ADDICT: Advanced Instruction Chasing for Transactions

Recent studies highlight that traditional transaction processing systems utilize the micro-architectural features of modern processors very poorly. L1 instruction cache and long-latency data misses dominate execution time. As a result, more than half of the execution cycles are wasted on memory stalls. Previous works on reducing stall time aim at improving locality through either hardware or software techniques. However, exploiting hardware resources based on the hints given by the software-side has not been widely studied for data management systems. In this paper, we observe that, independently of their high-level functionality, transactions running in parallel on a multicore system execute actions chosen from a limited subset of predefined database operations. Therefore, we initially perform a memory characterization study of modern transaction processing systems using standardized benchmarks. The analysis demonstrates that same-type transactions exhibit at most 6% overlap in their data footprints whereas there is up to 98% overlap in instructions. Based on the findings, we design ADDICT, a transaction scheduling mechanism that aims at maximizing the instruction cache locality. ADDICT determines the most frequent actions of database operations, whose instruction footprint can fit in an L1 instruction cache, and assigns a core to execute each of these actions. Then, it schedules each action on its corresponding core. Our prototype implementation of ADDICT reduces L1 instruction misses by 85% and the long latency data misses by 20%. As a result, ADDICT leads up to a 50% reduction in the total execution time for the evaluated workloads.