“MapD: A GPU-powered Big Data Analytics and Visualization Platform”
Conference:
Type(s):
Entry Number: 73
Title:
- MapD: A GPU-powered Big Data Analytics and Visualization Platform
Presenter(s)/Author(s):
Abstract:
MapD, or “Massively Parallel Database”, is a big data analytics platform that can query and visualize big data up to 100x faster than other systems. It leverages the massive parallelism of commodity GPUs to execute SQL queries over multi-billion row datasets with millisecond response times, and optionally render the results using the GPU’s native graphics pipeline. Depending on the use case, MapD can be used as a standalone SQL database or as a data visualization suite by using its own visualization frontend (see Fig. 1) or by integrating it with other third-party toolkits.
References:
Bolz, J., Jones, J., and Others, 2012. Nv-copy-image extension. https://www.opengl.org/registry/specs/NV/copy_image.txt.
Heer, J., and Others. Vega: A visualization grammar. http://vega.github.io/vega/.
Juliano, J., King, G., and Others, 2014. Khr-image-base egl extension. https://www.khronos.org/registry/egl/extensions/KHR/EGL_KHR_image_base.txt.
King, G., Leech, J., and Pooley, A., 2015. Oes-egl-image gl extension. https://www.khronos.org/registry/gles/extensions/OES/OES_EGL_image.txt.
Keyword(s):
Acknowledgements:
We’d like to acknowledge the rest of the MapD engineering team for their excellent work – backend engineers: Alex Suhan, An- drew Seidl, Michael Thomson, & Minggang Yu, frontend engi- neers: Marc Balaban, Danny Delott, Du Hoang, Jonathan Huang, & Mike Luby, & product management: Ed O’Donnell.
