  There has been considerable research into improving Fast Fourier Transform
(FFT) performance through parallelization and optimization for specialized
hardware. However, even with those advancements, processing of very large
files, over 1TB in size, still remains prohibitively slow. Analysts performing
signal processing are forced to wait hours or days for results, which results
in a disruption of their workflow and a decrease in productivity. In this paper
we present a unique approach that not only parallelizes the workload over
multi-cores, but distributes the problem over a cluster of graphics processing
unit (GPU)-equipped servers. By utilizing Hadoop and CUDA, we can take
advantage of inexpensive servers while still exceeding the processing power of
a dedicated supercomputer, as demonstrated in our result using Amazon EC2.
