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This article is part of the supplement: Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)

Open Access Proceedings

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

Xiaohua Wan12, Fa Zhang1*, Qi Chu12 and Zhiyong Liu1*

Author Affiliations

1 Institute of Computing Technology and Key Lab of Intelligent Information Processing, Beijing, China

2 Graduate University, Chinese Academy of Sciences, Beijing, China

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BMC Bioinformatics 2012, 13(Suppl 10):S4  doi:10.1186/1471-2105-13-S10-S4

Published: 25 June 2012

Abstract

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.