Abstract
To improve the performance of stencil computations tend to be challenging due to their inherent memory access pattern. A condition that remains valid for wave propagation simulation engines. Moving towards using elastic waves instead of acoustic ones (e.g., used in medical imaging), results in computationally more expensive processes along with an increment on memory usage. Despite the computational demand, the elevated cost of exploration joined to a low success rate drove the Oil & Gas industry to rely on elastic anisotropic wave propagation models as the core of many geophysical imaging mechanisms to explore subsurface without an excessive investment. Moreover, exploration codes are expected to run fast and efficiently on modern architectures. The Intel® Xeon Phi™ processors emerge as an energy efficient solution that provides a good trade-off between market price and computing capability. In this paper, we study the effect of several optimization techniques using the YASK framework to implement and evaluate a 25-points stencil of an elastic wave propagation engine for Intel Xeon Phi processors. The results showed improvements of up to 7× in computations and 8× in memory bandwidth with respect to the non-tuned version, reaching up to 75% of the attainable performance. We collected performance metrics for a set of the most representative optimizations and revealed the relation between each strategy and fundamental characteristics of both code and hardware.
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