###### Abstract  
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. heterogeneous applications; MATLAB; ArrayFire; GPUs; C++ maximum likelihood; bolometry +
Footnote †: journal: journal: Applied Acoustic Research Group, Universidad Politecnica de Madrid, 28031 Madrid, Spain; [EMAIL_ADDRESS]  
Footnote †: journal: Applied Acoustic Research Group, Universidad Politecnica de Madrid, 28031 Madrid, Spain; julian.nieto.valhond@upm. problem is an ill-posed one due to the limited number of views. Various tomographic methods have been proposed. [PERSON] et al. [4] introduced a constrained optimization (ICO) method. ICO uses anisotropic smoothness on flux surfaces as an objective function and measurements as constraints. The reconstruction problem is discretized using a grid of pyramid local basis functions. ICO has been used in JET for more than two decades for various studies. It represents a landmark in what concerns the quality of reconstructions. The minimization of the Fisher information of the unknown 2D distribution has also been used to derive a bolometry reconstruction algorithm [5]. The Maximum likelihood (ML) bolometry implements a nonlinear iterative algorithm for estimating the emissivity distribution that is most consistent with the measured data, in the sense that, among the variety of all possible emissivity distributions, the algorithm chooses the one which maximizes the probability of obtaining the given experimental data set [6]. The ML algorithm also includes an iteratively smoothing procedure, based on the magnetic surfaces, either on closed or on open ones, given by the plasma equilibrium code used at JET. The ML method's key feature is its ability to routinely provide the confidence intervals. Comprehensive studies regarding the uncertainties in bolometric tomography on JET have been reported [7; 8]. The proper assessment of the uncertainties is an important aspect of investigating high radiative discharges on JET with the ITER Like Wall [9]. Based on deep neural networks (DNN), a different conceptual approach has been proposed [10; 11]. The network was trained using a database of reconstructions provided by the ICO method. Therefore, DNN generalizes the knowledge accumulated by using the ICO. The method can provide very fast reconstructions. Figure 1: Schematic view of JET bolometric diagnostic layout. In ASDEX, the routine radiated power computation relies on the code first developed in 1993 [12]. This code is based on a Tikhonov regularization scheme, which includes an anisotropic diffusion term that imposes smoothness on the flux surfaces and allows steeper gradients in the normal direction. Recently, the method has been improved by using a refined evaluation of the tomographic projection matrix which takes into account the full 3D field of view of each channel [13]. A new algorithm to improve the computation of total and local radiated power was recently developed [14]. A version of the constraint optimization approach, based on the error between the measured tomographic projections and backprojection and the smoothing function, is presented in this work [15].