High performance computing infrastructure for evolutionary biology, spatial ecology, and conservation biology

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Rapid advances in genomics, bioinformatics, and geomatics (collection and analysis of geographic data) have revolutionized our fields of evolutionary ecology, community ecology, and conservation biology, allowing us to gather datasets of unprecedented size and scope. Unfortunately, our ability to generate data has outstripped our ability to analyse them in a cost-effective manner; this is having profound consequences for the types and quality of analyses that can be included in student research projects, for finishing times for graduate students, and for our ability to attract to calibre students and faculty. An obvious solution to intensive computer tasks that are parallelizable, as most of our analyses are, is to use cloud computing provided by commercial providers like Google or Amazon, or to use Compute Canada resources. The former are simply too costly for NSERC-supported principal investigators like us who are funded through Evaluation Group 1503 (Ecology and Evolution), and who mentor many students and postdocs annually. Compute Canada too presents issues. First, users like our students are typically relegated to lowest priority and this can mean pronounced delays for jobs to start. Second, while there are grants (Resource Allocation Competitions) to pay for researchers to access Compute Canada clusters, these require individual PIs to exceed 50 core years of projected CPU usage an amount this is typical of large medical groups but not of the vast majority of ecology and evolution researchers. To address this dire need in our research, we propose to create a dedicated Biology sub-cluster for the Department of Biology at Queen's, facilitated by our Centre for Advanced Computing (CAC). The sub-cluster would have 80 cores with 768GB CPU RAM and 64GB GPU RAM, 96TB of accessible Network Attached Storage, an additional 80TB of long-term tape storage, and no user fees. We use over 25 different software packages or workflows and this local infrastructure coupled with the support from CAC allows us maximum flexibility to adapt software for most efficient use and develop specific pipelines for individual needs of our HQP. Their in-kind support will include tailored training for all personnel each term to maximize their ability to take advantage of this new and powerful resource. This new computer infrastructure will serve our computational needs for the next 6 years, and will directly support the research of all five PIs, at least 49 undergraduate honours student projects, 45 MSc and PhD students, and 5 postdoctoral fellows through our own research programs. While our HQP would have priority, we intend to share this new computational capacity with all faculty (>30) and students (~120 graduate students) in Biology. We will use this infrastructure to attract diverse new faculty and students of the highest calibre, and to enhance the learning and research environment for all members of our department. **