As the scale of high-performance computing systems (HPC) continues to expand, the frequency of failures within the system also increases. The follow-up steps in the event of a failure are necessary because efficient prevention measures are absent. These actions, however, result in decreased operating efficiency as the system normalizes. To solve this problem, recent research has utilized deep learning to predict failures in advance by examining log messages. One common method uses log ID extracted from log messages as input data. However, these methods could lead to information loss in log messages and may not accurately capture log message properties. In this study, we propose the prior failure detection in large-scale systems using language models (FALL) that reflects the properties of log messages. The FALL uses log messages as text data and only uses normal data for training. The FALL makes use of sharpening, which concentrates on the minimal lexical change between normal and abnormal log messages. In addition, by leveraging the characteristics of log messages with limited vocabularies, the FALL utilizes only some tokens for anomaly detection. The experiments' results from log data from real industry-based HPC systems show that the FALL achieves superior performance in early failure detection.

Transient execution attacks are critical security threats since those attacks exploit speculative execution which is an essential architectural solution that can improve the performance of out-of-order processors significantly. Such attacks change cache state by accessing secret data during speculative executions, then the attackers leak the secret information exploiting cache timing side-channels. Even though software patches against transient execution attacks have been proposed, the software solutions significantly slow down the performance of a system. In this paper, we propose CacheRewinder, an efficient hardware-based defense mechanism against transient execution attacks. CacheRewinder prevents leakage of secret information by revoking the cache updates done by speculative executions. To restore the cache state efficiently, CacheRewinder exploits the underutilized write-back buffer space as the temporary storage for victimized cache blocks evicted during speculative executions. Hence, when speculation fails CacheRewinder can quickly restore the cache state using the victim blocks held in the write-back buffer. Our evaluation exhibits CacheRewinder can effectively defend against transient execution attacks. The performance overhead by CacheRewinder is only 0.6%, which is negligible compared to the unprotected baseline processor. CacheRewinder also requires minimal storage cost since it exploits unused write-back buffer entries as storage for evicted cache blocks.