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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Applications in GNSS water vapor tomography

Algebraic reconstruction algorithms are iterative algorithms that are used in many area including medicine, seismology or meteorology. These algorithms are known to be highly computational intensive. This may be especially troublesome for real-time applications or when processed by conventional low-cost personnel computers. One of these real time applications is the reconstruction of water vapor images from Global Navigation Satellite System (GNSS) observations. The parallelization of algebraic reconstruction algorithms has the potential to diminish signi cantly the required resources permitting to obtain valid solutions in time to be used for nowcasting and forecasting weather models. The main objective of this dissertation was to present and analyse diverse shared memory libraries and techniques in CPU and GPU for algebraic reconstruction algorithms. It was concluded that the parallelization compensates over sequential implementations. Overall the GPU implementations were found to be only slightly faster than the CPU implementations, depending on the size of the problem being studied. A secondary objective was to develop a software to perform the GNSS water vapor reconstruction using the implemented parallel algorithms. This software has been developed with success and diverse tests were made namely with synthetic and real data, the preliminary results shown to be satisfactory. This dissertation was written in the Space & Earth Geodetic Analysis Laboratory (SEGAL) and was carried out in the framework of the Structure of Moist convection in high-resolution GNSS observations and models (SMOG) (PTDC/CTE-ATM/119922/2010) project funded by FCT.Algoritmos de reconstrução algébrica são algoritmos iterativos que são usados em muitas áreas incluindo medicina, sismologia ou meteorologia. Estes algoritmos são conhecidos por serem bastante exigentes computacionalmente. Isto pode ser especialmente complicado para aplicações de tempo real ou quando processados por computadores pessoais de baixo custo. Uma destas aplicações de tempo real é a reconstrução de imagens de vapor de água a partir de observações de sistemas globais de navegação por satélite. A paralelização dos algoritmos de reconstrução algébrica permite que se reduza significativamente os requisitos computacionais permitindo obter soluções válidas para previsão meteorológica num curto espaço de tempo. O principal objectivo desta dissertação é apresentar e analisar diversas bibliotecas e técnicas multithreading para a reconstrução algébrica em CPU e GPU. Foi concluído que a paralelização compensa sobre a implementações sequenciais. De um modo geral as implementações GPU obtiveram resultados relativamente melhores que implementações em CPU, isto dependendo do tamanho do problema a ser estudado. Um objectivo secundário era desenvolver uma aplicação que realizasse a reconstrução de imagem de vapor de água através de sistemas globais de navegação por satélite de uma forma paralela. Este software tem sido desenvolvido com sucesso e diversos testes foram realizados com dados sintéticos e dados reais, os resultados preliminares foram satisfatórios. Esta dissertação foi escrita no Space & Earth Geodetic Analysis Laboratory (SEGAL) e foi realizada de acordo com o projecto Structure 01' Moist convection in high-resolution GNSS observations and models (SMOG) (PTDC / CTE-ATM/ 11992212010) financiado pelo FCT.Fundação para a Ciência e a Tecnologia (FCT

Sparse Matrix-Based HPC Tomography

Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. The solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide

Mapping Iterative Medical Imaging Algorithm on Cell Accelerator

Algebraic reconstruction techniques require about half the number of projections as that of Fourier backprojection methods, which makes these methods safer in terms of required radiation dose. Algebraic reconstruction technique (ART) and its variant OS-SART (ordered subset simultaneous ART) are techniques that provide faster convergence with comparatively good image quality. However, the prohibitively long processing time of these techniques prevents their adoption in commercial CT machines. Parallel computing is one solution to this problem. With the advent of heterogeneous multicore architectures that exploit data parallel applications, medical imaging algorithms such as OS-SART can be studied to produce increased performance. In this paper, we map OS-SART on cell broadband engine (Cell BE). We effectively use the architectural features of Cell BE to provide an efficient mapping. The Cell BE consists of one powerPC processor element (PPE) and eight SIMD coprocessors known as synergetic processor elements (SPEs). The limited memory storage on each of the SPEs makes the mapping challenging. Therefore, we present optimization techniques to efficiently map the algorithm on the Cell BE for improved performance over CPU version. We compare the performance of our proposed algorithm on Cell BE to that of Sun Fire ×4600, a shared memory machine. The Cell BE is five times faster than AMD Opteron dual-core processor. The speedup of the algorithm on Cell BE increases with the increase in the number of SPEs. We also experiment with various parameters, such as number of subsets, number of processing elements, and number of DMA transfers between main memory and local memory, that impact the performance of the algorithm

A distributed ASTRA toolbox

While iterative reconstruction algorithms for tomography have several advantages compared to standard backprojection methods, the adoption of such algorithms in large-scale imaging facilities is still limited,

A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems

Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction.Comment: Journal of Computational and Applied Mathematics (2014), 26 pages, 13 figures, 3 table

Fast tomographic reconstruction on multicore computers

Abstract Summary: Tomo3D implements a multithreaded vectorized approach to tomographic reconstruction that takes full advantage of the computer power in modern multicore computers. Full resolution tomograms are generated at high speed on standard computers with no special system requirements. Tomo3D has the most common reconstruction methods implemented, namely weighted Back-projection (WBP) and simultaneous iterative reconstruction technique (SIRT). It proves to be competitive with current graphic processor unit solutions in terms of processing time, in the order of a few seconds with WBP or minutes with SIRT. The program is compatible with standard packages, which easily allows integration in the electron tomography workflow. Availability: http://www.cnb.csic.es/%7ejjfernandez/tomo3d Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online