Abstract
HPC system users depend on profiling and analysis tools to obtain insights into the performance of their applications and tweak them. The complexity of modern HPC systems have necessitated advances in the associated HPC tools making them equally complex with various advanced features and complex user interfaces. While these interfaces are extensive and detailed, they require a steep learning curve even for expert users making them harder to use for novice users. While users are intuitively able to express what they are looking for in words or text (e.g., show me the process transmitting maximum data), they find it hard to quickly adapt to, navigate, and use the interface of advanced HPC tools to obtain desired insights. In this paper, we explore the challenges associated with designing a conversational (speech/text) interface for HPC tools. We use state-of-the-art AI models for speech and text and adapt it for use in the HPC arena by retraining them on a new HPC dataset we create. We demonstrate that our proposed model, retrained with an HPC specific dataset, can deliver higher accuracy than the existing state-of-the-art pre-trained language models. We also create an interface to convert speech/text data to commands for HPC tools and show how users can utilize the proposed interface to gain insights quicker leading to better productivity.
To the best of our knowledge, this is the first effort aimed at designing a conversational interface for HPC tools using state-of-the-art AI techniques to enhance the productivity of novice and advanced users alike.
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29 May 2022
In an older version of this paper, the name of the fourth author was missing. This has been corrected.
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Acknowledgement
This research is supported in part by NSF grants #1818253, #1854828, #1931537, #2007991, #2018627, #2112606, and XRAC grant #NCR-130002.
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Kousha, P. et al. (2022). “Hey CAI” - Conversational AI Enabled User Interface for HPC Tools. In: Varbanescu, AL., Bhatele, A., Luszczek, P., Marc, B. (eds) High Performance Computing. ISC High Performance 2022. Lecture Notes in Computer Science, vol 13289. Springer, Cham. https://doi.org/10.1007/978-3-031-07312-0_5
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