This paper introduces DANSEN, the hardware accelerator component for neoDBMS, a full-stack computational storage system designed to manage on-device execution of database queries/transactions as a Near-Data Processing (NDP)-operation. The proposed system enables Database Management Systems (DBMS) to offload NDP-operations to the storage while maintaining control over data through a native storage interface. DANSEN provides an NDP-engine that enables DBMS to perform both low-level database tasks, such as performing database administration, as well as high-level tasks like executing SQL, on the smart storage device while observing the DBMS concurrency control. Furthermore, DANSEN enables the incorporation of custom accelerators as an NDP-operation, e.g., to perform hardware-accelerated ML inference directly on the stored data. We built the DANSEN storage prototype and interface on an Ultrascale+HBM FPGA and fully integrated it with PostgreSQL 12. Experimental results demonstrate that the proposed NDP approach outperforms software-only PostgreSQL using a fast off-the-shelf NVMe drive, and significantly improves the end-to-end execution time of an aggregation operation (similar to Q6 from CH-benCHmark, 150 million records) by ≈ 10.6 ×. The versatility of the proposed approach is also validated by integrating a compute-intensive data analytics application with multi-row results, outperforming PostgreSQL by ≈ 1.5 ×.

本文介绍丹森，neoDBMS 的硬件加速器组件，neoDBMS 是一个全栈计算存储系统，旨在将数据库查询/事务的设备上执行作为近数据处理 (NDP) 操作进行管理。所提出的系统使数据库管理系统（DBMS）能够将 NDP 操作卸载到存储，同时通过本机存储接口保持对数据的控制。丹森提供一个 NDP 引擎，使 DBMS 能够在智能存储设备上执行低级数据库任务（例如执行数据库管理）以及高级任务（例如执行 SQL），同时观察 DBMS 并发控制。此外，丹森允许将自定义加速器合并为 NDP 操作，例如直接对存储的数据执行硬件加速的 ML 推理。我们建造了丹森在 Ultrascale+HBM FPGA 上构建存储原型和接口，并将其与 PostgreSQL 12 完全集成。实验结果表明，所提出的 NDP 方法优于使用快速现成 NVMe 驱动器的纯软件 PostgreSQL，并显着提高了端到端性能聚合操作的结束执行时间（类似于 CH-benCHmark 的 Q6，1.5 亿条记录）约 10.6 ×。该方法的多功能性还通过将计算密集型数据分析应用程序与多行结果集成来验证，其性能优于 PostgreSQL 约 1.5 倍。