Improving In-Memory Database Operations with Acceleration DIMM (AxDIMM)
Donghun Lee, Jinin So, Minseon Ahn, Jong-Geon Lee, Jungmin Kim, Jeong‐Hyeon Cho, Oliver Rebholz, Vishnu Charan Thummala, Ravi shankar JV, Sachin Suresh Upadhya, Mohammed Ibrahim Khan, Jin Hyun Kim
Abstract
The significant overhead needed to transfer the data between CPUs and memory devices is one of the hottest issues in many areas of computing, such as database management systems. Disaggregated computing on the memory devices is being highlighted as one promising approach. In this work, we introduce a new near-memory acceleration scheme for in-memory database operations, called Acceleration DIMM (AxDIMM). It behaves like a normal DIMM through the standard DIMM-compatible interface, but has embedded computing units for data-intensive operations. With the minimized data transfer overhead, it reduces CPU resource consumption, relieves the memory bandwidth bottleneck, and boosts energy efficiency. We implement scan operations, one of the most data-intensive database operations, within AxDIMM and compare its performance with SIMD (Single Instruction Multiple Data) implementation on CPU. Our investigation shows that the acceleration achieves 6.8x more throughput than the SIMD implementation.