Comparison of the capabilities of GPU clusters and general-purpose supercomputers for solving 3D inverse problems of ultrasound tomography

Abstract

This study focuses on developing the algorithms for solving 3D inverse problems of ultrasound tomography using GPU clusters and general-purpose supercomputers. The computing capabilities of these supercomputer architectures in application to wave tomography are compared via numerical simulations.Parallel gradient-based iterative algorithms designed to solve coefficient inverse problems for the wave equation are proposed. We show that layer-by-layer wave tomography can be efficiently performed using both CPU- and GPU-based supercomputers. Solving 3D coefficient inverse problems is much more computationally expensive. A general-purpose (CPU-based) system capable of reconstructing 3D images should be equipped with either a sufficient number of memory access channels or a large number of computing cores with large cache memory and high-speed communication network. Such a system would be very large and expensive. Therefore, the most promising solution is to develop dedicated GPU-based supercomputers.From the applications perspective, this study focuses on problems of developing ultrasound tomography devices for breast cancer diagnosis. Computer simulations show that a GPU cluster capable of performing 3D image reconstruction within reasonable time fits in a single rack and can be incorporated into medical ultrasound tomography facilities.