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http://dx.doi.org/10.5573/IEIESPC.2016.5.4.294

CPU-GPU2 Trigeneous Computing for Iterative Reconstruction in Computed Tomography  

Oh, Chanyoung (School of Electrical and Computer Engineering, University of Seoul)
Yi, Youngmin (School of Electrical and Computer Engineering, University of Seoul)
Publication Information
IEIE Transactions on Smart Processing and Computing / v.5, no.4, 2016 , pp. 294-301 More about this Journal
Abstract
In this paper, we present methods to efficiently parallelize iterative 3D image reconstruction by exploiting trigeneous devices (three different types of device) at the same time: a CPU, an integrated GPU, and a discrete GPU. We first present a technique that exploits single instruction multiple data (SIMD) architectures in GPUs. Then, we propose a performance estimation model, based on which we can easily find the optimal data partitioning on trigeneous devices. We found that the performance significantly varies by up to 6.23 times, depending on how SIMD units in GPUs are accessed. Then, by using trigeneous devices and the proposed estimation models, we achieve optimal partitioning and throughput, which corresponds to a 9.4% further improvement, compared to discrete GPU-only execution.
Keywords
Heterogeneous computing; Data partitioning; Image reconstruction; Computed tomography;
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