Improving In-Memory Database Operations with Acceleration DIMM (AxDIMM)

Lee, Donghun; So, Jinin; Ahn, Minseon; Lee, Jong-Geon; Kim, Jungmin; Cho, Jeonghyeon; Rebholz, Oliver; Thummala, Vishnu Charan; JV, Ravi shankar; Upadhya, Sachin Suresh; Khan, Mohammed Ibrahim; Kim, Jin Hyun

doi:10.1145/3533737.3535093

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Cited by 12 publications

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Accelerating Large Table Scan Using Processing-In-Memory Technology

Baumstark,

Jibril,

Sattler

2023

Datenbank Spektrum

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Today’s systems are capable of storing large amounts of data in main memory. Particularly, in-memory DBMSs benefit from this development. However, the processing of data from the main memory necessarily has to run via the CPU. This creates a bottleneck, which affects the possible performance of the DBMS. Processing-In-Memory (PIM) is a paradigm to overcome this problem, which was not available in commercial systems for a long time. With the availability of UPMEM, a commercial product is finally available that provides PIM technology in hardware. In this work, we focus on the acceleration of the table scan, a fundamental database query operation. We show and investigate an approach that can be used to optimize this operation by using PIM. We evaluate the PIM scan in terms of parallelism and execution time in benchmarks with different table sizes and compare it to a traditional CPU-based table scan. The result is a PIM table scan that outperforms the CPU-based scan significantly.

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