TOTO: Transparent I/O Tuning for HPC Applications

High-performance computing applications rely on parallel file systems, where I/O performance is strongly affected by configuration parameters such as stripe count. However, the ideal stripe count is highly application- and system-dependent, making it difficult to predict and rarely tuned in practice. As a result, substantial I/O performance potential remains unexplored. We present TOTO, a transparent tool that improves I/O performance without requiring application modifications. TOTO intercepts POSIX calls, characterizes application behavior, and uses a machine learning model to select an appropriate stripe count, even for already opened files. We also introduce an allocation algorithm that balances performance and resource occupation, and describe a methodology for training the model once per system using limited data. Our results show that TOTO can improve I/O performance by up to 4.6 × compared to using a default stripe count, while imposing an overhead of at most \(8\%\). Moreover, compared to the state of the art, TOTO can optimize more applications with a lower resource occupation, which is expected to decrease contention in the I/O infrastructure.