The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

• Published in: Scientific and statistical database management : International Conference, SSDBM ... : proceedings. International Conference on Scientific and Statistical Database Management
• Main Authors: Burns, Randal, Roncal, William Gray, Kleissas, Dean, Lillaney, Kunal, Manavalan, Priya, Perlman, Eric, Berger, Daniel R, Bock, Davi D, Chung, Kwanghun, Grosenick, Logan, Kasthuri, Narayanan, Weiler, Nicholas C, Deisseroth, Karl, Kazhdan, Michael, Lichtman, Jeff, Reid, R Clay, Smith, Stephen J, Szalay, Alexander S, Vogelstein, Joshua T, Vogelstein, R Jacob
• Format: Journal Article
• Language: English
• Published: Germany 2013
• Subjects: Data-intensive computing; Connectomics
• DOI: 10.1145/2484838.2484870

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build - neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.