A Probability Model of Calculating L2 Cache Misses
Kecheng Ji, Ming Ling, Li Liu
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.71How to use a DOI?
- probability model; stack distance; reuse distance; L2 cache misses
- Stack or reuse distances have been widely adopted in studying memory localities and cache behaviors. However, the memory references, normally profiled by a binary instru-mentation tool, only reflect the accessing sequence of instruction fetching and load or store executions. That is why the stack or the reuse distances obtained from these memory references cannot be used to predict the L2 or lower cache misses. This paper proposes a probability model to calculate the L2 reuse distance histogram from the L1 stack distance histograms without any extra simulations. The L2 cache misses or memory localities can be predicted fast and accurately based on the result of our model. We use 13 benchmarks chosen from Mobybench 2.0 and SPEC 2006 to evaluate the accuracy of our model. With the support of StatCache and StatStack, the average absolute error of modeling the L2 cache misses is about 8%. Meanwhile, contrast to gem5 fast simulations, the process of predicting L2 cache misses can be sped up by 50 times on average.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Kecheng Ji AU - Ming Ling AU - Li Liu PY - 2018/02 DA - 2018/02 TI - A Probability Model of Calculating L2 Cache Misses BT - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018) PB - Atlantis Press SP - 329 EP - 332 SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.71 DO - https://doi.org/10.2991/csece-18.2018.71 ID - Ji2018/02 ER -