“HORT: Hadoop online ray tracing with mapreduce” by Northam

  • ©Lesley Northam




    HORT: Hadoop online ray tracing with mapreduce



    High quality computer-generated imagery (CGI) rendering is a demanding computational task [Hearn and Baker 1997]. To generate high-quality CGI, film studios rely on expensive, specialized, rendering clusters (render farms) [Rath 2009]. Render farms cost a premium over more general infrastructure such as infrastructure as a service (IaaS) offerings. Furthermore, render farms often provide limited selection of commercial rendering applications and have significant overhead with respect to requesting a job. This contrasts general IaaS offerings such as Amazon’s Elastic Compute Cloud (EC2), which allows for straight-forward, automated provisioning of many virtualized machine instances.


    1. Bialecki, A., et al., 2005. Hadoop: a framework for running applications on large clusters built of commodity hardware. http://lucene.apache.org/hadoop.
    2. Dean, J., and Ghemawat, S. 2008. MapReduce: Simplified data processing on large clusters. Communications of the ACM 51, 1, 107–113.
    3. Hearn, D., and Baker, M. P. 1997. Computer graphics (2nd ed.): C version. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
    4. Rath, J., 2009. The data-crunching powerhouse behind ‘avatar’. http://www.datacenterknowledge.com/archives/2009/12/22/the-data-crunching-power, December.

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