scholarly journals Parallel Tensor Compression for Large-Scale Scientific Data.

2015 ◽  
Author(s):  
Tamara G. Kolda ◽  
Grey Ballard ◽  
Woody Nathan Austin
Keyword(s):  
Large Scale ◽  
Scientific Data

scholarly journals A Dynamic Programmable Network for Large-Scale Scientific Data Transfer Using AmoebaNet

2019 ◽  
Vol 9 (21) ◽  
pp. 4541
Author(s):  
Syed Asif Raza Shah ◽  
Seo-Young Noh
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Transfer ◽  
Scientific Data ◽  
Network Service ◽  
Bulk Data ◽  
Experimental Facilities ◽  
Bulk Data Transfer ◽  
Programmable Network ◽  
Extreme Scale

Large scientific experimental facilities currently are generating a tremendous amount of data. In recent years, the significant growth of scientific data analysis has been observed across scientific research centers. Scientific experimental facilities are producing an unprecedented amount of data and facing new challenges to transfer the large data sets across multi continents. In particular, these days the data transfer is playing an important role in new scientific discoveries. The performance of distributed scientific environment is highly dependent on high-performance, adaptive, and robust network service infrastructures. To support large scale data transfer for extreme-scale distributed science, there is the need of high performance, scalable, end-to-end, and programmable networks that enable scientific applications to use the networks efficiently. We worked on the AmoebaNet solution to address the problems of a dynamic programmable network for bulk data transfer in extreme-scale distributed science environments. A major goal of the AmoebaNet project is to apply software-defined networking (SDN) technology to provide “Application-aware” network to facilitate bulk data transfer. We have prototyped AmoebaNet’s SDN-enabled network service that allows application to dynamically program the networks at run-time for bulk data transfers. In this paper, we evaluated AmoebaNet solution with real world test cases and shown that how it efficiently and dynamically can use the networks for bulk data transfer in large-scale scientific environments.

Parallel visualization of large-scale multifield scientific data

2019 ◽  
Vol 22 (6) ◽  
pp. 1107-1123
Author(s):  
Yi Cao ◽  
Zeyao Mo ◽  
Zhiwei Ai ◽  
Huawei Wang ◽  
Li Xiao ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Data ◽  
Parallel Visualization

Using Research Evidence to Inform Public Policy Decisions

2013 ◽  
Vol 51 (5) ◽  
pp. 412-422 ◽  
Author(s):  
Charles Moseley ◽  
Harold Kleinert ◽  
Kathleen Sheppard-Jones ◽  
Stephen Hall
Keyword(s):  
Public Policy ◽  
Large Scale ◽  
Research Evidence ◽  
State Level ◽  
Scientific Data ◽  
Policy Decisions ◽  
Data Set ◽  
Large Scale Data ◽  
Drive State ◽  
Public Policy Decisions

Abstract The application of scientific data in the development and implementation of sound public policy is a well-established practice, but there appears to be less consensus on the nature of the strategies that can and should be used to incorporate research data into policy decisions. This paper describes the promise and the challenges of using research evidence to inform public policy. Most specifically, we demonstrate how the application of a large-scale data set, the National Core Indicators (NCI), can be systematically used to drive state-level policy decisions, and we describe a case example of one state's application of NCI data to make significant changes to its Intellectual and Developmental Disabilities waiver. The need for continued research in this area is highlighted.

A Virtual Dataspaces Model for large-scale materials scientific data access

2016 ◽  
Vol 54 ◽  
pp. 456-468 ◽  
Author(s):  
Changjun Hu ◽  
Yang Li ◽  
Xin Cheng ◽  
Zhenyu Liu
Keyword(s):  
Large Scale ◽  
Data Access ◽  
Scientific Data
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