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Profile-guided optimization of critical medical imaging algorithms

David R. Kaeli, Byunghyun Jang, Perhaad Mistry, Dana Schaa
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}

}

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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.
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