학술논문
SciSpark: Highly interactive in-memory science data analytics
Document Type
Conference
Author
Source
2016 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2016 IEEE International Conference on. :2964-2973 Dec, 2016
Subject
Language
Abstract
We present further work on SciSpark, a Big Data framework that extends Apache Spark's inmemory parallel computing to scale scientific computations. SciSpark's current architecture and design includes: time and space partitioning of highresolution geo-grids from NetCDF3/4; a sciDataset class providing N-dimensional array operations in Scala/Java and CF-style variable attributes (an update of our prior sciTensor class); parallel computation of time-series statistical metrics; and an interactive front-end using science (code & visualization) Notebooks. We demonstrate how SciSpark achieves parallel ingest and time/space partitioning of Earth science satellite and model datasets. We illustrate the usability, extensibility, and early performance of SciSpark using several Earth science Use cases, here presenting benchmarks for sciDataset Readers and parallel time-series analytics. A three-hour SciSpark tutorial was taught at an ESIP Federation meeting using a dozen “live” Notebooks.