Because Full Waveform Inversion (FWI) works with a massive amount of data, its execution requires much time and computational resources, being restricted to large-scale computer systems such as supercomputers. 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 size 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 size for the dynamic scheduling of wave propagation, one of the most expensive steps in FWI. Since testing each candidate chunk size 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 size is then employed in all wave propagations involved in an FWI. We conducted tests to measure the runtime of an FWI using the proposed autotuning, varying the problem size and running on different computational environments, such as supercomputers and cloud computing instances. The results show that applying the proposed autotuning in an FWI reduces its runtime by up to 70.46% compared to standard OpenMP schedulers.

翻译：由于全波形反演（FWI）处理海量数据，其执行需要大量时间和计算资源，因此局限于超级计算机等大规模计算机系统。FWI等技术能很好地适应并行计算，并可通过应用程序编程接口（API）OpenMP在共享内存系统中实现并行化。并行任务的管理可通过OpenMP中包含的循环调度器完成。其中动态调度器因能在运行时将预设固定大小的块分发给空闲处理核心而脱颖而出，它更能适应数据可能不规则的FWI场景。然而，块大小与运行时之间的关系尚不明确。优化技术可采用元启发式方法探索参数搜索空间，避免测试所有可能解决方案。本文提出一种策略：采用参数自动调优技术PATSMA（面向共享内存算法的参数自动调优），并以耦合模拟退火（CSA）作为其优化方法，自动调整波传播（FWI中最耗时的步骤之一）的动态调度块大小。由于在完整FWI中测试每个候选块大小不切实际，我们的方法是将FWI首次迭代的第一个地震道第一个时间步的运行时作为目标函数运行PATSMA，然后将得到的块大小应用于FWI涉及的所有波传播过程。我们通过在不同计算环境（如超级计算机和云计算实例）中改变问题规模进行测试，测量采用所提自动调优的FWI运行时。结果表明，与标准OpenMP调度器相比，在FWI中应用所提自动调优可减少高达70.46%的运行时。