Profile-guided optimization of critical medical imaging algorithms
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA
Biomedical Imaging: From Nano to Macro, 2009. ISBI ’09. IEEE International Symposium on In Biomedical Imaging: From Nano to Macro, 2009. ISBI ’09. IEEE International Symposium on (2009), pp. 1293-1293
@conference{kaeli2009profile,
title={Profile-guided optimization of critical medical imaging algorithms},
author={Kaeli, D.R. and Jang, B. and Mistry, P. and Schaa, D.},
booktitle={Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on},
pages={1293},
issn={1945-7928},
year={2009},
organization={IEEE}
}
Given the rapid growth in computational requirements for medical image analysis, Graphics Processing Units (GPUs) have begun to be utilized to address these demands. But even though GPUs are well-suited to the underlying processing associated with medical image reconstruction, extracting the full benefits of moving to GPU platforms requires significant programming effort, and presents a fundamental barrier for more general adoption of GPU acceleration in a wider range of medical imaging applications. In this paper we describe our experience in accelerating a number of challenging medical imaging applications, and discuss how we utilize profile-guided analysis to reap the full benefits available on GPU platforms. Our work considers different GPU architectures, as well as how to fully exploit the benefits of using multiple GPUs.
November 26, 2010 by hgpu
