Infrastructures and services for remote sensing data production management across multiple satellite data centers

With the number of satellite sensors and date centers being increased continuously, it is becoming a trend to manage and process massive remote sensing data from multiple distributed sources. However, the combination of multiple satellite data centers for massive remote sensing (RS) data collaborative processing still faces many challenges. In order to reduce the huge amounts of data migration and improve the efficiency of multi-datacenter collaborative process, this paper presents the infrastructures and services of the data management as well as workflow management for massive remote sensing data production. A dynamic data scheduling strategy was employed to reduce the duplication of data request and data processing. And by combining the remote sensing spatial metadata repositories and Gfarm grid file system, the unified management of the raw data, intermediate products and final products were achieved in the co-processing. In addition, multi-level task order repositories and workflow templates were used to construct the production workflow automatically. With the help of specific heuristic scheduling rules, the production tasks were executed quickly. Ultimately, the Multi-datacenter Collaborative Process System (MDCPS) were implemented for large-scale remote sensing data production based on the effective management of data and workflow. As a consequence, the performance of MDCPS in experiments environment showed that those strategies could significantly enhance the efficiency of co-processing across multiple data centers

The Search for Life: Exoplanet Detection with Deep Learning

The discovery of new exoplanets, planets outside of our solar system, is essential for increasing our understanding of the universe. Exoplanets capable of harboring life are particularly of interest. Over 600 GB of data was collected by the Kepler Space Telescope, and about 30 GB is being collected each day by the Transiting Exoplanet Survey Satellite since its launch in 2018. Traditional methods of experts examining this data manually are no longer tractable; automation is necessary to accomplish the task of vetting all of this data to identify planet candidates from astrophysical false positives. Previous state-of-the-art models, Astronet and Exonet, use deep convolutional neural networks (CNNs) with over 8.8 million parameters. In this paper, I experiment with the application of recurrent networks, attentional models, and scaling down Astronet. I have developed a CNN model with 8x fewer trainable parameters than Astronet with the same accuracy and improved precision. I also provide a CNN-LSTM model with 59x fewer parameters just 1\% behind Astronet in accuracy that, with further tuning, may also be a competitive model for particularly resource-constrained uses. All code for this research is available on GitHub

Plasma Edge Kinetic-MHD Modeling in Tokamaks Using Kepler Workflow for Code Coupling, Data Management and Visualization

A new predictive computer simulation tool targeting the development of the H-mode pedestal at the plasma edge in tokamaks and the triggering and dynamics of edge localized modes (ELMs) is presented in this report. This tool brings together, in a coordinated and effective manner, several first-principles physics simulation codes, stability analysis packages, and data processing and visualization tools. A Kepler workflow is used in order to carry out an edge plasma simulation that loosely couples the kinetic code, XGC0, with an ideal MHD linear stability analysis code, ELITE, and an extended MHD initial value code such as M3D or NIMROD. XGC0 includes the neoclassical ion-electron-neutral dynamics needed to simulate pedestal growth near the separatrix. The Kepler workflow processes the XGC0 simulation results into simple images that can be selected and displayed via the Dashboard, a monitoring tool implemented in AJAX allowing the scientist to track computational resources, examine running and archived jobs, and view key physics data, all within a standard Web browser. The XGC0 simulation is monitored for the conditions needed to trigger an ELM crash by periodically assessing the edge plasma pressure and current density profiles using the ELITE code. If an ELM crash is triggered, the Kepler workflow launches the M3D code on a moderate-size Opteron cluster to simulate the nonlinear ELM crash and to compute the relaxation of plasma profiles after the crash. This process is monitored through periodic outputs of plasma fluid quantities that are automatically visualized with AVS/Express and may be displayed on the Dashboard. Finally, the Kepler workflow archives all data outputs and processed images using HPSS, as well as provenance information about the software and hardware used to create the simulation. The complete process of preparing, executing and monitoring a coupled-code simulation of the edge pressure pedestal buildup and the ELM cycle using the Kepler scientific workflow system is described in this paper

Connecting the time domain community with the Virtual Astronomical Observatory

The time domain has been identified as one of the most important areas of astronomical research for the next decade. The Virtual Observatory is in the vanguard with dedicated tools and services that enable and facilitate the discovery, dissemination and analysis of time domain data. These range in scope from rapid notifications of time-critical astronomical transients to annotating long-term variables with the latest modeling results. In this paper, we will review the prior art in these areas and focus on the capabilities that the VAO is bringing to bear in support of time domain science. In particular, we will focus on the issues involved with the heterogeneous collections of (ancillary) data associated with astronomical transients, and the time series characterization and classification tools required by the next generation of sky surveys, such as LSST and SKA.Comment: Submitted to Proceedings of SPIE Observatory Operations: Strategies, Processes and Systems IV, Amsterdam, 2012 July 2-

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International Parallel and Distributed Processing Symposium (IPDPS'18