def profiler(self, cache_class, **kargs):
if 'size' in kargs:
size = kargs['size']
else:
size = self.cache_size
if 'data' in kargs and 'dataType' in kargs:
if kargs['dataType'] == 'plain':
reader = plainCacheReader(kargs['data'])
if kargs['dataType'] == 'csv':
reader = csvCacheReader(kargs['data'])
if kargs['dataType'] == 'vscsi':
reader = vscsiCacheReader(kargs['data'])
else:
reader = self.reader
assert size != -1, "you didn't provide size for cache"
assert reader, "you didn't provide data file or data type"
profiler = None
# print(cache_class)
# print(LRU)
if cache_class.lower() == "lru":
profiler = pardaProfiler(LRU, size, reader)
# print(profiler)
else:
# TODO
# parameters about cache class can be passed in kargs
pass
return profiler
def prepare_file(self):
print("changing file format")
with open('temp.dat', 'w') as ofile:
i = self.reader.read_one_element()
while i != None:
ofile.write(str(i) + '\n')
i = self.reader.read_one_element()
self.reader = plainCacheReader('temp.dat')
def prepare_file(self):
print("changing file format")
with open('temp.dat', 'w') as ofile:
i = self.reader.read_one_element()
while i != None:
ofile.write(str(i) + '\n')
i = self.reader.read_one_element()
self.reader = plainCacheReader('temp.dat')
count = 0
for i in self.c_long_array:
if i > -1 and i < cache_size:
count += 1
print(count)
# def __del__(self):
# if os.path.exists('temp.dat'):
# os.remove('temp.dat')
if __name__ == "__main__":
p = pardaProfiler(LRU, 30000, plainCacheReader("../data/parda.trace"))
# p = parda(LRU, 30000, csvCacheReader("../data/trace_CloudPhysics_txt", 4))
# p = parda(LRU, 30000, vscsiReader("../data/cloudPhysics/w02_vscsi1.vscsitrace"))
# p = parda(LRU, 3000000, basicCacheReader("temp.dat"))
# p.run(parda_mode.seq, threads=4)
# p.get_reuse_distance()
import os
import shutil
for f in os.listdir('../data/mining/'):
shutil.copy('../data/mining/' + f, '../data/mining/mining.dat')
print(f)
p._test()
# p.run_with_specified_lines(10000, 20000)
# p.plotHRC(autosize=True, autosize_threshhold=0.00001)
from mimircache.cache.LRU import LRU
from mimircache.cacheReader.plainReader import plainCacheReader
from mimircache.cacheReader.csvReader import csvCacheReader
from mimircache.cacheReader.vscsiReader import vscsiCacheReader
from mimircache.profiler.basicLRUProfiler import basicLRUProfiler
from mimircache.profiler.pardaProfiler import pardaProfiler
from mimircache.profiler.pardaProfiler import parda_mode
# first step: construct a reader for reading any kind of trace
# this one is the most basic one, each line is a label/tag
reader1 = plainCacheReader("../data/parda.trace")
# this one reads csv file and choose one column as label/tag
reader2 = csvCacheReader("../data/trace_CloudPhysics_txt", column=4)
# this one reads binary cloudphysics trace file
reader3 = vscsiCacheReader("../data/trace_CloudPhysics_bin")
# reader is also a generator, for readers you can do the following thing:
# read one trace element at one time:
reader1.read_one_element()
# for loop:
# for element in reader1:
# pass
# reset, after read some elements, you want to go back
reader1.reset()
# second step: construct a profiler for analyze
# basic mattson profiler (toooooo slow)
self.reader.reset()
for i in self.reader:
self.addOneTraceElement(i)
# p.printMRC()
self.outputHRC()
self.plotHRC()
if __name__ == "__main__":
import time
import cProfile
t1 = time.time()
# r = plainCacheReader('../../data/test')
r = plainCacheReader('../../data/parda.trace')
# p = generalProfiler(LRU, 6000, 20, r, 48)
# p = generalProfiler(ARC, (10, 0.5), 10, r, 1)
p = generalProfiler(LFU_LRU, 800, 80, r, 4)
# p = generalProfiler(ARC, 5, 5, r, 1)
import pstats, io
pr = cProfile.Profile()
pr.enable()
p.run()
print(count)
self.parda_seq.get_reuse_distance(c_file_name, c_line_num,
self.c_cache_size, self.c_long_array)
count = 0
for i in self.c_long_array:
if i > -1 and i < cache_size:
count += 1
print(count)
# def __del__(self):
# if os.path.exists('temp.dat'):
# os.remove('temp.dat')
if __name__ == "__main__":
p = pardaProfiler(LRU, 30000, plainCacheReader("../data/parda.trace"))
# p = parda(LRU, 30000, csvCacheReader("../data/trace_CloudPhysics_txt", 4))
# p = parda(LRU, 30000, vscsiReader("../data/cloudPhysics/w02_vscsi1.vscsitrace"))
# p = parda(LRU, 3000000, basicCacheReader("temp.dat"))
# p.run(parda_mode.seq, threads=4)
# p.get_reuse_distance()
import os
import shutil
for f in os.listdir('../data/mining/'):
shutil.copy('../data/mining/' + f, '../data/mining/mining.dat')
print(f)
p._test()
# p.run_with_specified_lines(10000, 20000)
# p.plotHRC(autosize=True, autosize_threshhold=0.00001)
from mimircache.cache.LRU import LRU
from mimircache.cacheReader.plainReader import plainCacheReader
from mimircache.cacheReader.csvReader import csvCacheReader
from mimircache.cacheReader.vscsiReader import vscsiCacheReader
from mimircache.profiler.basicLRUProfiler import basicLRUProfiler
from mimircache.profiler.pardaProfiler import pardaProfiler
from mimircache.profiler.pardaProfiler import parda_mode
# first step: construct a reader for reading any kind of trace
# this one is the most basic one, each line is a label/tag
reader1 = plainCacheReader("../data/parda.trace")
# this one reads csv file and choose one column as label/tag
reader2 = csvCacheReader("../data/trace_CloudPhysics_txt", column=4)
# this one reads binary cloudphysics trace file
reader3 = vscsiCacheReader("../data/trace_CloudPhysics_bin")
# reader is also a generator, for readers you can do the following thing:
# read one trace element at one time:
reader1.read_one_element()
# for loop:
# for element in reader1:
# pass
# reset, after read some elements, you want to go back
reader1.reset()
# second step: construct a profiler for analyze
# basic mattson profiler (toooooo slow)
def open(self, file_path):
# assert os.path.exists(file_path), "data file does not exist"
self.reader = plainCacheReader(file_path)