Replacement Policy Simulator

1 Overview

Dependencies

The following dependencies will need to be installed

pip3 install xlsxwriter plotly argparse openpyxl pandas tensorflow sklearn silence_tensorflow

Configuration

Memory can be...

• Read from a file
• Randomly generated

The simulations that will be ran can be changed in src/simulation.py in the SimConfig function. This file will allow you to add/remove simulations along with change the replacement policy and the set associativity. The title value will be applied to the final graph of the data.

def SimConfig(self):
	self.sim_configs = []

	self.title = '2-Way Set Associative Replacement Policy Comparison (' + self.mem_pattern + ')'

	self.sim_configs.append(Cache(config_name='LRU Cache (2-way)',   repl='LRU',     ways=2))
	self.sim_configs.append(Cache(config_name='RR Cache (2-way)',    repl='RR',      ways=2))
	self.sim_configs.append(Cache(config_name='LFRU Cache (2-way)',  repl='LFRU',    ways=2))
	self.sim_configs.append(Cache(config_name='LFU Cache (2-way)',   repl='LFU',     ways=2))
	self.sim_configs.append(Cache(config_name='FIFO Cache (2-way)',  repl='FIFO',    ways=2))
	self.sim_configs.append(Cache(config_name='MRU Cache (2-way)',   repl='MRU',     ways=2))

Output

Data will be saved to an excel workbook and a graph demonstrating the hit ratio. These will be located in the output folder

Supported Replacement Policies

Standard Replacement Policies

• LRU: Least Recently Used
  • Discards the least recently used items first.
• MRU: Most Recently Used
  • Discards the most recently used items first.
• RR: Random Replacement
  • Randomly chooses which item to discard
• LFRU: Least Frequently Recently Used
  • Replaces an item with the least usage and the least recently used
• LFU: Least Frequently Used
  • Replaces an item with the least usage
• FIFO: First In First Out
  • Replace the item that was added first with no regard to its usage

Custom Replacement Policies

• LRUML: Least Recently Used with Machine Learning
  • Discards the least recently used items first with machine learning checks and optimizations

2 Running

Command Line Arguments

Long	Description
--file	Input file containing memory addresses
--memsize	Size of memory file to be generated
--memrange	Max memory value to be generated
--mempattern	The memory pattern to be used (normal, loops, rep, random)
--cachesize	Size of the cache
--linesize	Size of the cache line
--mult	Run simulation a specified number of times
--test	Test the simulators functionality

All arguments are optional. memsize, memrange, and mempattern are used when auto generating a memory file. file is used to read in a memory file. memsize, memrange, and mempattern can't be used with file.

When running mult, specify a positive integer. This setting cannot be used with file. Each new sim will generate a completely new memory file to use.

Generated memory files can follow four patterns:

• Normal: A combination of random addresses, loops, and repetition
• Random: Completely random memory addresses
• Loops: A heavy amount of loops with some random addresses and repetition
• Repetitive: A heavy amount of repetition with some loops and random addresses

Examples

Running with a memory file

python3 run.py -f mem/sample_mem.txt 

Running with auto generated memory

python3 run.py --memsize 2000 --memrange 2000 --mempattern normal

Running with a configured cache

python3 run.py --cachesize 64KB --linesize 32B

Running multiple simulations

python3 run.py --mult 3

Authors

• Jake Summerville
• Henry Lee
• Martin Lopez
• Fausto Sanchez