Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional HPC clusters is therefore in many cases an infeasible solution for migrating HPC applications to the cloud. As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven approach for running large-scale instances of this problem in the cloud. Instead of permanently running compute instances, our workflow is based on a serverless architecture with high throughput batch computing and event-driven computations, in which computational resources are only running as long as they are utilized. We demonstrate that this approach is very flexible and allows for resilient and nested levels of parallelization, including domain decomposition for solving the underlying partial differential equations. While the event-driven approach introduces some overhead as computational resources are repeatedly restarted, it inherently provides resilience to instance shut-downs and allows a significant reduction of cost by avoiding idle instances, thus making the cloud a viable alternative to on-premise clusters for large-scale seismic imaging.

中文翻译：

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云中无服务器地震成像的事件驱动方法


使云适应高性能计算 (HPC) 是一项具有挑战性的任务，因为 HPC 应用程序的软件取决于快速的网络连接，并且对硬件故障很敏感。因此，在许多情况下，使用云基础设施重新创建传统的 HPC 集群是将 HPC 应用程序迁移到云端的解决方案是不可行的。作为通用提升和转移方法的替代方法，我们考虑了地震成像的具体应用，并演示了一种无服务器和事件驱动的方法，用于在云中运行此问题的大规模实例。我们的工作流程不是永久运行的计算实例，而是基于具有高吞吐量批量计算和事件驱动计算的无服务器架构，其中计算资源仅在被利用时才运行。我们证明这种方法非常灵活，并且允许弹性和嵌套级别的并行化，包括用于求解基础偏微分方程的域分解。虽然事件驱动的方法在计算资源反复重新启动时引入了一些开销，但它本质上提供了对实例关闭的弹性，并通过避免空闲实例来显着降低成本，从而使云成为本地集群的可行替代方案大尺度地震成像。