| Paper |
Title |
Page |
| FPAT082 |
From Visualisation to Data Mining with Large Data Sets
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4114 |
| |
- A. Adelmann
PSI, Villigen
- R.D. Ryne, J.M. Shalf, C. Siegerist
LBNL, Berkeley, California
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In 3D particle simulations, the generated 6D phase space data are can be very large due to the need for accurate statistics, sufficient noise attenuation in the field solver and tracking of many turns in ring machines or accelerators. There is a need for distributed applications that allow users to peruse these extremely large remotely located datasets with the same ease as locally downloaded data. This paper will show concepts and a prototype tool to extract useful physical information out of 6D raw phase space data. ParViT allows the user to project 6D data into 3D space by selecting which dimensions will be represented spatially and which dimensions are represented as particle attributes, and the construction of complex transfer functions for representing the particle attributes. It also allows management of time-series data. An HDF5-based parallel-I/O library, with C++, C and Fortran bindings simplifies the interface with a variety of codes. A number of hooks in ParVit will allow it to connect with a parallel back-end that is able to provide remote file access, progressive streaming, and even parallel rendering of particle sets in excess of 1Billion particles.
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| FPAT083 |
H5Part: A Portable High Performance Parallel Data Interface for Particle Simulations
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4129 |
| |
- A. Adelmann
PSI, Villigen
- R.D. Ryne, J.M. Shalf, C. Siegerist
LBNL, Berkeley, California
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| |
Largest parallel particle simulations, in six dimensional phase space generate wast amont of data. It is also desirable to share data and data analysis tools such as ParViT (Particle Visualization Toolkit) among other groups who are working on particle-based accelerator simulations. We define a very simple file schema built on top of HDF5 (Hierarchical Data Format version 5) as well as an API that simplifies the reading/writing of the data to the HDF5 file format. HDF5 offers a self-describing machine-independent binary file format that supports scalable parallel I/O performance for MPI codes on a variety of supercomputing systems and works equally well on laptop computers. The API is available for C, C++, and Fortran codes. The file format will enable disparate research groups with very different simulation implementations to share data transparently and share data analysis tools. For instance, the common file format will enable groups that depend on completely different simulation implementations to share custom data analysis tools like ParViT without modification. We will show examples and benchmak data for various platforms.
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