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  1. ; ; ; ; ; ; ; ( , 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Workshop on Graphs, Architecture, Programming, and Learning)
    The GraphBLAS emerged from an international effort to standardize linear-algebraic building blocks for computing on graphs and graph-structured data. The GraphBLAS is expressed as a C API and has paved the way for multiple implementations. The GraphBLAS C API, however, does not define how distributed-memory parallelism should be handled. This paper reviews various approaches for a GraphBLAS API for distributed computing. This work is guided by our experience with existing distributed memory libraries. Our goal for this paper is to highlight the pros and cons of different approaches rather than to advocate for one particular choice. 
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  2. ; ; ( , Proceedings of the 31st IEEE International Parallel and Distributed Processing Symposium)
    On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with scalable computation and a model for scalable communication for BFS and direction-optimized BFS. Our communication model uses global reduction for high-degree vertices, and point-to-point transmission for low-degree vertices. Leveraging the characteristics of degree separation, we reduce the graph size to one third of the conventional edge list representation. With several other optimizations, we observe linear weak scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access system.Proceedings of the 31st IEEE International Parallel and Distributed Processing Symposium 
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , AI Magazine)
    Abstract

    Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article, we describe concrete uses of Scalable PrecisiOn Medicine Knowledge Engine (SPOKE), an open knowledge network that connects curated information from thirty‐seven specialized and human‐curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID‐19 research and chronic disease diagnosis, and management.

     
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