Performance and Best Practices to Run Finite Difference Kernel in the Cloud using Devito

As the energy industry transition to hydrocarbon alternatives for automotive and electricity production, fossil energy continues to be needed in those segments as well as solvent, plastic, solvent and consumer goods. The discovery of oil and gas-bearing formation is an increasing challenge as easily accessible resources has depleted. It requires to go deeper in the earth crust and image more complex geological topologies of the subsurface. Seismic imaging is key to understand the subsurface velocities and is one of the most demanding workloads for high performance computing. The need for high resolution image led to higher frequency processing and more complex wave equation. Compute and storage requirements have grown accordingly to accommodate those needs. Cloud computing is an attractive technology that provides the benefit of quickly access additional compute and storage capability for new algorithms development or production projects. In this paper, we present architecture best practices and performance recommendation for finite difference kernel method such as RTM and FWI. Devito is used to illustrate performance and runtime guidance on the latest AMD Milan and Intel Icelake instances. We will show performance using different compilers and flags as well as MPI, OpenMPI and hybrid on single and multi-instances.