  Knowing HPC applications of jobs and analyzing their performance behavior
play important roles in system management and optimizations. The existing
approaches detect and identify HPC applications through machine learning
models. However, these approaches rely heavily on the manually extracted
features from resource utilization data to achieve high prediction accuracy. In
this study, we propose an innovative application recognition method, ARcode,
which encodes job monitoring data into images and leverages the automatic
feature learning capability of convolutional neural networks to detect and
identify applications. Our extensive evaluations based on the dataset collected
from a large-scale production HPC system show that ARcode outperforms the
state-of-the-art methodology by up to 18.87% in terms of accuracy at high
confidence thresholds. For some specific applications (BerkeleyGW and e3sm),
ARcode outperforms by over 20% at a confidence threshold of 0.8.
