High Performance Multivariate Visual Data Exploration for Extremely Large Data
One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Accelerator& Fusion Research Division; Computational Research Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 941717
- Report Number(s):
- LBNL-716E; TRN: US0807486
- Resource Relation:
- Conference: SuperComputing 2008, Austin, Texas, USA, Nov.15-21 2008
- Country of Publication:
- United States
- Language:
- English
Similar Records
Query-driven visualization of large data sets
Application of High-performance Visual Analysis Methods to Laser Wakefield Particle Acceleration Data