PyMimircache: a Python3 cache analysis platform¶
Release v0.0.2.103.
Welcome to the documentation of PyMimircache, a Python3 cache analysis platform. The target users of PyMimircache are researchers and system administrators. The goal behind PyMimircache is to provide a platform that
- allows researchers to study and design cache replacement algorithms easily and efficiently.
- allows system administrators to analyze and visualize their cache performance easily and efficiently.
The power of PyMimircache:
An example of hit ratio curve plot and hit ratio heatmap.
>>> from PyMimircache import Cachecow
>>> c = Cachecow()
>>> c.vscsi("trace.vscsi") # this file is in the data folder on GitHub, other data types also supported
>>> print(c.stat())
>>> print(c.get_reuse_distance())
[-1 -1 -1 -1 -1 -1 11 7 11 8 8 8 -1 8]
>>> print(c.get_hit_ratio_dict("LRU", cache_size=20))
{0: 0.0, 1: 0.025256428270338627, 2: 0.031684698608964453, ... 20: 0.07794716875087819}
>>> c.plotHRCs(["LRU", "LFU", "Optimal"])
>>> c.heatmap('r', "hit_ratio_start_time_end_time", time_interval=10000000)
The User Guide¶
Supported Features¶
- Cache replacement algorithms simulation.
- trace visualization.
- A variety of cache replacement algorithms, including LRU, LFU, MRU, FIFO, Clock, LinuxClock, TEAR, Random, ARC, SLRU, Optimal and etc.
- Hit/miss ratio curve (HRC/MRC) plotting.
- Efficient reuse distance calculation for LRU.
- Heatmap plotting for visualizing cache dynamics.
- Reuse distance distribution plotting.
- Cache replacement algorithm comparison.
Customization¶
Now you can customize PyMimircache to fit your own need. You can write
- your own cache reader for reading your special cache trace files.
- your own cache replacement algorithms.
- a middleware for sampling your cache traces for analysis.