Phase-Based Miss Rate Prediction Across Program Inputs
Xipeng Shen, Yutao Zhong, and Chen Ding
ABSTRACT
Previous work shows the possibility of predicting the cache miss
rate (CMR) for all inputs of a program. However, most
optimization techniques need to know more than the miss rate of
the whole program. Many of them benefit from knowing miss
rate of each execution phase of a program for all inputs.
In this paper, we describe a method that divides a program into
phases that have a regular locality pattern. Using a regression
model, it predicts the reuse signature and then the cache miss
rate of each phase for all inputs. We compare the
prediction with the actual measurement. The average
prediction is over 98% accurate for a set of floating-point
programs. The predicted CMR-traces matches the simulated
ones in spite of dramatic fluctuations of the miss rate over
time. This technique can be used for improving dynamic
optimization, benchmarking, and compiler design.
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