MOSIQS: Persistent Memory Object Storage With Metadata Indexing and Querying for Scientific Computing

Awais Khan,Hyogi Sim,Sudharshan S Vazhkudai,Youngjae Kim

doi:10.1109/access.2021.3087502

Awais Khan, Hyogi Sim + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3087502

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Abstract

Scientific applications often require high-bandwidth shared storage to perform joint simulations and collaborative data analytics. Shared memory pools provide a chance to satisfy such needs. Recently, a high-speed network such as Gen-Z utilizing persistent memory (PM) offers an opportunity to create a shared memory pool connected to compute nodes. However, there are several challenges to use scientific applications on the shared memory pool directly such as scalability, failure-atomicity, and lack of scientific metadata-based search and query. In this paper, we propose MOSIQS, a persistent memory object storage framework with metadata indexing and querying for scientific computing. We design MOSIQS based on the key idea that memory objects on PM pool can live beyond the application lifetime and can become the sharing currency for applications and scientists. MOSIQS provides an aggregate memory pool atop an array of persistent memory devices to store and access memory objects to accelerate scientific computing. MOSIQS uses a lightweight persistent memory key-value store to manage the metadata of memory objects, which enables memory object sharing. To facilitate metadata search and query over millions of memory objects resident on memory pool, we introduce Group Split and Merge (GSM), a novel persistent index data structure designed primarily for scientific datasets. GSM splits and merges dynamically to minimize the query search space and maintains low query processing time while overcoming the index storage overhead. MOSIQS is implemented on top of PMDK. We evaluate the proposed approach on many-core server with an array of real PM devices. Experimental results show that MOSIQS gains a 100% write performance improvement and executes multi-attribute queries efficiently with 2.7× less index storage overhead offering significant potential to speed up scientific computing applications.

Highlights

Large-scale scientific applications, including simulations, experiments, and observations, generate tens of petabytes of data objects and are forecasted to grow even further [1]–[3]
To enable efficient multi-attribute metadata search and querying for scientific data objects resident in Persistent Memory (PM) pool, we introduce Group Split-and-Merge (GSM) index data structure (Section IV)
EXPERIMENTAL SETUP Testbed: We perform our experiments on a machine equipped with second-generation Intel Xeon scalable dualsocket 56-core processor with 1.5 TB Intel Optane DC 3D-XPoint PM, and 768 GB DRAM

Summary

Introduction

Large-scale scientific applications, including simulations, experiments, and observations, generate tens of petabytes of data objects and are forecasted to grow even further [1]–[3]. Many applications from various scientific domains tend to store experimental, simulation, and analytical data in domain-specific scientific data formats such as HDF5 [33], netCDF [34], FITS [36], Plot3D [37], and GRIB [38] in the underlying parallel file system. These scientific data formats are often referred to as self-described and self-contained, i.e., the metadata is stored alongside the data objects [26].

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