The importance of graph applications is growing as society becomes more interconnected. Many real world datasets are best modeled as graphs, e.g., road networks, the Internet, social networks, protein-protein interactions, utility grids, and communication. Graph analytics be used to answer many different categories of questions, including traversal, querying, and data mining. It is often desirable to execute graph analytics applications on a wide range of hardware platforms (from supercomputers to mobile devices). The equivalence between a graph and the associated sparse matrix encoding a graph's adjacency list has prompted considerable interest in casting various graph algorithms in terms of sparse matrix operations. We propose to identify a set of high-level matrix and graph primitives and to create efficient implementations of primitives for multiple platforms. This will serve the basis for a performance portable graph framework. A runtime graph analysis engine will also be developed and integrated into the framework (to select the best algorithms and data structures based on the graph and compute hardware). This will allow graph analytics experts to develop performance portable graph applications without concern for the low-level optimizations required to target their application to a particular hardware platform for multiple graph layouts.
What’s New:
August 2023
RNET has recently been awarded a Department of Energy Phase II SBIR to develop a Machine Learning Based Data Compression (MLDC) algorithm for numeric simulation data.
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July 2023
RNET has recently been awarded a NASA Phase I SBIR to develop a Rapid Data Analytics Platform using machine learning uses a novel dimensional reduction algorithm.
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June 2023
RNET has recently been awarded an NIH Phase II SBIR to design and develop Machine Learning tools to help Pathologists overcome the limitations of current computing hardware to design more accurate deep learning models for use in clinical diagnostics. These models will be able to analyze very large digitized images of glass slides (i.e., Whole Slide Images) to aid pathologists in tasks like cancer detection. The ability to analyze these images in their entirety instead of in small parts will improve the diagnostic accuracy of models and will accelerate algorithm development efforts.