Projects

This award supports the development of advanced computational methods for tracking and analyzing evolving patterns in large-scale networks. Patterns of connections among entities, known as subgraphs, underpin insights in domains such as social interactions, biological processes, financial transactions, and communication systems.

Community detection methods enable an understanding of the structure of networks at multiple scales. While many methods exist, only a few are able to scale to large networks and/or are implemented in large computational infrastructure.

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. This award will design and implement fundamental algorithms for high performance computing solutions that enable the interactive large-scale data analysis of massive data sets.

Research Directions Graph algorithms represent some of the most challenging known problems in computer science for modern processors. These algorithms contain far more memory access per unit of computation than traditional scientific computing.

Facebook AI Systems Hardware/Software Co-Design research award on Scalable Graph Learning Algorithms https://research.fb.com/blog/2019/05/announcing-the-winners-of-the-ai-system-hardware-software-co-design-research-awards/ Deep learning has boosted the machine learning field at large and created significant increases in the performance of tasks including speech recognition, image classification, object detection, and recommendation.

High-Performance Streaming Graph Analytics on GPUs

Dynamic graphs are all around us. Social networks containing interpersonal relationships and communication patterns. Information on the Internet, Wikipedia, and other datasources. Disease spread networks and bioinformatics problems. Business intelligence and consumer behavior.

dynamic graph data structures and streaming algorithms for GPU

Scalable Multicore Code for RNA Secondary Structure Prediction

The GraphBLAS Forum is an open effort to define standard building blocks for graph algorithms in the language of linear algebra. We believe that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks. We believe that it is critical to move quickly and define such a standard, thereby freeing up researchers to innovate and diversify at the level of higher level algorithms and graph analytics applications. This effort was inspired by the Basic Linear Algebra Subprograms (BLAS) of dense Linear Algebra, and hence our working name for this standard is “the GraphBLAS”.

Cray XMT software developed in collaboration with PNNL

an open source library for developing efficient and portable implementations that make use of multi-core processors

High-performance software for computational phylogeny