Optimization of instruction fetch for decision support workloads

Instruction fetch bandwidth is feared to be a major limiting factor to the performance of future wide-issue aggressive superscalars. In this paper, we focus on database applications running decision support workloads. We characterize the locality patterns of ia database kernel and find frequently executed paths. Using this information, we propose an algorithm to lay out the basic blocks for improved I-fetch. Our results show a miss reduction of 60-98% for realistic I-cache sizes and a doubling of the number of instructions executed between taken branches. As a consequence, we increase the fetch bandwith provided by an aggressive sequential fetch unit from 5.8 for the original code to 10.6 using our proposed layout. Our software scheme combines well with hardware schemes like a trace cache providing up to 12.1 instruction per cycle, suggesting that commercial workloads may be amenable to the aggressive I-fetch of future superscalars