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High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

Xiaohua Wan, Fa Zhang, Qi Chu, Zhiyong Liu
Institute of Computing Technology and Key Lab of Intelligent Information Processing, Beijing, China
BMC Bioinformatics, 13(Suppl 10):S4, 2012

@article{wan2012high,

   title={High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs},

   author={Wan, X. and Zhang, F. and Chu, Q. and Liu, Z.},

   journal={BMC Bioinformatics},

   volume={13},

   number={Suppl 10},

   pages={S4},

   year={2012},

   publisher={BioMed Central Ltd}

}

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BACKGROUND: Three-dimensional (3D) reconstruction in electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction in ET, but demand huge computational costs. Multiple graphic processing units (multi-GPUs) offer an affordable platform to meet these demands. However, a synchronous communication scheme between multi-GPUs leads to idle GPU time, and a weighted matrix involved in iterative methods cannot be loaded into GPUs especially for large images due to the limited available memory of GPUs. RESULTS: In this paper we propose a multilevel parallel strategy combined with an asynchronous communication scheme and a blob-ELLR data structure to efficiently perform blob-based iterative reconstructions on multi-GPUs. The asynchronous communication scheme is used to minimize the idle GPU time so as to asynchronously overlap communications with computations. The blob-ELLR data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELLR) data structure and yields significant acceleration. CONCLUSIONS: Experimental results indicate that the multilevel parallel scheme combined with the asynchronous communication scheme and the blob-ELLR data structure allows efficient implementations of 3D reconstruction in ET on multi-GPUs.
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