Automatic Performance Tuning of Parallel and Accelerated Seismic Imaging Kernels

With the increased complexity and diversity of mainstream high performance computing systems, significant effort is required to tune parallel applications in order to achieve the best possible performance for each particular platform. This task becomes more and more challenging and requiring a larger set of skills. Automatic performance tuning is becoming a must for optimizing applications such as Reverse Time Migration (RTM) widely used in seismic imaging for oil and gas exploration. An empirical search based auto-tuning approach is applied to the MPI communication operations of the parallel isotropic and tilted transverse isotropic kernels. The application of auto-tuning using the Abstract Data and Communication Library improved the performance of the MPI communications as well as developer productivity by providing a higher level of abstraction. Keeping productivity in mind, we opted toward pragma based programming for accelerated computation on latest accelerated architectures such as GPUs using the fairly new OpenACC standard. The same auto-tuning approach is also applied to the OpenACC accelerated seismic code for optimizing the compute intensive kernel of the Reverse Time Migration application. The application of such technique resulted in an improved performance of the original code and its ability to adapt to different execution environments.