  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.
