Survey on software tools that implement deep learning algorithms on intel/x86 and IBM/Power8/Power9 platforms

Neural networks are becoming more and more popular in scientific field and in the industry. It is mostly because new solutions using neural networks show state-of-the-art results in the domains previously occupied by traditional methods, eg. computer vision, speech recognition etc. But to get these results neural networks become progressively more complex, thus needing a lot more training. The training of neural networks today can take weeks. This problems can be solved by parallelization of the neural networks training and using modern clusters and supercomputers, which can significantly reduce the learning time. Today, a faster training for data scientist is essential, because it allows to get the results faster to make the next decision. In this paper we provide an overview of distributed learning provided by the popular modern deep learning frameworks, both in terms of provided functionality and performance. We consider multiple hardware choices: training on multiple GPUs and multiple computing nodes. © The Authors 2019.Council on grants of the President of the Russian Federation: MK-2330.2019.9You can use a special version of Caffe, NVCaffe, which is supported by NVidia. This version was created specifically for the use of several GPUs. User instructions can be found in [35].For NVidia, MXNet is supported by Nvidia Cloud. MXNet also has support for CUDA and CuDNN.The results described in this paper were obtained with the financial support of the grant from the Russian Federation President Fund (MK-2330.2019.9)

Transit Ly-α\alpha signatures of terrestrial planets in the habitable zones of M dwarfs

We modeled the transit signatures in the Lya line of a putative Earth-sized planet orbiting in the HZ of the M dwarf GJ436. We estimated the transit depth in the Lya line for an exo-Earth with three types of atmospheres: a hydrogen-dominated atmosphere, a nitrogen-dominated atmosphere, and a nitrogen-dominated atmosphere with an amount of hydrogen equal to that of the Earth. We calculated the in-transit absorption they would produce in the Lya line. We applied it to the out-of-transit Lya observations of GJ 436 obtained by the HST and compared the calculated in-transit absorption with observational uncertainties to determine if it would be detectable. To validate the model, we also used our method to simulate the deep absorption signature observed during the transit of GJ 436b and showed that our model is capable of reproducing the observations. We used a DSMC code to model the planetary exospheres. The code includes several species and traces neutral particles and ions. At the lower boundary of the DSMC model we assumed an atmosphere density, temperature, and velocity obtained with a hydrodynamic model for the lower atmosphere. We showed that for a small rocky Earth-like planet orbiting in the HZ of GJ436 only the hydrogen-dominated atmosphere is marginally detectable with the STIS/HST. Neither a pure nitrogen atmosphere nor a nitrogen-dominated atmosphere with an Earth-like hydrogen concentration in the upper atmosphere are detectable. We also showed that the Lya observations of GJ436b can be reproduced reasonably well assuming a hydrogen-dominated atmosphere, both in the blue and red wings of the Lya line, which indicates that warm Neptune-like planets are a suitable target for Lya observations. Terrestrial planets can be observed in the Lya line if they orbit very nearby stars, or if several observational visits are available.Comment: 17 pages, 12 figures, accepted for publication in Astronomy & Astrophysic

Effect of stellar wind induced magnetic fields on planetary obstacles of non-magnetized hot Jupiters

We investigate the interaction between the magnetized stellar wind plasma and the partially ionized hydrodynamic hydrogen outflow from the escaping upper atmosphere of non- or weakly magnetized hot Jupiters. We use the well-studied hot Jupiter HD 209458b as an example for similar exoplanets, assuming a negligible intrinsic magnetic moment. For this planet, the stellar wind plasma interaction forms an obstacle in the planet's upper atmosphere, in which the position of the magnetopause is determined by the condition of pressure balance between the stellar wind and the expanded atmosphere, heated by the stellar extreme ultraviolet (EUV) radiation. We show that the neutral atmospheric atoms penetrate into the region dominated by the stellar wind, where they are ionized by photo-ionization and charge exchange, and then mixed with the stellar wind flow. Using a 3D magnetohydrodynamic (MHD) model, we show that an induced magnetic field forms in front of the planetary obstacle, which appears to be much stronger compared to those produced by the solar wind interaction with Venus and Mars. Depending on the stellar wind parameters, because of the induced magnetic field, the planetary obstacle can move up to ~0.5-1 planetary radii closer to the planet. Finally, we discuss how estimations of the intrinsic magnetic moment of hot Jupiters can be inferred by coupling hydrodynamic upper planetary atmosphere and MHD stellar wind interaction models together with UV observations. In particular, we find that HD 209458b should likely have an intrinsic magnetic moment of 10-20% that of Jupiter.Comment: 8 pages, 6 figures, 2 tables, accepted to MNRA