North Carolina State University
Analytical modeling is becoming an increasingly important technique used in the design of chip multiprocessors. Most such models assume multi-programmed workload mixes and either ignore or oversimplify the behavior of multi-threaded applications. In particular, data sharing observed in multi-threaded applications, and its impact on chip design decisions, has not been well characterized in prior analytical modeling work. In this paper, the authors describe why data sharing behavior is hard to capture in an analytical model, and study why, and by how much, past attempts have fallen short. They propose a new methodology to measure the impact of data sharing, which quantifies the reduction in on-chip cache miss rates attributable solely to the presence of data sharing.