1. ECE-752 Zheng Zheng, Anuj Gadiyar
Implementation, Benchmark and Comparison of Frequency-Based Replacement
ECE-752
Zheng Zheng, Anuj Gadiyar

2. Motivation
From the research trend towards virtual software managed cache, the concern is put into performance without traditional constraints.
Traditional LRU is not suitable for a good replacement policy
Frequency based replacement improves practical performance

3. Least recently used-LRU
The most known and traditional replacement policy. Evicts oldest entry each time.
Disadvantage: worst case poor performance and no utilizing frequency

4. Frequency-based replacement
Improved from LRU
Stack partition:
New section: store more recently added entry
Old section: entries with more stale state

5. Frequency-based replacement
The traditional implementation does not avoid locality transition

6. Frequency-based replacement
The partitioning takes two parameter to mark boundaries:
New section to avoid monotonic instructions
Old section to spawn most stale entry to be replaced

7. Testbench Cache2K Test Kit JMH 1.11.3 Amazon AWS
Intel(R) Core(TM) i7-5600U DDR3 1600MHz
2 cores with hyperthreading enabled

8. Trace Source Sprite trace
This trace is a long-tail distribution and is the representative from unix server cluster, which contains requests to a file server from client workstations for a two-day period.

9. Experiment Cache_Size(KB): 512KB-8MB Line_Size: 64 Associativity:
16-256

10. Experiment

11. Conclusion
The feature of factoring out locality can be achieved using stack partitioning
The FBR cache takes two parameter as input. In the practical use, FBR suffers from frequent context switch.
The popularity of FBR was due to its higher performance compared to LRU.

12. Future research focus
More analytic FBR models might be designed to optimize performance from the parameter
Additional support in the highly parallel use case
To optimize hardware requirement and put into use in lower level design

13. Reference
[1] J. T. Robinson and M. V. Devarakonda, “Data cache management using frequency-based replacement,” in SIGMETRICS Conf, 1990.
[2] N. Megiddo, and D. Modha, “Outperforming LRU with an Adaptive Replacement Cache Algorithm”, Computer, Vol. 37, No. 4, April 2004, pp
[3] Jiménez, Daniel A.. “Insertion and promotion for tree-based Pseudo LRU last-level caches.” MICRO (2013).