Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks

Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions for analyzing and processing such Big Data by distributing the computations among a number of compute nodes. In this work, performance of distributed computing environments on the basis of Hadoop and Spark frameworks is estimated for real and virtual versions of clusters. As a test task, we chose the classic use case of word counting in texts of various sizes. It was found that the running times grow very fast with the dataset size and faster than a power function even. As to the real and virtual versions of cluster implementations, this tendency is the similar for both Hadoop and Spark frameworks. Moreover, speedup values decrease significantly with the growth of dataset size, especially for virtual version of cluster configuration. The problem of growing data generated by IoT and multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye tracking, etc.) interaction channels is presented. In the context of this problem, the current observations as to the running times and speedup on Hadoop and Spark frameworks in real and virtual cluster configurations can be very useful for the proper scaling-up and efficient job management, especially for machine learning and Deep Learning applications, where Big Data are widely present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine

Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases

The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and inference phases is investigated. The numerous runs of the selected deep neural network (DNN) were performed on the standard MNIST and Fashion-MNIST datasets. The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads). The precise speedup values depend on the utilization level of TPUv2 units and increase with the increase of the data volume under processing, but for the datasets used in this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was observed for batch sizes >512 images for training phase and >40 000 images for prediction phase. It should be noted that these results were obtained without detriment to the prediction accuracy and loss that were equal for both GPU and TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd significant digit for Fashion-MNIST dataset.Comment: 10 pages, 7 figures, 2 table

High emission rate of sulfuric acid from Bezymianny volcano, Kamchatka

High concentrations of primary sulfuric acid (H2SO4) in fumarolic gases and high emission rate of sulfuric acid aerosol in the plume were measured at Bezymianny volcano, an active dome-growing andesitic volcano in central Kamchatka. Using direct sampling, filter pack sampling, and differential optical absorption spectroscopy measurements, we estimated an average emission of H2SO4 at 243 ± 75 t/d in addition to an average SO2 emission of 212 ± 65 t/d. The fumarolic gases of Bezymianny correspond to arc gases released by several magma bodies at different stages of degassing and contain 25-92% of entrained air. H2SO4 accounts for 6-87 mol% of the total sulfur content, 42.8 mol% on average, and SO2 is the rest. The high H2SO4 in Bezymianny fumaroles can be explained by catalytic oxidation of SO2 inside the volcanic dome. Because sulfate aerosol is impossible to measure remotely, the total sulfur content in a plume containing significant H2SO4 may be seriously underestimated

Geochemistry of geothermal gases

Available from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Polarization Investigations with Slow Neutrons

Available from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Seawater-rock interaction at Ushishir volcano-hydrothermal system, Kuril Islands

Ushishir volcano is located in the middle of the Kuril Arc. The Ushishir crater, a closed bay connected with the ocean by a narrow and shallow strait is characterized by a strong hydrothermal activity. Boiling springs, hot pools, fumaroles and shallow submarine vents are manifestations of a magmatic-seawater hydrothermal system with the discharging solution similar in chemical and isotopic composition to the seafloor hydrothermal fluids. The main features of the Ushishir fluids are: (1) water has close to zero δD and a large oxygen isotopic shift (6 7‰); (2) high boron concentration (~70 ppm); (3) a significant uptake of Ca and Sr from the rock and Ca/Sr higher than that for seawater with 87Sr/86Sr ~0.7037, a bit higher than the rock value (0.7032). The measured onshore discharge of boiling water is ~ 5 kg/s; however, a large plume of the discoloured seawater releasing from the outer submarine slope of the volcano indicates a much higher total mass and heat output

Heat and mass fluxes monitoring of El Chichón crater lake

El Chichón crater lake is characterized by important variations in volume (40,000 m3 to 230,000 m3) and in chemical composition alternating between acid-sulfate and acid-chloride-sulfate composition (Cl�/SO4 2� = 0�79 molar ratio). These variations in volume can occur very fast within less than a few weeks, and are not always directly correlated with the precipitation rate; the seepage rate of lake water is also an important parameter to consider in the lake mass balance. In this study, we present for the first time continuous physical data (temperature, depth, precipitation, wind velocity, solar radiation) of the crater lake registered by a meteorological station and two dataloggers. A heat and mass balance approach is proposed to estimate the heat and mass fluxes injected into the lake by the sublacustrine fumaroles and springs. Tracing the evolution of such fluxes can be helpful to understand this highly dynamic lake and offers an efficient way of monitoring the volcanic activity. During the observation period, the hydrothermal heat flux was estimated to be 17-22 MW, and the mass flux 10-12 kg/s (error on both values of ± 15%). These fluxes are mainly counterbalanced by the loss of heat and mass by evaporation, respectively of 20-24 MW and 8-10 kg/s. Furthermore, the seepage rate of the lake waters was estimated and shown to be a highly variable parameter (12-42 kg/s), depending on the lake surface. This new data set constitutes a baseline to monitor the future activity of El Chichón volcano. In case of volcanic activity renewal, one of the first precursor signals would probably be the full evaporation of the lake.El lago cratérico del volcán El Chichón se caracteriza por variaciones importantes en volumen (de 40,000 m3 hasta 230,000 m3), así como en su composición química, alternando composiciones de tipo ácido-sulfato y ácido-sulfato-cloruro (Cl�/SO4 2� molar = 0�79). Estas variaciones en volumen pueden ocurrir en un intervalo de tiempo corto, de menos de unas semanas, y no siempre se correlacionan con la cantidad de precipitación, debido al efecto de la tasa de infiltración de las aguas del lago. En este estudio, se presentan por primera vez datos físicos (temperatura, profundidad, precipitación, velocidad de viento, radiación solar) del lago registrados continuamente por una estación meteorológica y dos sondas. Además, con un modelo de balance de calor y masa se propone una estimación de los flujos de calor y masa inyectados en el lago por las fumarolas sublacustrinas y manantiales. El estudio de tales flujos permite entender mejor la dinámica del lago y podrá ofrecer una manera eficiente de monitorear la actividad del volcán. Durante el periodo de observación, los flujos de calor y de masa provenientes del sistema hidrotermal fueron estimados entre 17-22 MW y entre 10-12 kg/s (error para los dos valores de ± 15%), respectivamente. Estos flujos son balanceados por la pérdida de calor y masa debido a la evaporación, respectivamente de 20-24 MW y 8-10 kg/s. También se estimó la tasa de infiltración y se demostró que puede variar consideradamente (12-42 kg/s), y esto depende de la superficie del lago. Los datos presentados en este estudio constituyen una base importante para monitorear la actividad futura del volcán El Chichón. En caso de renovación de la actividad volcánica, una de las señales precursoras sería probablemente la evaporación completa del lago