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Streaming Data Reorganization at Scale with DeltaFS Indexed Massive Directories
We report complex storage stacks providing data compression, indexing, and analytics help leverage the massive amounts of data generated today to derive insights. It is challenging to perform this computation, however, while fully utilizing the underlying storage media. This is because, while storage servers with large core counts are widely available, single-core performance and memory bandwidth per core grow slower than the core count per die. Computational storage offers a promising solution to this problem by utilizing dedicated compute resources along the storage processing path. We present DeltaFS Indexed Massive Directories (IMDs), a new approach to computational storage. DeltaFS IMDs harvest available (i.e., not dedicated) compute, memory, and network resources on the compute nodes of an application to perform computation on data. We demonstrate the efficiency of DeltaFS IMDs by using them to dynamically reorganize the output of a real-world simulation application across 131,072 CPU cores. DeltaFS IMDs speed up reads by 1,740x while only slightly slowing down the writing of data during simulation I/O for in situ data processing.
The White-Box Adversarial Data Stream Model
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Real Time Streaming Data visualization tooling (RTS Data Vis App)
NREL's RTS Data Vis App contains the following sub-projects: (1) Conditioner: A tool to resample high-rate data from the relay for low-rate visualization and (2) Chart: A tool to plot the data from the relay, including a specialized copy of the Conditioner. The Conditioner is a command-line tool for reading ADMS data from ZMQ, resampling, and exporting over a websocket. The Chart is an example of a charting tool for ADMS applications, using a ZMQ message bus.
An Empirical Evaluation of Denoising Techniques for Streaming Data
Abstract not provided
Monitoring DUNE Data Streaming Efficiency
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Architecture of a Multi-Channel Data Streaming Device with an FPGA as a Coprocessor
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Generalized Canonical Polyadic Tensor Decompositions for Streaming Data.
Abstract not provided.
Data Stream Processing & Integrated Research Infrastructure Across Facilities
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Generalized Canonical Polyadic Tensor Decompositions for Streaming Data
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Hybrid Parallel Tucker Decomposition of Streaming Data
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Computing Sobol main effects indices with unstructured samples for discrete random variables and streaming data
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Geo-metabolomics of a saltmarsh: Combining in situ, bulk, genomic, transcriptomic, and DOMeomic data streams
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KAZRARSCL-CLOUDSAT Value Added Product Data Stream
The KAZRARSCL-CLOUDSAT Value-Added Product (VAP) is based on the KAZR-ARSCL VAP, which provides cloud boundaries and best-estimate time-height fields of radar moments. The KAZRARSCL-CLOUDSAT VAP applies a statistically-derived calibration offset to reflectivity fields in order to align them with observations from the spaceborne CloudSat Cloud Profiling Radar. For details on the offset values applied, please refer to "Kollias, P., Puigdomènech Treserras, B., and Protat, A.: Calibration of the 2007–2017 record of ARM Cloud Radar Observations using CloudSat, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-34, 2019."
Results of an aerodynamic investigation of a space shuttle orbiter/747 carrier vehicle configuration to establish a free-stream data base for ALT separation investigations, utilizing a 0.0125-scale model (48-0/AX1318I-1) in the ARC 14-foot wind tunnel (CA23A)
Force and moment data are presented which were obtained for each vehicle separately at a Mach number of 0.6, and for the mated orbiter/747 configuration at Mach numbers of 0.3, 0.5, 0.6, and 0.7. Orbiter angles of attack from 0 degrees to +12 degrees and 747/Carrier angles of attack from -3 degrees to +7 degrees were investigated at angles of sideslip of 0 degrees and -5 degrees. Model variables include orbiter elevon and rudder deflections, orbiter tail cone-on and off, various orbiter/747 attach structure configurations, 747 stabilizer and rudder deflections, and 747 CAM modification components-on and off. Photographs of test configurations are included.
Approaching 80 years of snow water equivalent information by merging different data streams
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