The GPIES Data Cruncher: An Automated Data Processing System for the Gemini Planet Imager Exoplanet Survey Jason J. Wang, Pauline Arriaga, Marshall D. Perrin, Dmitry Savransky, James R. Graham, Christian Marois, Julien Rameau, Jean-Baptise Ruffio, and the GPI Team c Summary: • The Data Cruncher can automatically process all science and calibration data from the GPI Exoplanet Survey and more • Sensitivity curves and multiple PSF subtraction products are produced one hour after the data are available • The Super Data Cruncher can also run on a supercomputing cluster and reprocess the entire campaign in a few hours Acknowledgements: This research was supported in part by NASA NNX15AD95G, NASA NNX11AD21G, NSF AST-0909188, and the University of California LFRP-118057. The GPI project has been supported by Gemini Observatory, which is operated by AURA, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the NSF (USA), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), MCTI (Brazil) and MINCYT (Argentina). References: Marois, C., Correia, C., Galicher, R., et al. 2014, Proc SPIE, 9148. Perrin, M. D., Maire, J. , Ingraham, P., et al. 2014, Proc SPIE, 9147. Wang, J. J., Ruffio, J.-B., De Rosa, R. J., et al. 2015, Astrophysics Source Code Library, record ascl:1506.001. Crunchable Data GPI Exoplanet Survey Science • 1 hour H-band integral field spectroscopy planet search • 10 minute H-band snapshot broadband imaging polarimetry • 1 hour H-band deep broadband imaging polarimetry GPIES Follow-up • Multi-epoch deep follow-up observations in multiple bands GPI Queue Programs • All coronagraphic data taken for GPIES members’ queue programs Calibrations • All calibration data taken by GPI (which are publically available) Architecture Super Data Cruncher Reduced Data Products 0 0.5 1 1.5 2 2.5 3 0 5 10 15 20 25 30 Runtime (Hours) # of Datsets and Nodes Weak Scaling Summit Taken Dropbox Stored & Synced MySQL DB Logged Quality Checked • All data products produced within ~1 hour of the data being available • All data are synced to Dropbox for accessibility Datacubes PSF Subtracted Images Contrast Curves Calibrations pyKLIP: ADI+SDI pyKLIP: ADI pyKLIP: ADI+SDI w/ methane cADI • Runs on NERSC’s Edison supercomputer (5576 nodes, 133,824 cores, 357 TB RAM) • Uses MPI for inter-node communication • < 100 lines of code needed to implement the Super Data Cruncher • Reprocesses the entire campaign in a few hours Spectral Cube Polarimetry Cube Processing Backend Network Interface Web Socket or MPI Processing Controller High-level Python logic that controls dataflow through the various pipelines Uses queues to communicate between threads and monitors for synchronization GPI DRP (Perrin et al. 2014) TLOCI (Marois et al. 2014) pyKLIP (Wang et al. 2015) cADI (UdeM pipeline) Realtime Scanner Queues new datasets for processing and updates the GPIES Wiki Reprocessor Queries database to find and process existing datasets on demand Update Wiki • Written in Python with some pipeline components written in IDL • Highly modularized, multithreaded, and asynchronous New Files Save Reduced Data Products Send Commands Query for data Check for bad files Data Flow [email protected]