Transfer Data from NetCDF on Hierarchical Storage to Zarr on Object Storage: CMIP6 Climate Data Use Case

<p>This study provides a guidance to data providers on how to transfer existing NetCDF data from a hierarchical storage system into Zarr to an object storage system.</p><p>In recent years, object storage systems became an alternative to traditional hierarchical file systems, because they are easily scalable and offer faster data retrieval, as compared to hierarchical storage systems.</p><p>Earth system sciences, and climate science in particular, handle large amounts of data. These data usually are represented as multi-dimensional arrays and traditionally stored in netCDF format on hierarchical file systems. However, the current netCDF-4 format is not yet optimized for object storage systems. NetCDF data transfers from an object storage can only be conducted on file level which results in heavy download volumes. An improvement to mitigate this problem can be the Zarr format, which reduces data transfers, due to the direct chunk and meta data access and hence increases the input/output operation speed in parallel computing environments.</p><p>As one of the largest climate data providers worldwide, the German Climate Computing Center (DKRZ) continuously works towards efficient ways to make data accessible for the user. This use case shows the conversion and the transfer of a subset of the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data archive from netCDF on the hierarchical file system into Zarr to the OpenStack object store, known as Swift, by using the Zarr Python package. Conclusively, this study will evaluate to what extent Zarr formatted climate data on an object storage system is a meaningful addition to the existing high performance computing environment of the DKRZ.</p>

NVMM-Oriented Hierarchical Persistent Client Caching for Lustre

In high-performance computing (HPC), data and metadata are stored on special server nodes and client applications access the servers’ data and metadata through a network, which induces network latencies and resource contention. These server nodes are typically equipped with (slow) magnetic disks, while the client nodes store temporary data on fast SSDs or even on non-volatile main memory (NVMM). Therefore, the full potential of parallel file systems can only be reached if fast client side storage devices are included into the overall storage architecture. In this article, we propose an NVMM-based hierarchical persistent client cache for the Lustre file system (NVMM-LPCC for short). NVMM-LPCC implements two caching modes: a read and write mode (RW-NVMM-LPCC for short) and a read only mode (RO-NVMM-LPCC for short). NVMM-LPCC integrates with the Lustre Hierarchical Storage Management (HSM) solution and the Lustre layout lock mechanism to provide consistent persistent caching services for I/O applications running on client nodes, meanwhile maintaining a global unified namespace of the entire Lustre file system. The evaluation results presented in this article show that NVMM-LPCC can increase the average read throughput by up to 35.80 times and the average write throughput by up to 9.83 times compared with the native Lustre system, while providing excellent scalability.