应用于多核系统上的3D逆时迁移的动态调度的自动调整
Auto-tuning of dynamic scheduling applied to 3D reverse time migration on multicore systems
摘要：
逆时偏移（RTM）是一种广泛应用于石油和天然气工业的地震数据处理算法。这是一个计算密集型的任务，非常适合并行计算机。诸如RTM的方法可以通过将并行循环的迭代调度到线程而在共享存储器系统中并行化。然而，几个方面，如内存大小和层次结构，核心数量和输入大小，使最佳调度非常具有挑战性。在本文中，我们介绍了一种运行时策略，自动调整并行循环迭代的动态调度迭代应用程序，如RTM，在多核系统。所提出的方法旨在减少此类应用程序的执行时间。为了找到最佳的粒度，我们提出了一种基于耦合模拟退火（CSA）的自动调整策略，该策略可以调整OpenMP并行循环在3D RTM应用程序初始化过程中动态分配给工作线程的工作块大小。不同的计算系统和输入大小进行的实验表明，该方法始终优于默认的OpenMP编译器，静态，自动和引导，使应用程序的速度提高了33%。我们表明，这种性能的可能原因是减少高速缓存未命中，主要是L3级，和低开销，低于2%。已经证明对于3D RTM是鲁棒的和可扩展的，所提出的方法还可以提高类似的基于波的算法的性能，例如全波形反演（FWI）和其他迭代应用。
英文摘要：
Reverse time migration (RTM) is an algorithm widely used in the oil and gas industry to process seismic data. It is a computationally intensive task that suits well in parallel computers. Methods such as RTM can be parallelized in shared memory systems through scheduling iterations of parallel loops to threads. However, several aspects, such as memory size and hierarchy, number of cores, and input size, make optimal scheduling very challenging. In this paper, we introduce a run-time strategy to automatically tune the dynamic scheduling of parallel loops iterations in iterative applications, such as the RTM, in multicore systems. The proposed method aims to reduce the execution time of such applications. To find the optimal granularity, we propose a coupled simulated annealing (CSA) based auto-tuning strategy that adjusts the chunk size of work that OpenMP parallel loops assign dynamically to worker threads during the initialization of a 3D RTM application. Experiments performed with different computational systems and input sizes show that the proposed method is consistently better than the default OpenMP schedulers, static, auto, and guided, causing the application to be up to 33% faster. We show that the possible reason for this performance is the reduction of cache misses, mainly level L3, and low overhead, inferior to 2%. Having shown to be robust and scalable for the 3D RTM, the proposed method could also improve the performance of similar wave-based algorithms, such as full-waveform inversion (FWI) and other iterative applications.
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