Super-Stencil: A Memory-Efficient Superstep Wave Propagation Method for Seismic Imaging - Gigilas, George; Peixoto, Pedro S.; Senger, Hermes; Yviquel, Hervé (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2025)
Wave propagation is a fundamental component of seismic imaging, a technique crucial to oil and gas exploration. Traditional finite-difference (FD) methods advance the wave-field one time step at a time. While effective, these methods are memory-bound and require multiple high-end GPU nodes to achieve acceptable performance. To address this, the superstep wavefield propagation technique was introduced, grouping multiple time steps into a single large operator to increase computational intensity. However, it incurs a prohibitive memory overhead, requiring the storage of hundreds of large matrices for realistic problems. In this work, we introduce Super-Stencil, a novel symbolic formulation of superstep propagation that eliminates the need to store intermediate operators. This drastically reduces memory consumption—by about 1009x compared to the original superstep method for 20 time steps—shifting the computational bottleneck from memory to compute. Although Super-Stencil incurs up to 9.1× longer execution time than superstep and 337.3× longer than the FD baseline in a sequential setting, it unlocks a new dimension of parallelism through a Parallel-in-Time execution strategy. By enabling simultaneous time and space parallelism, Super-Stencil transforms wave propagation into a compute-bound kernel, opening the door to aggressive optimization on modern parallel architectures. This makes it a compelling alternative for next-generation seismic imaging workflows where scalability is paramount.