Mixed-Precision Tomographic Reconstructor Computations on Hardware Accelerators

Nicolas Doucet, Hatem Ltaief, Damien Gratadour, David E. Keyes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The computation of tomographic reconstructors (ToR) is at the core of a simulation framework to design the next generation of adaptive optics (AO) systems to be installed on future Extremely Large Telescopes (ELT). In fact, it is also a critical component for their operation on sky. The goals of these instruments range from the detection of the light from the most distant galaxies to the analysis of the composition of exoplanets terrestrial atmospheres. Based on advanced AO techniques, the instrument MOSAIC relies on a computational framework to filter out the Earth atmospheric turbulence and eventually enhance the images quality out of ground-based telescopes. The ToR calculation is a compute-bound operation based on the Cholesky factorization. Due to its cubical algorithmic complexity, the ToR may represent a major bottleneck for the E-ELT when scaling up the large number of wavefront sensors used in the baseline MOSAIC design. To mitigate this increasing dimensionality overhead, this paper presents the implementation of a novel mixed-precision Cholesky-based dense matrix solver on hardware accelerators. The new algorithm takes into account the data-sparse structure of the covariance matrix operator and uses the tensor cores of NVIDIA V100 GPUs to leverage performance at an unprecedented scale. To our knowledge, this is the first computational astronomy application that exploits V100's tensor cores outside of the traditional arena of artificial intelligence. Experimental results demonstrate the accuracy robustness and the high performance of the mixed-precision ToR on synthetic datasets, which paves the way for future instrument deployments on the E-ELT
Original languageEnglish (US)
Title of host publication2019 IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms (IA3)
PublisherIEEE
Pages31-38
Number of pages8
ISBN (Print)9781728159874
DOIs
StatePublished - Dec 31 2019

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