Exaflops Biomedical Knowledge Graph Analytics

Ramakrishnan Kannan; Piyush Sao; Hao Lu; Jakub Kurzak; Gundolf Schenk; Yongmei Shi; Seung Hwan Lim; Sharat Israni; Vijay Thakkar; Guojing Cong; Robert Patton; Sergio E. Baranzini; Richard Vuduc; Thomas Potok

doi:10.1109/SC41404.2022.00011

Exaflops Biomedical Knowledge Graph Analytics

Ramakrishnan Kannan
, Piyush Sao
, Hao Lu
, Jakub Kurzak
, Gundolf Schenk
, Yongmei Shi
, Seung Hwan Lim
, Sharat Israni
, Vijay Thakkar
, Guojing Cong
, Robert Patton
, Sergio E. Baranzini
, Richard Vuduc
, Thomas Potok

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.

Original language	English
Title of host publication	Proceedings of SC 2022
Subtitle of host publication	International Conference for High Performance Computing, Networking, Storage and Analysis
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665454445
DOIs	https://doi.org/10.1109/SC41404.2022.00011
State	Published - 2022
Event	2022 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2022 - Dallas, United States Duration: Nov 13 2022 → Nov 18 2022

Publication series

Name	International Conference for High Performance Computing, Networking, Storage and Analysis, SC
Volume	2022-November
ISSN (Print)	2167-4329
ISSN (Electronic)	2167-4337

Conference

Conference	2022 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2022
Country/Territory	United States
City	Dallas
Period	11/13/22 → 11/18/22

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/ downloads/doe-public-access-plan). 1https://spoke.ucsf.edu This material is based upon work supported by the US Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research (Robinson Pino, program manager) under contract DE-AC05-00OR22725 and by the National Science Foundation (NSF) under award number 1710371. SPOKE development was funded in substantial part by the NSF Convergence Accelerator awards 1937160 and 12033569. This research used resources of the OLCF which is a DOE Office of Science User Facility supported under contract DE-AC05-00OR22725.

Keywords

High-Performance Computing
Parallel Algorithms
Shortest Path Problem

Access to Document

10.1109/SC41404.2022.00011

Cite this

Kannan, R., Sao, P., Lu, H., Kurzak, J., Schenk, G., Shi, Y., Lim, S. H., Israni, S., Thakkar, V., Cong, G., Patton, R., Baranzini, S. E., Vuduc, R., & Potok, T. (2022). Exaflops Biomedical Knowledge Graph Analytics. In Proceedings of SC 2022: International Conference for High Performance Computing, Networking, Storage and Analysis (International Conference for High Performance Computing, Networking, Storage and Analysis, SC; Vol. 2022-November). IEEE Computer Society. https://doi.org/10.1109/SC41404.2022.00011

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title = "Exaflops Biomedical Knowledge Graph Analytics",

abstract = "We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99\% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.",

keywords = "High-Performance Computing, Parallel Algorithms, Shortest Path Problem",

author = "Ramakrishnan Kannan and Piyush Sao and Hao Lu and Jakub Kurzak and Gundolf Schenk and Yongmei Shi and Lim, \{Seung Hwan\} and Sharat Israni and Vijay Thakkar and Guojing Cong and Robert Patton and Baranzini, \{Sergio E.\} and Richard Vuduc and Thomas Potok",

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year = "2022",

doi = "10.1109/SC41404.2022.00011",

language = "English",

series = "International Conference for High Performance Computing, Networking, Storage and Analysis, SC",

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Kannan, R , Sao, P , Lu, H, Kurzak, J, Schenk, G, Shi, Y, Lim, SH, Israni, S, Thakkar, V, Cong, G, Patton, R, Baranzini, SE, Vuduc, R & Potok, T 2022, Exaflops Biomedical Knowledge Graph Analytics. in Proceedings of SC 2022: International Conference for High Performance Computing, Networking, Storage and Analysis. International Conference for High Performance Computing, Networking, Storage and Analysis, SC, vol. 2022-November, IEEE Computer Society, 2022 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2022, Dallas, United States, 11/13/22. https://doi.org/10.1109/SC41404.2022.00011

Exaflops Biomedical Knowledge Graph Analytics. / Kannan, Ramakrishnan ; Sao, Piyush ; Lu, Hao et al.
Proceedings of SC 2022: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society, 2022. (International Conference for High Performance Computing, Networking, Storage and Analysis, SC; Vol. 2022-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Exaflops Biomedical Knowledge Graph Analytics

AU - Kannan, Ramakrishnan

AU - Sao, Piyush

AU - Lu, Hao

AU - Kurzak, Jakub

AU - Schenk, Gundolf

AU - Shi, Yongmei

AU - Lim, Seung Hwan

AU - Israni, Sharat

AU - Thakkar, Vijay

AU - Cong, Guojing

AU - Patton, Robert

AU - Baranzini, Sergio E.

AU - Vuduc, Richard

AU - Potok, Thomas

PY - 2022

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N2 - We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.

AB - We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.

KW - High-Performance Computing

KW - Parallel Algorithms

KW - Shortest Path Problem

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DO - 10.1109/SC41404.2022.00011

M3 - Conference contribution

AN - SCOPUS:85149282520

T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC

BT - Proceedings of SC 2022

PB - IEEE Computer Society

T2 - 2022 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2022

Y2 - 13 November 2022 through 18 November 2022

ER -

Kannan R , Sao P , Lu H, Kurzak J, Schenk G, Shi Y et al. Exaflops Biomedical Knowledge Graph Analytics. In Proceedings of SC 2022: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society. 2022. (International Conference for High Performance Computing, Networking, Storage and Analysis, SC). doi: 10.1109/SC41404.2022.00011

Exaflops Biomedical Knowledge Graph Analytics

Abstract

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Funding

Keywords

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