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Virtualizing Lifemapper: Enhancing Computational Tier for Biodiversity Analysis

This document details the Computational Tier of Lifemapper, a powerful tool for modeling species distribution and analyzing biodiversity. Contributions from researchers at the University of Kansas Biodiversity Institute and the University of California, San Diego, highlight the integration of species occurrence data with advanced computational methods. Key features include potential habitat mapping, job tracking, and data management tools that facilitate biodiversity research. Lifemapper leverages opportunities from NSF funding to enhance job execution and data analysis across various platforms.

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Virtualizing Lifemapper: Enhancing Computational Tier for Biodiversity Analysis

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  1. Virtualizing Lifemapper for PRAGMA: Step 2 - The Computational Tier By Aimee Stewart, Cindy Zheng, Phil Papadopoulos, C.J. Grady University of Kansas Biodiversity Institute University of California, San Diego http://www.lifemapper.org

  2. Lifemapper • Data • Climate Data • Species Archive • Input: points • Output: potential habitat maps • Tools • LmSDM: Species Distribution Modeling • LmRAD: Range and Diversity GBIF

  3. Lifemapper Tiers Data Tier Compute Tier Management Tier Pipeline Species Distribution Modeling Range and Diversity Analysis Web Tier Client Tier Species Sensors Desktop client Browser client Lm Web site REST Web Services Models Data updater Job generator Job tracker Metadata manager Virtual clusters request Jobs Result archives Job submission Data request Result request Experiment management, Result visualization, exploration, analysis

  4. Virtual Biodiversity ExpeditionStep 1:Species Distribution Modeling 1 2 5 3 Species Occurrence Data 4 3 Potential Habitat Landsat Data

  5. Adding a PRAGMA Compute Tier LM Management LM Compute LM Compute LM Data

  6. DEMO

  7. Preparation Data Tier Compute Tier Management Tier Pipeline Species Distribution Modeling Range and Diversity Analysis Web Tier Client Tier Species Sensors Desktop client Browser client Lm Web site REST Web Services Models Data updater Job generator Job tracker Metadata manager Virtual clusters request Jobs Result archives Job submission Data request Result request Experiment management, Result visualization, exploration, analysis

  8. Execution • Specialization • Resources request subset of available jobs • Based on data, users, computation type • Scaling • Management - Increased job speed • Compute – increased data replication

  9. Lifemapper funded by: U.S. National Science Foundation NSF EPSCoR 0553722 NSF EPSCoR 0919443 EHR/DRL 0918590 BIO/DBI 0851290 OCI/CI-TEAM 0753336 http://www.lifemapper.org astewart@ku.edu

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