Enabling Interoperation of High Performance, Scientific Computing Applications: Modeling Scientific Data with the Sets and Fields (SAF) Modeling System

Miller, Mark; Reus, James F.; Matzke, Robb P.; Arrighi, William; Schoof, L.A.; Hitt, Ray T.; Espen, Peter K.

doi:10.1007/3-540-45718-6_18

Cited by 11 publications

(4 citation statements)

References 9 publications

(11 reference statements)

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“…Several libraries for scientific analysis have been proposed with the intention of increasing scientists’ ability to process and share data (Miller et al, 2001; Moran, 2001). These libraries provide high-level abstractions on spatial discretization and vector spaces, but do not have implementations on systems with CPU–GPU nodes.…”

Section: Related Workmentioning

confidence: 99%

Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures

Saltz

Teodoro

Pan

et al. 2013

The International Journal of High Performance Computing Applica

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Analysis of large sensor datasets for structural and functional features has applications in many domains, including weather and climate modeling, characterization of subsurface reservoirs, and biomedicine. The vast amount of data obtained from state-of-the-art sensors and the computational cost of analysis operations create a barrier to such analyses. In this paper, we describe middleware system support to take advantage of large clusters of hybrid CPU-GPU nodes to address the data and compute-intensive requirements of feature-based analyses in large spatio-temporal datasets.

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Section: Related Workmentioning

confidence: 99%

Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures

Saltz

Teodoro

Pan

et al. 2013

The International Journal of High Performance Computing Applica

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“…The FCDMF project addressed this issue by constructing the tool around the SAF [20] data format. SAF provides for the flexible representation of a wide variety of scientific data by incorporating metadata (or data about data) as part of the format.…”

Section: Data Formatmentioning

confidence: 99%

Pattern Discovery in Time-Ordered Data

Conrad¹,

Britanik²,

Deland³

et al. 2002

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This report describes the results of a Laboratory-Directed Research and Development project on techniques for pattern discovery in discrete event time series data. In this project, we explored two different aspects of the pattern matching/discovery problem. The first aspect studied was the use of Dynamic Time Warping for pattern matching in continuous data. In essence, DTW is a technique for aligning time series along the time axis to optimize the similarity measure. The second aspect studied was techniques for discovering patterns in discrete event data. We developed a pattern discovery tool based on adaptations of the A-priori and GSP (Generalized Sequential Pattern mining) algorithms. We then used the tool on three different application areas -unattended monitoring system data from a storage magazine, computer network intrusion detection, and analysis of robot training data.4

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“…Parallel 10 and scientific data management have played an important role since the beginning of large scale scientific computing, and are getting more important due to the increase of the scale of the computing. Existing products, which have partially addressed the issue, include HDF5 [1 ,2], SAF [3], CGNS [4,5,6], NetCDF [7,8], Silo [9] , UDM [10], and ~thers. Each of the existing products has certain advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Application I/O and Data Management

Dai

2011

2011 IEEE International Conference on Cluster Computing

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A library called HIO has been developed for largescale multi-physics simulations, based on the UDM library [10]. The goal of the library is to provide sustainable, interoperable, efficient, scalable, and convenient tools for parallel 10 and data management for high-level data structures in applications, and to provide tools for the connection between applications. The high-level data structures include one-and multi-dimensional arrays, structured meshes, unstructured meshes, and the meshes generated through adaptive mesh refinement. The 10 mechanism can be collective and non-collective. The data objects suitable for the library could be either large or small data sets. Even for small data sets, the 10 performance is close to one of MPI-IO performance.

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Enabling Interoperation of High Performance, Scientific Computing Applications: Modeling Scientific Data with the Sets and Fields (SAF) Modeling System

Cited by 11 publications

References 9 publications

Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures

Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures

Pattern Discovery in Time-Ordered Data

Application I/O and Data Management

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