# Enhancing speed and scalability of the ParFlow simulation code

## TL;DR

This paper improves the ParFlow simulation code's scalability on petascale supercomputers by reorganizing its mesh parallelization using p4est, enabling efficient large-scale subsurface flow simulations.

## Contribution

The authors enhance ParFlow's parallel mesh handling with p4est, significantly boosting its scalability on supercomputers up to 458,000 cores.

## Key findings

- Achieved good weak and strong scaling on Juqueen supercomputer
- Demonstrated efficient large-scale subsurface flow simulation
- Identified and addressed mesh parallelization bottleneck

## Abstract

Regional hydrology studies are often supported by high resolution simulations of subsurface flow that require expensive and extensive computations. Efficient usage of the latest high performance parallel computing systems becomes a necessity. The simulation software ParFlow has been demonstrated to meet this requirement and shown to have excellent solver scalability for up to 16,384 processes. In the present work we show that the code requires further enhancements in order to fully take advantage of current petascale machines. We identify ParFlow's way of parallelization of the computational mesh as a central bottleneck. We propose to reorganize this subsystem using fast mesh partition algorithms provided by the parallel adaptive mesh refinement library p4est. We realize this in a minimally invasive manner by modifying selected parts of the code to reinterpret the existing mesh data structures. We evaluate the scaling performance of the modified version of ParFlow, demonstrating good weak and strong scaling up to 458k cores of the Juqueen supercomputer, and test an example application at large scale.