Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma

TL;DR

This paper introduces a real-time algorithm for detecting and tracking blob-filaments in fusion plasma data, leveraging parallel computing to achieve millisecond detection times, with potential applications in other fields.

Contribution

The work presents a novel, parallelized method for real-time detection and tracking of spatio-temporal features, specifically blob-filaments, in large-scale fusion data.

Findings

Achieved linear speedup on 1024 processes.

Completed blob detection in less than three milliseconds.

Demonstrated effectiveness on 30GB fusion simulation data.

Abstract

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.