High throughput heavy hitter aggregation for modern SIMD processors

Abstract

Heavy hitters are data items that occur at high frequency in a data set. They are among the most important items for an organization to summarize and understand during analytical processing. In data sets with sufficient skew, the number of heavy hitters can be relatively small. We take advantage of this small footprint to compute aggregate functions for the heavy hitters in fast cache memory in a single pass. We design cache-resident, shared-nothing structures that hold only the most frequent elements. Our algorithm works in three phases. It first samples and picks heavy hitter candidates. It then builds a hash table and computes the exact aggregates of these elements. Finally, a validation step identifies the true heavy hitters from among the candidates. We identify trade-offs between the hash table configuration and performance. Configurations consist of the probing algorithm and the table capacity that determines how many candidates can be aggregated. The probing algorithm can be perfect hashing, cuckoo hashing and bucketized hashing to explore trade-offs between size and speed. We optimize performance by the use of SIMD instructions, utilized in novel ways beyond single vectorized operations, to minimize cache accesses and the instruction footprint.