SIMD Vectorized Hashing for Grouped Aggregation

Abstract

Grouped aggregation is a commonly used analytical function. The common implementation of the function using hashing techniques suffers lower throughput rate due to the collision of the insert keys in the hashing techniques. During collision, the underlying technique searches for an alternative location to insert keys. Searching an alternative location increases the processing time for an individual key thereby degrading the overall throughput. In this work, we use Single Instruction Multiple Data (SIMD) vectorization to search multiple slots at an instant followed by direct aggregation of results. We provide our experimental results of our vectorized grouped aggregation with various open-addressing hashing techniques using several dataset distributions and our inferences on them. Among our findings, we observe different impacts of vectorization on these techniques. Namely, linear probing and two-choice hashing improve their performance with vectorization, whereas cuckoo and hopscotch hashing show a negative impact. Overall, we provide in this work a basic structure of a dedicated SIMD accelerated grouped aggregation framework that can be adapted with different hashing techniques.