Auto-Tuning for OpenMP Dynamic Scheduling applied to Full Waveform Inversion

TL;DR

This paper introduces an auto-tuning strategy using meta-heuristics to optimize OpenMP dynamic scheduling in Full Waveform Inversion, significantly reducing computation time in seismic data processing.

Contribution

It proposes a novel auto-tuning approach with Coupled Simulated Annealing to automatically select optimal chunk sizes for dynamic scheduling in FWI, improving efficiency.

Findings

Runtime reduced by up to 70.46% with auto-tuning

Auto-tuning adapts chunk size to problem size and environment

Significant performance gains in seismic data processing

Abstract

Full Waveform Inversion (FWI) is a widely used method in seismic data processing, capable of estimating models that represent the characteristics of the geological layers of the subsurface. Because it works with a massive amount of data, the execution of this method requires much time and computational resources. Techniques such as FWI adapt well to parallel computing and can be parallelized in shared memory systems using the application programming interface (API) OpenMP. The management of parallel tasks can be performed through loop schedulers contained in OpenMP. The dynamic scheduler stands out for distributing predefined fixed-size chunk sizes to idle processing cores at runtime. It can better adapt to FWI, where data processing can be irregular. However, the relationship between the size of the chunk and the runtime is unknown. Optimization techniques can employ meta-heuristics to explore the parameter search space, avoiding testing all possible solutions. Here, we propose a strategy to use the Parameter Auto-Tuning for Shared Memory Algorithms (PATSMA), with Coupled Simulated Annealing (CSA) as its optimization method, to automatically adjust the chunk for the dynamic scheduling of wave propagation, one of the most expensive steps in FWI. Since testing each candidate chunk in the complete FWI is unpractical, our approach consists of running a PATSMA where the objective function is the runtime of the first time iteration of the first seismic shot of the first FWI iteration. The resulting chunk is then employed in all wave propagations involved in an FWI. We conducted tests to measure the runtime of an FWI using the proposed auto-tuning, varying the problem size and running on different computational environments. The results show that applying the proposed auto-tuning in an FWI reduces its runtime by up to 70.46% compared to standard OpenMP schedulers.