PNI is committed to maintaining leading-edge research computing infrastructure and support services which help support the creation of novel data collection and analysis methods. PNI’s computational infrastructure supports data-driven research methods that require substantial computational resources coupled with resilient, high-performance research data management and storage services. These systems and services support computational research methods from traditional image analysis, through natural language processing using deep learning neural networks, from basic fMRI analysis, through machine learning to extract brain structure from lightsheet and electron microscopy. PNI is grateful to the Huo Family Foundation Computational and Theoretical Neuroscience Fund, the National Science Foundation, Intel, and others for supporting this computing infrastructure. Computing Support and Documentation PNI computing support can reached at [email protected], and documentation and answers to frequently asked questions can be found at the computing support wiki. Resources PNI’s information technology infrastructure has been designed to support research methods requiring extremely high-performance processing coupled with low-latency, high-speed connectivity, such as the integration of fMRI scanners with HPC clusters, enabling real-time analysis of fMRI data with complex algorithms. Research Data Storage Collection and analysis of data is central to Institute research, and the need for a centralized, reliable, shared storage pool is critical. The central file server provides 10 petabytes of usable storage for research data, analyses, and administrative data.Data from the file server is accessible on the PNI compute cluster and from user workstations both on campus and off. This greatly facilitates collaboration and the sharing of data, and eliminates the need for multiple copies of datasets, saving time and disk space. General Purpose Compute Cluster High Performance Computing needs are served by a local compute cluster with access to research data storage. The cluster has substantial memory, CPU and GPU resources.
