应用于全波形反演的OpenMP动态调度自动调优
Auto-Tuning for OpenMP Dynamic Scheduling applied to Full Waveform Inversion
AI总结：
针对全波形反演（FWI）中OpenMP动态调度块大小难确定的问题，提出基于PATSMA框架与CSA算法的自动调优策略，以局部迭代运行时间为目标函数调优块大小，最多可降低FWI运行时间70.46%。
摘要：
全波形反演（Full Waveform Inversion, FWI）是地震数据处理中广泛使用的方法，能够估计代表地下地质层特征的模型。由于该方法需处理海量数据，其执行需要大量时间和计算资源。FWI这类技术适配并行计算，可使用应用程序编程接口（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内置的循环调度器完成，其中动态调度器的特点是在运行时将预定义的固定大小块（chunk）分配给空闲的处理核心，能更好地适配数据处理可能存在不规则性的FWI场景。然而，块大小与运行时间之间的关系尚不明确。优化技术可采用元启发式算法探索参数搜索空间，避免测试所有可能的解。本文提出一种策略，使用共享内存算法参数自动调优框架（Parameter Auto-Tuning for Shared Memory Algorithms, PATSMA），以耦合模拟退火算法（Coupled Simulated Annealing, CSA）作为优化方法，自动调整FWI中计算成本最高的步骤之一——波传播的动态调度块大小。由于在完整FWI中测试每个候选块大小不切实际，我们的方法是运行PATSMA，其目标函数为第一次FWI迭代的第一个地震炮的第一次时间迭代的运行时间，随后将得到的块大小应用于FWI涉及的所有波传播过程。我们通过测试测量了采用所提自动调优方法的FWI运行时间，测试中改变了问题规模并在不同计算环境下运行。结果表明，与标准OpenMP调度器相比，在FWI中应用所提自动调优方法可将运行时间最多降低70.46%。
英文摘要：
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.
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