Towards Real-Time Detection and Tracking of Blob-Filaments in Fusion Plasma Big Data

Magnetic fusion could provide an inexhaustible, clean, and safe solution to the global energy needs. The success of magnetically-confined fusion reactors demands steady-state plasma confinement which is challenged by the blob-filaments driven by the edge turbulence. Real-time analysis can be used to monitor the progress of fusion experiments and prevent catastrophic events. However, terabytes of data are generated over short time periods in fusion experiments. Timely access to and analyzing this amount of data demands properly responding to extreme scale computing and big data challenges. In this paper, we apply outlier detection techniques to effectively tackle the fusion blob detection problem on extremely large parallel machines. We present a real-time region outlier detection algorithm to efficiently find blobs in fusion experiments and simulations. In addition, we propose an efficient scheme to track the movement of region outliers over time. We have implemented our algorithms with hybrid MPI/OpenMP and demonstrated the accuracy and efficiency of the proposed blob detection and tracking methods with a set of data from the XGC1 fusion simulation code. Our tests illustrate that we can achieve linear time speedup and complete blob detection in two or three milliseconds using Edison, a Cray XC30 system at NERSC.