With the increasing prevalence of Parallel File Systems (PFSes) in the context of vast and complex server networks, the importance of accurate anomaly detection on runtime logs of parallel file systems is increasing. But as it currently stands, many state-of-the-art methods for log-based anomaly detection, such as DeepLog, have encountered numerous challenges when applied to parallel file system logs due to their irregularity and a lack of identifying characteristics. Although a previous work, SentiLog has shown promising results, the sentiment based model lacks analysis of temporal dependencies within a log sequence, and hence misses important sequence-based anomalies. To circumvent these problems, this study proposes ModuleLog, a log anomaly detection solution which analyzes the temporal sequence of logging modules to detect irregularity. The key distinction from existing sequence-based anomaly detection solutions is the attempt to reduce the granularity of using individual log keys by grouping these keys by the module they reside in, based on the PFS source code. We apply an RNN architecture with regular LSTM cells to the sequence of modules. This method allows ModuleLog to be able to detect transition points between normal and abnormal logs in a given sequence, as well as detect sequences of abnormal logs. Presented at the 2022 UNC Charlotte Undergraduate Research Conference.