ADASS 2003 Conference Proceedings

I describe a new parallel-processing stellar photometry code called QLWFPC2 (http://www.noao.edu/staff/mighell/qlwfpc2) which is designed to do quick-look analysis of two entire WFPC2 observations from the Hubble Space Telescope in under 5 seconds using a fast Beowulf cluster with a Gigabit-Ethernet local network. This program is written in ANSI C and uses MPICH implementation of the Message Passing Interface from the Argonne National Laboratory for the parallel-processing communications, the CFITSIO library (from HEASARC at NASA's GSFC) for reading the standard FITS files from the HST Data Archive, and the Parameter Interface Library (from the INTEGRAL Science Data Center) for the IRAF parameter-file user interface. QLWFPC2 running on 4 processors takes about 2.4 seconds to analyze the WFPC2 archive datasets u37ga407r.c0.fits (F555W; 300 s) and u37ga401r.c0.fits (F814W; 300 s) of M54 (NGC 6715) which is the bright massive globular cluster near the center of the nearby Sagittarius dwarf spheroidal galaxy. The analysis of these HST observations of M54 lead to the serendipitous discovery of more than 50 new bright variable stars in the central region of M54. Most of the candidate variables stars are found on the PC1 images of the cluster center -- a region where no variables have been reported by previous ground-based studies of variables in M54. This discovery is an example of how QLWFPC2 can be used to quickly explore the time domain of observations in the HST Data Archive.

1. Motivation

Software tools which provide quick-look data analysis with moderate accuracy (3-6 percent relative precision) could prove to be very powerful data mining tools for researchers using the U.S. National Virtual Observatory (NVO).

The NVO data server may also find quick-look analysis tools to be very useful from a practical operational perspective. While quick-look stellar photometry codes are excellent tools to create metadata about the contents of CCD image data in the NVO archive, they also can provide the user with real-time analysis of NVO archival data.

It is significantly faster to transmit to the NVO user a quick-look color-magnitude diagram (consisting of a few kilobytes of graphical data) than it is to transmit the entire observational data set which may consist of 10, 100, or more megabytes of data. By judiciously expending a few CPU seconds at the NVO data server, an astronomer using the NVO might well be able to determine whether a given set of observations is likely to meet their scientific needs.

Quick-look analysis tools thus could provide a better user experience for NVO researchers while simultaneously allowing the NVO data servers to perform their role more efficiently with better allocation of scarce computational resources and communication bandwidth.

Successful quick-look analysis tools must be fast. Such tools must provide useful information in just a few seconds in order to be capable of improving the user experience with the NVO archive.

2. QDPHOT

3. QLWFPC2

This new stand-alone multi-processing WFPC2 stellar photometry task is called QLWFPC2 (Quick Look WFPC2) and is designed to analyze two complete WFPC2 observations of Local Group star clusters in less than 5 seconds on a 5-node Beowulf cluster of Linux-based PCs with a Gigabit-Ethernet local network. QLWFPC2 is written in ANSI C and uses the CFITSIO library (from HEASARC at NASA's Goddard Space Flight Center) to read FITS images from the HST Data Archive, and the Parameter Interface Library (PIL) (from the INTEGRAL Science Data Center) for the IRAF parameter-file user interface.

4. QLWFPC2 Performance

QLWFPC2 running on 4 processors takes about 2.4 seconds (see Figure 1) to analyze the WFPC2 archive data sets u37ga407r.c0.fits (filter: F555W; exposure: 300 s) and u37ga401r.c0.fits (filter: F814W; exposure: 300 s) of M54 which is the bright massive globular cluster near the center of the Sagittarius dwarf spheroidal galaxy. QLWFPC2 analyzed over 50,000 point source candidates and reported V, I, F555W and F814W photometry of 14,611 stars with signal-to-noise ratios of 8 or better.

The analysis of these HST observations of M54 lead to the serendipitous discovery of more than 50 new bright variable stars in the central region of M54 (Mighell & Schlaufman 2004). Most of the candidate variables stars are found on the PC1 images of the cluster center -- a region where no variables have been reported by previous ground-based studies of variables in M54. This discovery is an example of how QLWFPC2 can be used to quickly explore the time domain of observations in the HST Data Archive.

**Figure:** Typical QLWFPC2 performance results with two WFPC2 observations of a Local Group globular cluster running on a 5-node Beowulf cluster with 1.8 GHz CPUs and a Gigabit-Ethernet local network. The points show actual run times for between 1 and 5 processors; QLWFPC2 running on 4 processors takes about 2.4 seconds. The thin line shows a simple performance model based on measured cluster performance metrics (network bandwidth, disk drive bandwidth, and execution time of QLWFPC2 with a single CPU). The thick line shows the theoretical limit of performance. Note that the current version of the QLWFPC2 algorithm already meets the ideal performance values for 1, 2, and 4 processors. A single WFPC2 data set is about 10 Mbytes in size and is partitioned into four calibrated images from the PC1, WF2, WF3, and the WF4 cameras; the current QLWFPC2 analysis algorithm sends all of the image data from one WFPC2 camera to a single compute (slave) node for analysis -- the increase in computation time for 3 (5) processors compared to 2 (4) processors reflects the underlying 4-fold partitioning of a single WFPC2 data set. Spreading the analysis of data from a WFPC2 camera to all compute nodes would improve the computation time for 3 and 5 (and more) processors but would not improve the results for 1, 2 and 4 processors which are already optimal.
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QLWFPC2: Parallel-Processing Quick-Look WFPC2 Stellar Photometry Based on the Message Passing Interface

Abstract:

1. Motivation

2. QDPHOT

3. QLWFPC2

4. QLWFPC2 Performance

5. Recommendations

Acknowledgments

References