Exploration of I/O Reproducibility with HDF5
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/OMPI-I/OReproducibilityHPCHDF5 - Skills: C/C++, Python
- Difficulty: Medium
- Size: Large (350 hours)
- Mentors: Luanzheng "Lenny" Guo and [Wei Zhang]Wei Zhang
Luanzheng "Lenny" Guo
Computer Scientist, Pacific Northwest National Laboratory
Luanzheng (Lenny) Guo is a computer scientist in Pacific Northwest National Laboratory’s Future Computing Technologies group, working within the research area between scientific computing, data management, large-scale systems (e.g., HPC, CPS, Cloud, Edge, etc.), and machine learning (ML).
Wei Zhang
Postdoctoral Researcher, Lawrence Berkeley National Laboratory
Dr. Wei Zhang (张威) is a Computer Science Researcher at Lawrence Berkeley National Laboratory (LBNL), specializing in advancing data management for scientific applications in heterogeneous computing environments. His research focuses on bridging the gap between high-performance computing (HPC) and artificial intelligence (AI) by developing innovative solutions for managing and discovering large-scale scientific data.