  There is a strong need for high-accuracy and efficient modeling of
extreme-mass-ratio binary black hole systems because these are strong sources
of gravitational waves that would be detected by future observatories. In this
article, we present sample results from our Teukolsky EMRI code: a time-domain
Teukolsky equation solver (a linear, hyperbolic, partial differential equation
solver using finite-differencing), that takes advantage of several mathematical
and computational enhancements to efficiently generate long-duration and
high-accuracy EMRI waveforms.
  We emphasize here the computational advances made in the context of this
code. Currently there is considerable interest in making use of many-core
processor architectures, such as Nvidia and AMD graphics processing units
(GPUs) for scientific computing. Our code uses the Open Computing Language
(OpenCL) for taking advantage of the massive parallelism offered by modern GPU
architectures. We present the performance of our Teukolsky EMRI code on
multiple modern processor architectures and demonstrate the high level of
accuracy and performance it is able to achieve. We also present the code's
scaling performance on a large supercomputer i.e. NSF's XSEDE resource:
Keeneland (a 201 TeraFLOP/s, 120-node HP SL390 system with 240 Intel Xeon 5660
CPUs and 360 NVIDIA Fermi M2070 graphics processors, with the nodes connected
by an InfiniBand QDR network).
