Using a high-level parallel programming language for GPU-accelerated tomographic reconstruction

This paper aims to determine the usefulness of using a high-level parallel programming language for implementing parallel high-performance tomographic reconstruction algorithms. The purpose of this is to make it easier for researchers to implement advanced model-based iterative reconstruction algori...

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Bibliographic Details
Published in:2019 International Conference on High Performance Computing & Simulation (HPCS) pp. 27 - 32
Main Authors: Lindhoj, Mette Bjerg, Henriksen, Troels, Pedersen, Larke, Sporring, Jon
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2019
Subjects:
algebraic reconstruction
back-projection
forward-projection
Futhark
GPU
Graphics processing units
Hardware
high-level language
Image reconstruction
Mathematical model
OpenCL
parellelization
Reconstruction algorithms
SIRT
tomographic reconstruction
X-ray imaging
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Summary:This paper aims to determine the usefulness of using a high-level parallel programming language for implementing parallel high-performance tomographic reconstruction algorithms. The purpose of this is to make it easier for researchers to implement advanced model-based iterative reconstruction algorithms for use at synchrotron facilities, while still taking advantage of hardware such as GPUs. To this end, we implement the forward- and back-projection in the programming language Futhark, and verify their applicability through an implementation of an algebraic reconstruction algorithm. We obtain promising performance results by use of algorithmic considerations instead of low-level optimizations. Finally, we demonstrate that the implementation makes it possible to prototype implementations of iterative reconstruction algorithms on a standard laptop while still obtaining good scaling towards highend GPUs.
DOI:10.1109/HPCS48598.2019.9188217