The main goal of this work is to analyze the behavior of a nighttime image processing module and find out basic estimates of required computational time and energy consumption for processing large data archives. As part of this work, we have performed the code refactoring of the most computing-intensive module in a system for detecting fishing boat lights. The algorithm is capable of detecting isolated bright spikes that are sharply visible on the sea surface at night. The refactored module has been optimized for effective usage of multi- and many-core Intel Xeon architectures. In the paper, we describe the algorithmic complexity for all computational stages of the module. Also, we have collected detailed statistic data for two data sets, different input parameter sets, and three test beds: Intel® Xeon® E5-2697A (codename Broadwell), Intel® Xeon® Gold 6148 (Skylake), and Intel® Xeon Phi® 7250 (KNL). Key correlations between module behavior and energy consumption are also included in the paper. The results of the study were used for calculations of the estimate time and energy requirements for a whole year archive of day/night band (DNB) images from the Visible Infrared Imaging Radiometer Suite (VIIRS). Moreover, driving factors, including price and legacy software systems, are presented for discussion.

This paper briefly examines the advantages and disadvantages of Elbrus architectures as building blocks for Seismic Processing cluster system. The configuration of a heterogeneous clustered system build for research Oil and Gas Company is examined in more detail. In this system, processing nodes with different architecture (x86, GPU and e2k) are integrated in a single computing cluster through a high performance global networking topologies. Heterogeneous cluster with Elbrus node provides a good opportunity for software cross-architectural migration. To demonstrate the potential of Elbrus nodes usage, the multispectral data analysis application has been optimized for e2k architecture. Paper includes performance results and scalability analysis for implemented module using e2k and x86 nodes. It is anticipated that the heterogeneous cluster with Elbrus node will form an integral part of the preparation process of the domestic supercomputer under development, based on the Elbrus processors. The basic software stack in Seismic Processing will be naturally emerged on the use of Elbrus node as part of the heterogeneous cluster.

wide range of modern system architectures and platforms targeted for different algorithms and application areas is now available. Even general-purpose systems have advantages in some computation areas and bottlenecks in another. Scientific applications on specific areas, on the other hand, have different requirements for CPU performance, scalability and power consumption. The best practice now is algorithm/architecture co-exploration approach, where scientific problem requirements influence the hardware configuration; on the other hand, algorithm implementation is re-factored and optimized in accordance with the platform architectural features. In this research, two typical modules used for multispectral nighttime satellite image processing are studied: measurement of local perceived sharpness in visible band using the Fourier transform; cross-correlation in a moving window between visible and infrared bands. Both modules are optimized and studied on wide range of up-to-date testbeds, based on different architectures. Our testbeds include computational nodes based on Intel Xeon E5-2697A v4, Intel Xeon Phi, Texas Instruments Sitara AM5728 dual-core ARM Cortex-A15, and NVIDIA JETSON TX2. The study includes performance testing and energy consumption measurements. The results achieved can be used for assessing serviceability for multispectral nighttime satellite image processing by two key parameters: execution time and energy consumption.

The problems which arise during the gas-oil exploration process and require high-performance computing resources can be divided in two groups. The first group is seismic data processing, the second group is 3D reservoir simulating for the exploration process optimization. For the each group of problems typical applications used in real technological process were chosen, and their behavior was examined on different computational architectures. Performed analysis shows that the applications of the first group have good scalability potential on the studied computational platforms, meanwhile for the applications of the second group the limit of the performance increasing is reached relatively fast.

This paper examines the prospects of the Elbrus computing platform for high-performance computations. The results of the most representative HPC benchmarks (HPCC, NPB, HPCG) and their analysis were presented. The testbed node was equipped with four MCST Elbrus-4C processors and DRAM DDR3 with total capacity 48 Gb. Different factors affecting the performance of FT and MG tests from NPB benchmark suite were analyzed by using Paraver tool, hardware performance counters (HPC) and MPI communications data. The scalability of geological application implementing the double-square-root (DSR) prestack migration method was investigated. Benchmark results show that the code customization to reveal platform-specific optimizations is required for the best performance. Nevertheless, the scalability analysis demonstrates that most tests are linearly scalable within a certain range of processor numbers.

This paper includes the energy consumption analysis of the testing mini-application that implements night time infrared remote sensing algorithm Nightfire. On this stage of our project computational nodes with Intel Xeon E5 and Intel Xeon Phi processors were tested. The correlation analysis between the number of used MPI ranks - OMP threads and total energy consumptions was performed for each of tested computational nodes. The optimized benchmarking parameters were used to compare energy efficiency of tested nodes. Moreover, the analysis of mini-application statements blocks was carried out for the following computation phases: I/O with HDF5 and ENVI data; the data processing using Nelder-Mead method. The impact of each computation phase to the total energy consumptions was determined so it gives new insights to possible ways of further optimization. Based on obtained results, the effectiveness of tested computational architectures for multispectral satellite images processing was evaluated.

In this research we have used computational-intensive software that implements 2D and 3D seismic migrations to study mini-application behavior for a set of the computational architectures. In addition to three architecture type comparative analyses, two CPU generation comparisons have been done. The dynamic behavior of chosen mini-applications was studied using BSC performance analysis tools to identify their common features. In summary, we observe the best performance of mini-applications on Intel Xeon E5-2698 CPU generation 4. Intel Xeon Phi 7250 peculiar architectural characteristics requires careful source code optimizations to help the compiler to effectively vectorize time-consuming loops and to improve the cache locality in order to achieve higher performance level. Elbrus-4S CPU is theoretically suitable for such kind of applications, but the currently observed performance is an order of magnitude smaller than on Xeon E5 family; we believe that the frequency and RAM bandwidth increase, as well as source code optimization work could improve its performance.