VIP: A SIMD vectorized analytical query engine

Query execution engines for analytics are continuously adapting to the underlying hardware in order to maximize performance. Wider SIMD registers and more complex SIMD instruction sets are emerging in mainstream CPUs and new processor designs such as the many-core Intel Xeon Phi CPUs that rely on SIMD vectorization to achieve high performance per core while packing a greater number of smaller cores per chip. In the database literature, using SIMD to optimize stand-alone operators with key–rid pairs is common, yet the state-of-the-art query engines rely on compilation of tightly coupled operators where hand-optimized individual operators become impractical. In this article, we extend a state-of-the-art analytical query engine design by combining code generation and operator pipelining with SIMD vectorization and show that the SIMD speedup is diminished when execution is dominated by random memory accesses. To better utilize the hardware features, we introduce VIP, an analytical query engine designed and built bottom up from pre-compiled column-oriented data parallel sub-operators and implemented entirely in SIMD. In our evaluation using synthetic and TPC-H queries on a many-core CPU, we show that VIP outperforms hand-optimized query-specific code without incurring the runtime compilation overhead, and highlight the efficiency of VIP at utilizing the hardware features of many-core CPUs.