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Updating.

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Daniel Lemire 2019-01-03 15:57:34 -05:00
parent 2e9a92588b
commit 58f7b55a09
6 changed files with 71 additions and 29 deletions
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scripts/data/nuc/gbps.pdf
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scripts/data/skylake/gbps.pdf
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37
scripts/modeldata/nuc/analysis.txt
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@ -1,24 +1,31 @@
loading modeltable.txt
integer_count float_count ... total_cycles ratio
count 14.000000 14.000000 ... 1.400000e+01 14.000000
mean 17156.642857 21488.071429 ... 2.910166e+06 2.517336
std 35520.191644 40466.023650 ... 3.465531e+06 1.043643
min 0.000000 0.000000 ... 9.816417e+04 1.209798
25% 13.000000 0.000000 ... 6.484984e+05 1.643587
50% 2108.000000 0.500000 ... 1.607978e+06 2.305758
75% 12044.500000 26800.250000 ... 3.766565e+06 3.436660
max 130225.000000 114950.000000 ... 1.227446e+07 4.129712
[8 rows x 18 columns]
chosenpredictors= ['integer_count', 'float_count', 'string_count', 'backslash_count', 'nonasciibyte_count', 'object_count', 'array_count', 'null_count', 'true_count', 'false_count', 'byte_count', 'structural_indexes_count']
target = stage1_cycle_count
0.55 cycles per byte_count
R2 = 0.9952005292028262
1.8 cycles per structural_indexes_count
0.62 cycles per byte_count
R2 = 0.9966890133532899
target = stage2_cycle_count
2 cycles per structural_indexes_count
0.11 cycles per byte_count
R2 = 0.9941606366930587
target = stage3_cycle_count
14 cycles per float_count
11 cycles per structural_indexes_count
0.31 cycles per byte_count
R2 = 0.9824350906350493
19 cycles per float_count
9.5 cycles per structural_indexes_count
0.33 cycles per byte_count
R2 = 0.9868882924152415
target = total_cycles
17 cycles per float_count
13 cycles per structural_indexes_count
0.96 cycles per byte_count
R2 = 0.991605569037089
19 cycles per float_count
11 cycles per structural_indexes_count
0.95 cycles per byte_count
R2 = 0.9923672903089373

16
scripts/modeldata/nuc/learn.py
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@ -1,7 +1,7 @@
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
@ -18,28 +18,30 @@ def displaycoefs(coef_name):
datafile = "modeltable.txt" ## from ./scripts/statisticalmodel.sh
predictors = ["integer_count", "float_count", "string_count", "backslash_count", "nonasciibyte_count", "object_count", "array_count", "null_count", "true_count", "false_count", "byte_count", "structural_indexes_count"]
targets = ["stage1_cycle_count", "stage1_instruction_count", "stage2_cycle_count", "stage2_instruction_count", "stage3_cycle_count", "stage3_instruction_count"]
targets = ["stage1_cycle_count", "stage1_instruction_count", "stage2_cycle_count", "stage2_instruction_count"]
print("loading", datafile)
dataset = pd.read_csv(datafile, delim_whitespace=True, skip_blank_lines=True, comment="#", header=None, names = predictors + targets)
dataset.columns = predictors + targets
dataset['total_cycles']=dataset['stage1_cycle_count']+dataset['stage2_cycle_count']+dataset['stage3_cycle_count']
dataset['total_cycles']=dataset['stage1_cycle_count']+dataset['stage2_cycle_count']
dataset['ratio']=dataset['total_cycles']/dataset['byte_count']
#print(dataset[['ratio']])
print(dataset.describe())
chosenpredictors = predictors #["integer_count", "float_count", "string_count", "backslash_count", "nonasciibyte_count", "byte_count", "structural_indexes_count"]
chosenpredictors = predictors
print("chosenpredictors=",chosenpredictors)
print()
chosentargets=["stage1_cycle_count", "stage2_cycle_count", "stage3_cycle_count","total_cycles"]
chosentargets=["stage1_cycle_count", "stage2_cycle_count","total_cycles"]
for t in chosentargets:
print("target = ", t)
howmany = 1 # we want at most one predictors
if(t.startswith("stage2")):
if(t.startswith("stage1")):
howmany = 2 # we allow for less
if(t.startswith("stage3")):
if(t.startswith("stage2")):
howmany = 3 # we allow for more
if(t.startswith("total")):
howmany = 3 # we allow for more

31
scripts/modeldata/skylake/analysis.txt
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@ -0,0 +1,31 @@
loading modeltable.txt
integer_count float_count ... total_cycles ratio
count 14.000000 14.000000 ... 1.400000e+01 14.000000
mean 17156.642857 21488.071429 ... 2.898374e+06 2.523610
std 35520.191644 40466.023650 ... 3.408262e+06 1.021949
min 0.000000 0.000000 ... 9.934419e+04 1.225950
25% 13.000000 0.000000 ... 6.545444e+05 1.650991
50% 2108.000000 0.500000 ... 1.611746e+06 2.369115
75% 12044.500000 26800.250000 ... 3.803468e+06 3.441740
max 130225.000000 114950.000000 ... 1.205456e+07 4.110868
[8 rows x 18 columns]
chosenpredictors= ['integer_count', 'float_count', 'string_count', 'backslash_count', 'nonasciibyte_count', 'object_count', 'array_count', 'null_count', 'true_count', 'false_count', 'byte_count', 'structural_indexes_count']
target = stage1_cycle_count
1.9 cycles per structural_indexes_count
0.63 cycles per byte_count
R2 = 0.9965695015271681
target = stage2_cycle_count
19 cycles per float_count
9 cycles per structural_indexes_count
0.36 cycles per byte_count
R2 = 0.9858116267470738
target = total_cycles
19 cycles per float_count
11 cycles per structural_indexes_count
0.98 cycles per byte_count
R2 = 0.9919590553913162

16
scripts/modeldata/skylake/learn.py
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@ -1,7 +1,7 @@
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
@ -18,28 +18,30 @@ def displaycoefs(coef_name):
datafile = "modeltable.txt" ## from ./scripts/statisticalmodel.sh
predictors = ["integer_count", "float_count", "string_count", "backslash_count", "nonasciibyte_count", "object_count", "array_count", "null_count", "true_count", "false_count", "byte_count", "structural_indexes_count"]
targets = ["stage1_cycle_count", "stage1_instruction_count", "stage2_cycle_count", "stage2_instruction_count", "stage3_cycle_count", "stage3_instruction_count"]
targets = ["stage1_cycle_count", "stage1_instruction_count", "stage2_cycle_count", "stage2_instruction_count"]
print("loading", datafile)
dataset = pd.read_csv(datafile, delim_whitespace=True, skip_blank_lines=True, comment="#", header=None, names = predictors + targets)
dataset.columns = predictors + targets
dataset['total_cycles']=dataset['stage1_cycle_count']+dataset['stage2_cycle_count']+dataset['stage3_cycle_count']
dataset['total_cycles']=dataset['stage1_cycle_count']+dataset['stage2_cycle_count']
dataset['ratio']=dataset['total_cycles']/dataset['byte_count']
#print(dataset[['ratio']])
print(dataset.describe())
chosenpredictors = predictors #["integer_count", "float_count", "string_count", "backslash_count", "nonasciibyte_count", "byte_count", "structural_indexes_count"]
chosenpredictors = predictors
print("chosenpredictors=",chosenpredictors)
print()
chosentargets=["stage1_cycle_count", "stage2_cycle_count", "stage3_cycle_count","total_cycles"]
chosentargets=["stage1_cycle_count", "stage2_cycle_count","total_cycles"]
for t in chosentargets:
print("target = ", t)
howmany = 1 # we want at most one predictors
if(t.startswith("stage2")):
if(t.startswith("stage1")):
howmany = 2 # we allow for less
if(t.startswith("stage3")):
if(t.startswith("stage2")):
howmany = 3 # we allow for more
if(t.startswith("total")):
howmany = 3 # we allow for more