Terabyte-sized image computations on Hadoop cluster platforms

We present a characterization of four basic Terabyte-sized image computations on a Hadoop cluster in terms of their relative efficiency according to the modified Amdahl's law. The work is motivated by the lack of standard benchmarks and stress tests for big image processing operations on a Hadoop computer cluster platform. Our benchmark design and evaluations were performed on one of the three microscopy image sets, each consisting of over one half Terabyte. All image processing benchmarks executed on the NIST Raritan cluster with Hadoop were compared against baseline measurements, such as the Terasort/Teragen designed for Hadoop testing previously, image processing executions on a multiprocessor desktop and on NIST Raritan cluster using Java Remote Method Invocation (RMI) with multiple configurations. By applying our methodology to assessing efficiencies of computations on computer cluster configurations, we could rank computation configurations and aid scientists in measuring the benefits of running image processing on a Hadoop cluster.