Exploration of I/O Reproducibility with HDF5

Wed, 19 Feb 2025 09:00:00 -0700

Parallel I/O is a critical component in high-performance computing (HPC), allowing multiple processes to read and write data concurrently from a shared storage system. HDF5—a widely adopted data model and library for managing complex scientific data—supports parallel I/O but introduces challenges in I/O reproducibility, where repeated executions do not always produce identical results. This lack of reproducibility can stem from non-deterministic execution orders, variations in collective buffering strategies, and race conditions in metadata and dataset chunking operations within HDF5’s parallel I/O hierarchy. Moreover, many HDF5 operations that leverage MPI I/O require collective communication; that is, all processes within a communicator must participate in operations such as metadata creation, chunk allocation, and data aggregation. These collective calls ensure that the file structure and data layout remain consistent across processes, but they also introduce additional synchronization complexity that can impact reproducibility if not properly managed. In HPC scientific workflows, consistent I/O reproducibility is essential for accurate debugging, validation, and benchmarking, ensuring that scientific results are both verifiable and trustworthy. Tools such as h5bench—a suite of I/O kernels designed to exercise HDF5 I/O on parallel file systems—play an important role in identifying these reproducibility challenges, tuning performance, and ultimately supporting the overall robustness of large-scale scientific applications.

Workplan

The proposed work will include (1) analyzing and characterizing parallel I/O operations in HDF5 with h5bench miniapps, (2) exploring and validating potential reproducibility challenges within the parallel I/O hierarchy (e.g., MPI I/O), and (3) implementing solutions to address parallel I/O reproducibility.

Topics: Parallel I/O MPI-I/O Reproducibility HPC HDF5
Skills: C/C++, Python
Difficulty: Medium
Size: Large (350 hours)
Mentors: Luanzheng "Lenny" Guo and [Wei Zhang]Wei Zhang

Wei Zhang | UCSC OSPO

Exploration of I/O Reproducibility with HDF5

Workplan