Engineering topics

Nouanesengsy, Boonthanome

Publications and source records attributed to Nouanesengsy, Boonthanome.

4 records

DOE OSTI2018

Cinema Toolkit

Cinema is a workflow and toolkit for capturing, storing and exploring extreme scale scientific data. It is a highly interactive image-based approach to data analysis and exploration that promotes investigation of large scientific datasets, and is easily integrated into existing workflows through extensions to widely used open source tools. In short, many different images of the dataset are captured and stored in a database, along with metadata describing the images. This provides a method of exploration that is unlike other current methods. This novel approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage. The Cinema toolkit includes database specifications and software such as database writers, database viewers, algorithms and other support software that enable a wide variety of applications to write, operate on, and view Cinema databases.

ECP↗

DOE OSTI2016

2016 CSSE L3 Milestone: Deliver In Situ to XTD End Users

This report summarizes the activities in FY16 toward satisfying the CSSE 2016 L3 milestone to deliver in situ to XTD end users of EAP codes. The Milestone was accomplished with ongoing work to ensure the capability is maintained and developed. Two XTD end users used the in situ capability in Rage. A production ParaView capability was created in the HPC and Desktop environment. Two new capabilities were added to ParaView in support of an EAP in situ workflow. We also worked with various support groups at the lab to deploy a production ParaView in the LANL environment for both desktop and HPC systems. . In addition, for this milestone, we moved two VTK based filters from research objects into the production ParaView code to support a variety of standard visualization pipelines for our EAP codes.

"In Situ" production↗

DOE OSTI2013

LANL CSSE L2: Case Study of In Situ Data Analysis in ASC Integrated Codes [Slides]

The massive, complex datasets generated by scientific simulations on next-generation computer architectures at extreme scale pose new challenges for data management and scientific understanding. A critical element in meeting these new challenges is in situ data analysis where information is reduced and analyzed as close to the point of generation as possible, for example, at run time or as the data stream to longer term storage. This milestone will demonstrate in situ data analysis in xRAGE, an ASC Eulerian Radiation Hydrodynamics code. We will work with the Eulerian Applications Project to identify target problems that will exercise physics of interest to the program, and we will work closely with users to maximize the achieved usability and productivity improvements. We will investigate enhancements to the in situ process that enable and improve techniques of data reduction and data triage in addition to improving in situ performance by using portable acceleration technology. We will report on their viability and performance in xRAGE or another ASC code. These products will provide ASC IC developers with reference codes and tools that will benefit DSW by advancing the technology for predictive science at extreme scales.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

A model for optimizing file access patterns using spatio-temporal parallelism. In: UltraVis '13 Proceedings of the 8th International Workshop on Ultrascale Visualization, Article No. 4

For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.

Nouanesengsy, Boonthanome↗