In-RDBMS Hardware Acceleration of Advanced Analytics Divya Mahajan∗ Joon Kyung Kim∗ Jacob Sacks∗ Adel Ardalan† Arun Kumar‡ Hadi Esmaeilzadeh‡ ∗Georgia Institute of Technology †University of Wisconsin-Madison ‡University of California, San Diego fdivya mahajan,jkkim,
[email protected] [email protected] farunkk,
[email protected] ABSTRACT The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently. As Enterprise Bismarck [7] such, there is a void of solutions that enables hardware accelera- In-Database Glade [8] tion at the intersection of these disjoint fields. This paper sets out Analytics Centaur [3] to be the initial step towards a unifying solution for in-Database DoppioDB [4] Acceleration of Advanced Analytics (DAnA). Deploying special- Modern DAnA Analytical ized hardware, such as FPGAs, for in-database analytics currently Acceleration Programing Platforms Paradigms requires hand-designing the hardware and manually routing the data. Instead, DAnA automatically maps a high-level specifica- tion of advanced analytics queries to an FPGA accelerator. The TABLA [5] accelerator implementation is generated for a User Defined Func- Catapult [6] tion (UDF), expressed as a part of an SQL query using a Python- Figure 1: DAnA represents the fusion of three research directions, embedded Domain-Specific Language (DSL). To realize an effi- in contrast with prior works [3{5,7{9] that merge two of the areas. cient in-database integration, DAnA accelerators contain a novel hardware structure, Striders, that directly interface with the buffer terprise settings. However, data-driven applications in such envi- pool of the database.