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

Xiaohua Wan,Qi Chu,Zhiyong Liu,Fa Zhang

doi:10.1186/1471-2105-13-s10-s4

Abstract

BackgroundThree-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.ResultsIn 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.ConclusionsExperimental 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.

Highlights

Three-dimensional (3D) reconstruction in electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens
We present a data structure named blob-ELLR and exploit several geometric related symmetry relationships to reduce the weighted matrix involved in iterative reconstruction methods
In order to evaluate the performance of the multilevel parallel strategy, the blob-based iterative reconstructions of the caveolae from the porcine aorta endothelial (PAE) cell have been performed [30]

Summary

Introduction

Three-dimensional (3D) reconstruction in electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. 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. Electron tomography (ET) combines electron microscopy (EM) and tomographic imaging to elucidate three-dimensional (3D) descriptions of complex biological structures at molecular resolution [1]. The idle sit of GPU is a waste of resources which has a negative impact on the performance of reconstructions on multi-GPUs. as data collection strategies and electron detectors improve, a sparse weight matrix involved in blob-based iterative reconstruction methods needs large memory storage. Due to the limited available memory, it is infeasible to store such a large sparse matrix for most GPUs. Computing the weight matrix on the fly is more efficient than storing the matrix in the previous GPUbased ET implementations [14]. It could bring the redundant computations since the weighted matrix has to be computed twice at least in each iteration

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Results

Conclusion