Articolo in rivista, 2022, ENG, 10.3390/app12136798

Acceleration of an Algorithm Based on the Maximum Likelihood Bolometric Tomography for the Determination of Uncertainties in the Radiation Emission on JET Using Heterogeneous Platforms

Ruiz M.; Nieto J.; Costa V.; Craciunescu T.; Peluso E.; Vega J.; Murari A.

Instrumentation and Applied Acoustic Research Group, Universidad Politécnica de Madrid, Spain; National Institute for Laser, Plasma and Radiation Physics, Magurele, Bucharest, Romania; Department of Industrial Engineering, University of Rome Tor Vergata, Rome, Italy; Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain; Consorzio RFX (CNR, ENEA, INFN, Universita' di Padova, Acciaierie Venete SpA), Padova and CNR ISTP - Institute for Plasma Science and Technology, Section of Padova, Italy.

In recent years, a new tomographic inversion method based on the Maximum Likelihood (ML) approach has been adapted to JET bolometry. Apart from its accuracy and reliability, the key advantage is its ability to provide reliable estimates of the uncertainties in the reconstructions. The original algorithm was implemented and validated using the MATLAB software tool. This work presents the accelerated version of the algorithm implemented using a compatible ITER fast controller platform with the Ubuntu 18.04 or the ITER Codac Core System distributions (6.1.2). The algorithm has been implemented in C++ using the open-source libraries: ArrayFire, ALGLIB, and MATIO. These libraries simplify the management of specific hardware accelerators such as GPUs and increase performance. The speed-up factor obtained is approximately 10 times. The work presents the methodology followed, the results obtained, and the advantages and drawbacks of implementation.

Applied sciences 12 (13), pp. 1–14

Keywords

heterogeneous applications, MATLAB, ArrayFire, GPUs, C++ maximum likelihood, bolometry

CNR authors

Murari Andrea

CNR institutes

ISTP – Istituto per la Scienza e Tecnologia dei Plasmi

ID: 469319

Year: 2022

Type: Articolo in rivista

Creation: 2022-07-20 14:22:09.000

Last update: 2022-11-28 22:32:35.000

CNR authors

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:469319

DOI: 10.3390/app12136798

Scopus: 2-s2.0-85133842627

ISI Web of Science (WOS): 000823484800001