Articolo in rivista, 2022, ENG, 10.3390/app12136798
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
heterogeneous applications, MATLAB, ArrayFire, GPUs, C++ maximum likelihood, bolometry
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
CNR institutes
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