Co-Utilizing SIMD and Scalar to Accelerate the Data Analytics Workloads

The increasing capacity and reducing cost of the main memory made in-memory data analytics systems widely deployed as they could provide higher throughput and lower latency. Since the data resides in memory, computational throughput becomes a crucial factor in the performance of these systems rather than disk accesses. Single instruction multiple data (SIMD) is an effective mechanism to improve computational performance, which has been well studied to accelerate data analytics systems. However, the state-of-the-art methods focus on using SIMD more efficiently while neglecting scalar execution units.In this paper, we present the hybrid execution framework (HEF) to co-utilize SIMD and scalar execution units for the data analytics workload. We also extend the concept of pack to eliminate the data dependency between adjacent instructions, achieving shorter instruction execution intervals. Experimental results show that the hybrid execution achieves up to 2.38× and 1.45× better performance compared with the purely scalar and SIMD implementation on the star schema benchmark (SSB) queries, respectively. Besides, HEF performs better than the state-of-the-art system Voila for a majority of queries in SSB under all data scales.