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The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task.

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In parallel with the R&D efforts in USA and Europe, China's National High-tech R&D program has setup its goal in developing petaflops computers. Researchers and engineers world-wide are looking for appropriate methods and technologies to achieve the petaflops computer system. Based on discussion on important design issues in developing the petaflops computer, this paper raises the major technological challenges including the memory wall, low power system design, interconnects, and programming support, etc. Current efforts in addressing some of these challenges and in pursuing possible solutions for developing the petaflops systems are presented. Several existing systems are briefly introduced as examples, including Roadrunner, Cray XT5 jaguar, Dawning 5000A/6000, and Lenovo DeepComp 7000. Architectures proposed by Chinese researchers for implementing the petaflops computer are also introduced. Advantages of the architecture as well as the difficulties in its implementation are discussed. Finally, future research direction in development of high productivity computing systems is discussed.