abstraction framework called ACCORD to capture the spatial and temporal patterns of massively parallel memory accesses, enabling quantitative...

This Project Grant award, valued at $568,008.00 and provided by the National Science Foundation (NSF) under its Computer and Information Science and Engineering (CFDA 47.070) program, supports research to explore new computational capabilities in graph machine learning. The key products and services to be delivered under this award include: Developing a non-canonical representation of graphs that expresses them as a combination of intersecting cliques or communities, enabling scalable data...

This three-year, $290,739 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will support the development of a novel non-volatile nano-second right-grained reconfigurable architecture for data-intensive machine learning and graph computing applications. The proposed computing architecture, called Right-Grained Reconfigurable Architecture (RGRA), combines aspects of coarse-grained reconfigurable arrays and field-programmable gate arrays...

This three-year, $532,241 Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of scalable algorithms, systems, and infrastructures for graph neural network training. The University of Massachusetts will develop a novel "split parallelism" training paradigm to transparently scale graph neural network training to large-scale graphs...

This $1.2 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Pittsburgh to expedite machine learning applications on multi-GPU infrastructure. Specifically, the university will uncover and address architectural bottlenecks in deep neural network executions on multi-GPU systems. Researchers will redesign translation lookaside buffer hierarchies and page table walks to reduce address...

This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) is focused on enhancing machine learning with graph-structured data. The research aims to address the challenge of data distribution shifts in AI models when applied to real-world scenarios, particularly in fields like particle physics and biochemistry. The key activities under this 3-year award include: Developing methods to estimate and...