Supporting the scheduling over local HPC and cloud platforms: An FWI case study

Abstract

Full Waveform Inversion (FWI) is a mathematically and computationally challenging inverse problem of finding a quantitative rock-property description of the subsurface to match observed seismogram data. The inversion employs a forward model, which relates the subsurface to the observed seismograms. FWI and other seismic applications require High Performance Computing (HPC) to simulate the dynamics of such complex models. Not a long ago, companies, research institutes, and universities used to acquire clusters of computers to maintain on-premise. Recently, cloud computing has become an alternative posing a challenge to the end-users, who have to decide whether they should execute their applications: on their local clusters or burst them to a remote cloud provider. In this paper, we present a decision support method to choose the correct environment considering trade-offs, such as resource costs, performance, and availability on such heterogeneous execution platforms. We evaluated the system using our FWI application and preliminary results indicate that users of HPC applications can benefit from such a cloud advisory system to reduce costs, turnaround times, and even boost local platforms.