Exploiting temporal data reuse and asynchrony in the reverse time migration

Abstract

Reverse Time Migration (RTM) is a state-of-the-art algorithm used in seismic depth imaging in complex geological environments for the oil and gas exploration industry. It calculates high-resolution images by solving the three-dimensional acoustic wave equation using seismic datasets recorded at various receiver locations. Reverse Time Migration’s computational phases are predominantly composed of stencil computational kernels for the finite-difference time-domain scheme, applying the absorbing boundary conditions, and I/O operations needed for the imaging condition. In this paper, we integrate the asynchronous Multicore Wavefront Diamond (MWD) tiling approach into the full RTM workflow. Multicore Wavefront Diamond permits to further increase data reuse by leveraging spatial with Temporal Blocking (TB) during the stencil computations. This integration engenders new challenges with a snowball effect on the legacy synchronous RTM workflow as it requires rethinking of how the absorbing boundary conditions, the I/O operations, and the imaging condition operate. These disruptive changes are necessary to maintain the performance superiority of asynchronous stencil execution throughout the time integration, while ensuring the quality of the subsurface image does not deteriorate. We assess the overall performance of the new MWD-based RTM and compare against traditional Spatial Blocking (SB)-based RTM on various shared-memory systems using the SEG Salt3D model. The MWD-based RTM achieves up to 70% performance speedup compared to SB-based RTM. To our knowledge, this paper highlights for the first time the applicability of asynchronous executions with temporal blocking throughout the whole RTM. This may eventually create new research opportunities in improving hydrocarbon extraction for the petroleum industry.

A compression-based memory-efficient optimization for out-of-core GPU stencil computation

“…Qu et al [30], [31] leveraged an asynchronous multicore wavefront diamond tiling approach to optimize stencil computational kernels, enhancing data reuse during temporal blocking is being performed. Nonetheless, diamond tiling introduces complex dependencies between tiles (i.e., chunks), which hinders the process of streaming chunks to and from the GPU.…”