On the Performance of Fetch Engines Running DSS Workloads

This paper examines the behavior of current and next generation microprocessors' fetch engines while running Decision Support Systems (DSS) workloads. We analyze the effect of the latency of instructions being fetched, their quality and the number of instructions that the fetch engine provides per access. Our study reveals that a well dimensioned fetch engine is of great importance for DSS performance, showing gains over 100% between a conventional fetch engine and a perfect one. We have found that, in many cases, the I-cache size bounds the benefits that one might expect from a better branch prediction. The second part of our study focuses on the performance benefits of a code reordering technique for the DatabaseManagement System (DBMS) that runs our DSS workload. Our results show that the reordering has a positive effect on the three parameters and can speed-up the DSS execution by 21% for a 4 issue processor, and 27% for an 8 issue one.