MapD Technologies, the GPU-accelerated database specialist, said it is working with university researchers on leveraging graphics processors to advance geospatial analytics.
The San Francisco-based company is collaborating with the Center for Geographic Analysis at Harvard University. The research center is a member of university-industry consortium backed by the National Science Foundation called the Spatiotemperal Innovation Center.
The partners said center researchers would use MapD’s GPU-based tools to analyze and visualize billions of rows of geospatial data to search for insights into “natural and social phenomena,” including hydrological models used for water management and public safety, the partners said Wednesday (Aug. 16).
The geospatial analytics effort also will focus on detailed weather forecasts and field observations on streams and reservoirs. These data will be used to predict water flow, saturation and flooding, which are becoming increasingly common as the number of extreme weather events increases. The partners said they expect to generate billion of hydrological predictions.
Part of the impetus for the project is the enormous computing demands associated with the current U.S. National Water Model, which incorporates data from about 7,000 river gauges along with weather models and other geospatial data. The model makes hourly predictions on water flows from 2.7 million stream outlets and 1,260 reservoirs.
The predictions are delivered as “preprocessed visuals,” MapD noted, but are not currently available as an interactive application due to computing limits. “It is hoped that GPU-based analytics will support faster visualization of data sets like the U.S. National Water Model and enrich them with data such as flood or drought vulnerabilities, local population densities, emergency response availability and even social media sentiment about water policies,” MapD noted in a statement announcing the collaboration.
Center officials explained that they previously relied on data preparation and CPU-based computing resources to churn through large spatio-temporal data sets that were then integrated with external sources. “We hope to explore whether a GPU-based platform will enable testing hypotheses as we think of them, using a fraction of the computing resources at a much lower cost,” added Ben Lewis, geospatial technology manager at the Center for Geographic Analysis.
The Harvard Center will leverage MapD’s open source database and visualization libraries along with its Immerse visual analytics client. In exchange, the university researchers will look to add new geospatial features to the company’s platform while extending support for open geospatial standards.
MapD’s analytics approach leverages graphics processors to accelerate SQL queries and visualizations of large data sets. Aggressive startups such as Kinetica and MapD are pushing the boundaries of GPU processing and in-memory techniques to developed real-time analytics platforms for faster SQL queries. Along with geospatial data, these companies are also focusing on deep learning and other applications.
MapD’s roots are in university research. Founded in 2013, it was spun off from MIT’s Computer Science and Artificial Intelligence Laboratory. Other seed investors include GPU specialist Nvidia and In-Q-Tel, the CIA’s venture capital arm.