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2 Fundamenta Informaticae

2 2015

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GPU-optimized Direct Fourier Method for On-line Tomography

Shkarin R., Ametova E., Chilingaryan S, Dritschler T., Kopmann A., Mirone A., Shkarin A., Vogelgesang M., Tsapko S.

Fundamenta Informaticae

2015

Vol. 141, nr 2/3

245--258

On-line monitoring of synchrotron 3D-imaging experiments requires very fast tomographic reconstruction. Direct Fourier methods (DFM) have the potential to be faster than standard Filtered Backprojection. We have evaluated multiple DFMs using various interpolation techniques. We compared reconstruction quality and studied the parallelization potential. A method using Direct Fourier Inversion (DFI) and a sinc-based interpolation was selected and parallelized for execution on GPUs. Several optimization steps were considered to boost the performance. Finally we evaluated the achieved performance for the latest generation of GPUs from NVIDIA and AMD. The results show that tomographic reconstruction with a throughput of more than 1.5 GB/sec on a single GPU is possible.

An Open Source GPU Accelerated Framework for Flexible Algebraic Reconstruction at Synchrotron Light Sources

Shkarin A., Ametova E., Chilingaryan S, Dritschler T., Kopmann A., Vogelgesang M., Shkarin R., Tsapko S.

Fundamenta Informaticae

2015

Vol. 141, nr 2/3

259--274

The recent developments in detector technology made possible 4D (3D + time) X-ray microtomographywith high spatial and time resolutions. The resolution and duration of such experiments is currently limited by destructive X-ray radiation. Algebraic reconstruction technique (ART) can incorporate a priori knowledge into a reconstruction model that will allow us to apply some approaches to reduce an imaging dose and keep a good enough reconstruction quality. However, these techniques are very computationally demanding. In this paper we present a framework for ART reconstruction based on OpenCL technology. Our approach treats an algebraic method as a composition of interacting blocks which performdifferent tasks, such as projection selection, minimization, projecting and regularization. These tasks are realised using multiple algorithms differing in performance, the quality of reconstruction, and the area of applicability. Our framework allows to freely combine algorithms to build the reconstruction chain. All algorithms are implemented with OpenCL and are able to run on a wide range of parallel hardware. As well the framework is easily scalable to clustered environment with MPI. We will describe the architecture of ART framework and evaluate the quality and performance on latest generation of GPU hardware from NVIDIA and AMD.