Mimir: Extending I/O Interfaces to Express User Intent for Complex Workloads in HPC
Hariharan Devarajan, Kathryn Mohror · 2023
The complexity of data management in HPC systems stems from the diversity in I/O behavior exhibited by new workloads, multistage workflows, and the presence of multitiered storage systems. This complexity is managed by the storage systems, which provide user-level configurations to allow the tuning of workload I/O within the system. However, these configurations are difficult to set by users who lack expertise in I/O subsystems. We propose a paradigm change in which users specify the intent of I/O operations and storage systems automatically set various configurations based on the supplied intent. To this end, we developed the Mimir infrastructure to assist users in passing I/O intent to the underlying storage system. We demonstrate several use cases that map user-defined intents to storage configurations that lead to optimized I/O. In this study, we make three observations. First, I/O intents should be applied to each level of the I/O storage stack, from HDF5 to MPI-IO to POSIX, and integrated using lightweight adaptors in the existing stack. Second, the Mimir infrastructure supports up to 400M Ops/sec throughput of intents in the system, with a low memory overhead of 6.85KB per node. Third, intents assist in configuring a hierarchical cache to preload I/O, buffer in a node-local device, and store data in a global cache to optimize I/O workloads by 2.33×, 4×, and 2.1×, respectively. Our Mimir infrastructure optimizes complex large-scale workflows by up to 4× better I/O performance on the Lassen supercomputer by using automatically derived I/O intents.