SSD in-storage computing for list intersection

Abstract

Recently, there has been a renewed interest of in-storage computing in the context of solid state drives (SSDs), called "Smart SSDs." Smart SSDs allow application-specific code to execute inside SSDs. This allows applications to take advantage of the high internal bandwidth that Smart SSDs provide. This work studies the offloading of list intersection into Smart SSDs, because intersection is prominent in both search engines and analytics queries. Furthermore, intersection is interesting because the algorithms are more complex than plain scans; they are affected by multiple parameters, as we show, and provide lessons that can be used in other operations also. We are interested to know whether Smart SSDs can accelerate the processing of list intersection and reduce the consumed energy. Intuitively, the answer is yes. However, the performance tradeoffs on real devices are complex. We implement list intersection into a real Samsung Smart SSD research prototype. We also provide an analytical model to understand the key factors to the overall performance, and when list intersection can benefit from Smart SSDs. Finally, we conduct experiments on the Samsung Smart SSD. Based on the results (both analytical and experimental), we provide many suggestions for both SSD vendors on how to manufacture powerful Smart SSDs and for applications on how to make full use of the functionalities that Smart SSDs provide.