Name
Abstract | ||
|---|---|---|
Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three V's (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities. Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/NYSDS.2017.8085039 | 2017 New York Scientific Data Summit (NYSDS) |
Keywords | Field | DocType |
streaming,high-performance,data analysis,experimental facility,Spark,MPI | SQL,Middleware,Pipeline transport,Spark (mathematics),Computer science,Real-time computing,Applied research,Analytics,Detector,Data management,Distributed computing | Journal |
Volume | ISBN | Citations |
abs/1805.04886 | 978-1-5386-3162-1 | 2 |
PageRank | References | Authors |
0.52 | 9 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Malitsky | 1 | 2 | 1.53 |
| aashish chaudhary | 2 | 3 | 1.20 |
| Sebastien Jourdain | 3 | 38 | 3.47 |
| Matt Cowan | 4 | 2 | 0.86 |
| Patrick O'Leary | 5 | 2 | 0.52 |
| Marcus D. Hanwell | 6 | 115 | 9.51 |
| Kerstin Kleese van Dam | 7 | 9 | 1.64 |