Projects

High Performance Algorithms for Interactive Data Science at Scale

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.

NVIDIA AI Lab (NVAIL) for Scalable Graph Algorithms

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 Research

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.

HORNET

High-Performance Streaming Graph Analytics on GPUs

STINGER

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.

cuSTINGER

dynamic graph data structures and streaming algorithms for GPU

GTfold

Scalable Multicore Code for RNA Secondary Structure Prediction

GraphBLAS

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”.

Graph500

GraphCT: Graph Characterization Toolkit

Cray XMT software developed in collaboration with PNNL

Multicore SWARM: Software and Algorithms for Running on Multicore Processors

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

GRAPPA: Genome Rearrangements Analysis under Parsimony and other Phylogenetic Algorithms

High-performance software for computational phylogeny