Optimization of a Radiofrequency Ablation FEM Application Using Parallel Sparse Solvers

TL;DR

This paper enhances the computational efficiency of a finite element method application for radiofrequency ablation by integrating parallel sparse solvers, significantly reducing execution time while maintaining result accuracy.

Contribution

It introduces the use of parallel sparse solvers on multicore and GPU architectures to accelerate a medical simulation application, achieving up to 40 times faster performance.

Findings

Execution time reduced by up to 40 times.

Solution quality maintained with similar numerical accuracy.

Effective use of multiple sparse solver packages across architectures.

Abstract

Finite element method applications are a common approach to simulate a handful of phenomena but can take a lot of computing power, causing elevated waiting time to produce precise results. The radiofrequency ablation finite element method is an application to simulate the medical procedure of radiofrequency ablation, a minimally invasive liver cancer treatment. The application runs sequentially and can take up to 20 hours of execution to generate 15 minutes of simulation results. Most of this time arises from the need to solve a sparse system of linear equations. In this work, we accelerate this application by using three sparse solvers packages (MAGMA cuSOLVER, and QRMumps), including direct and iterative methods over different multicore and GPU architectures. We conducted a numerical result analysis to access the solution quality provided by the distinct solvers and their configurations, proposing the use of the peak signal-to-noise ratio metric. We were able to reduce the application execution time up to 40 times compared to the original sequential version while keeping a similar numerical quality for the results.