Paper Info
Title
A Lightweight I/O Scheme To Facilitate Spatial And Temporal Queries Of Scientific Data Analytics
Abstract
In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR ( Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.
Year
DOI
Venue
2013
10.1109/MSST.2013.6558441
2013 IEEE 29TH SYMPOSIUM ON MASS STORAGE SYSTEMS AND TECHNOLOGIES (MSST)
Keywords
Field
DocType
data analysis,spatial dimensions,aggregate,turnaround time,spatial relationships,computer programming,jaguar supercomputer,data structures,meteorology,time dimension,data models,software engineering,spatial query,star,computational modeling,data elements,petascale computing,data management,organizations,layout
Data science,Data modeling,Data structure,Small data,Data analysis,Computer science,Input/output,Analytics,Petascale computing,Data management
Conference
ISSN
Citations 
PageRank 
2160-195X
6
0.46
References 
Authors
19
10
Name
Order
Citations
PageRank
Yuan Tian115815.89
Zhuo Liu211816.03
Scott Klasky3154799.00
Bin Wang41208.13
Hasan Abbasi566032.61
Shujia Zhou621617.50
Norbert Podhorszki7104683.84
Tom Clune8201.79
Jeremy Logan915416.72
Weikuan Yu10104277.40