Works (10)

Updated: July 5th, 2023 15:43

2016 article

AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications

2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), pp. 116–125.

By: W. Zhang n, H. Tang n, S. Harenberg n, S. Byna*, X. Zou n, D. Devendran*, D. Martin*, K. Wu* ...

TL;DR: AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores, in the best case, the framework achieves a 46% performance improvement. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2016 article

In situ Storage Layout Optimization for AMR Spatio-temporal Read Accesses

PROCEEDINGS 45TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING - ICPP 2016, pp. 406–415.

By: H. Tang n, S. Byna*, S. Harenberg n, W. Zhang n, X. Zou n, D. Martin*, B. Dong*, D. Devendran* ...

TL;DR: This work develops an in situ data layout optimization framework that automatically selects from a set of candidate layouts based on a performance model, and reorganizes the data before writing to storage to enable efficient AMR read accesses. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2016 article

Usage Pattern-Driven Dynamic Data Layout Reorganization

2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), pp. 356–365.

By: H. Tang n, S. Byna*, S. Harenberg n, X. Zou n, W. Zhang n, K. Wu*, B. Dong*, O. Rubel* ...

TL;DR: This work proposes a framework that dynamically recognizes the data usage patterns, replicates the data of interest in multiple reorganized layouts that would benefit common read patterns, and makes runtime decisions on selecting a favorable layout for a given read pattern. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2015 article

Exploring Memory Hierarchy to Improve Scientific Data Read Performance

2015 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING - CLUSTER 2015, pp. 66–69.

author keywords: scientific data; read contention; memory hierarchy; SSD
TL;DR: This paper proposes a framework that exploits the memory hierarchy resource to address the read contention issues involved with SSDs and achieves up to 50% read performance improvement when tested on datasets from real-world scientific simulations. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2015 article

Parallel In Situ Detection of Connected Components in Adaptive Mesh Refinement Data

2015 15TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING, pp. 302–312.

By: X. Zou n, K. Wu*, D. Boyuka n, D. Martin*, S. Byna*, H. Tang n, K. Bansal n, T. Ligocki*, H. Johansen*, N. Samatova n

TL;DR: The first connected component detection methodology for structured AMR that is applicable in a parallel, in situ context is presented, incorporating an multi-phase AMR-aware communication pattern that synchronizes connectivity information across the AMR hierarchy. (via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2015 article

The Hyperdyadic Index and Generalized Indexing and Query with PIQUE

PROCEEDINGS OF THE 27TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT.

By: D. Boyuka n, H. Tang n, K. Bansal n, X. Zou n, S. Klasky* & N. Samatova n

TL;DR: PIQUE factors out commonalities in indexing, employing algorithmic/data structure "plugins" to mix orthogonal indexing concepts such as FastBit compressed bitmaps with ALACRITY binning, all within one framework. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2014 journal article

DIRAQ: scalable in situ data- and resource-aware indexing for optimized query performance

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 17(4), 1101–1119.

By: S. Lakshminarasimhan n, X. Zou n, D. Boyuka n, S. Pendse n, J. Jenkins n, V. Vishwanath*, M. Papka*, S. Klasky*, N. Samatova n

author keywords: Exascale computing; Indexing; Query processing; Compression
TL;DR: DIRAQ is proposed, a parallel in situ, in network data encoding and reorganization technique that enables the transformation of simulation output into a query-efficient form, with negligible runtime overhead to the simulation run. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2014 article

Transparent In Situ Data Transformations in ADIOS

2014 14TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), pp. 256–266.

By: D. Boyuka n, S. Lakshminarasimhan*, X. Zou n, Z. Gong n, J. Jenkins n, E. Schendel n, N. Podhorszki*, Q. Liu*, S. Klasky*, N. Samatova n

TL;DR: This work develops an in situ data transformation framework in the ADIOS I/O middleware with a "plug in" interface, thus greatly simplifying both the deployment and use of data transform services in scientific applications. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2013 article

PARLO: PArallel Run-time Layout Optimization for Scientific Data Explorations with Heterogeneous Access Patterns

PROCEEDINGS OF THE 2013 13TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID 2013), pp. 343–351.

Source: Web Of Science
Added: August 6, 2018

report

Parallel data layout optimization of scientific data through access-driven replication

Jenkins, J. P., Zou, X., Tang, H., Kimpe, D., Ross, R., & Samatova, N. F. In Technical Report- Not held in TRLN member libraries.

By: J. Jenkins, X. Zou, H. Tang, D. Kimpe, R. Ross & N. Samatova

Source: NC State University Libraries
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.