Load-balancing Parallel I/O of Compressed Hierarchical Layouts

Scientific phenomena are being simulated at ever-increasing resolution and fidelity thanks to advances in modern supercomputers. These simulations produce a deluge of data, putting unprecedented demand on the end-to-end data-movement pipeline that consists of parallel writes for checkpoint and analysis dumps and parallel localized reads for exploratory analysis and visualization tasks. Parallel I/O libraries are often optimized for uniformly distributed large-sized accesses, whereas reads for analysis and visualization benefit from data layouts that enable random-access and multiresolution queries. While multiresolution layouts enable interactive exploration of massive datasets, efficiently writing such layouts in parallel is challenging, and straightforward methods for creating a multiresolution hierarchy can lead to inefficient memory and disk access. In this paper, we propose a compressed, hierarchical layout that facilitates efficient parallel writes, while being efficient at serving random access, multiresolution read queries for post-hoc analysis and visualization. To efficiently write data to such a layout in parallel is challenging due to potential load-balancing issues at both the data transformation and disk I/O steps. Data is often not readily distributed in a way that facilitates efficient transformations necessary for creating a multi resolution hierar-chy. Further, when compression or data reduction is applied, the compressed data chunks may end up with different sizes, confounding efficient parallel I/O. To overcome both these issues, we present a novel two-phase load-balancing strategy to optimize both memory and disk access patterns unique to writing non-uniform multiresolution data. We implement these strategies in a parallel I/O library and evaluate the efficacy of our approach by using real-world simulation data and a novel approach to micro benchmarking on the Theta Supercomputer of Argonne National Laboratory.