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gemm op final analysis
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‎evaluation/measure_gemm.py

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import setup # noqa # isort:skip
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import keras
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from keras import layers

‎evaluation/results_gemm/10th.csv

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‎evaluation/results_gemm/6th.csv

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MNK,runtime,time_mean,time_std
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736,tensorflow,8.18517155,0.8955714304885909
81+
736,onnxruntime,3.4846302700000003,0.29393547805562775
82+
768,onnx2code-gemm-naive,114.22778595999999,8.203709334359631
83+
768,onnx2code-loop-tiling,14.643412720000002,0.770094425828583
84+
768,onnx2code-libxsmm,21.88221372,2.3626316578265025
85+
768,tensorflow,9.402519983333335,1.0571307250323347
86+
768,onnxruntime,3.9847046133333333,0.35216399742038346
87+
800,onnx2code-gemm-naive,102.52447922666666,4.841998191757737
88+
800,onnx2code-loop-tiling,16.721700436666666,0.7321350723210706
89+
800,onnx2code-libxsmm,22.56168299333333,1.5385892624864095
90+
800,tensorflow,10.610878073333334,1.2925829745715158
91+
800,onnxruntime,4.526122559999999,0.394388227185878
92+
832,onnx2code-gemm-naive,118.56218002333335,3.939953692180838
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832,onnx2code-loop-tiling,18.922631803333335,1.4425692216179269
94+
832,onnx2code-libxsmm,25.1037305,3.1527200230313914
95+
832,tensorflow,11.518043606666668,1.147024838134664
96+
832,onnxruntime,5.080414686666666,0.47586088191467807
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864,onnx2code-gemm-naive,130.09689659666668,5.189265473084857
98+
864,onnx2code-loop-tiling,20.914084600000002,0.9556391532755657
99+
864,onnx2code-libxsmm,29.258843553333328,1.9307758241843858
100+
864,tensorflow,13.143729653333333,2.0231548745389447
101+
864,onnxruntime,5.760477336666666,1.477134492618634
102+
896,onnx2code-gemm-naive,168.89975199666668,5.312927351664043
103+
896,onnx2code-loop-tiling,23.185644933333332,1.0045551482742974
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896,onnx2code-libxsmm,34.172522423333334,1.9982843256822413
105+
896,tensorflow,14.184290020000002,1.8862201389964413
106+
896,onnxruntime,6.194402336666667,0.5051867525439248
107+
928,onnx2code-gemm-naive,165.13455939666667,3.6329771619842033
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928,onnx2code-loop-tiling,25.830488803333335,1.6850606511951327
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928,onnx2code-libxsmm,37.66786822666667,3.800566116722251
110+
928,tensorflow,15.979504666666665,1.5224369501662662
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928,onnxruntime,6.822730663333334,0.8137015051617086
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960,onnx2code-gemm-naive,183.91971043000004,3.7863765477388824
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960,onnx2code-loop-tiling,27.901448323333334,1.140211974885468
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960,onnx2code-libxsmm,40.32194981999999,4.608950302465135
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960,tensorflow,17.55200400333333,3.1222178969760126
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960,onnxruntime,7.582337316666665,1.3798306560698488
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992,onnx2code-gemm-naive,202.18115277666666,4.9461951688977575
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992,onnx2code-loop-tiling,30.831881576666667,1.2410191207267294
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992,onnx2code-libxsmm,46.472281063333334,3.5206640553037913
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992,tensorflow,19.609973956666668,1.9594430432812078
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992,onnxruntime,8.354810469999999,1.224331820426381
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1024,onnx2code-gemm-naive,311.7934103333333,6.877924793102408
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1024,onnx2code-loop-tiling,34.00422953666667,1.65053201542282
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1024,onnx2code-libxsmm,56.044168510000006,4.313170511153236
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1024,tensorflow,21.53622228,3.0112313016520313
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1024,onnxruntime,9.185121386666667,1.5104097708390252
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1056,onnx2code-gemm-naive,254.14210583000002,6.641041915904256
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1056,onnx2code-loop-tiling,38.359650099999996,1.305478721310599
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1056,onnx2code-libxsmm,59.21067334000001,4.997676490874754
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1056,tensorflow,22.382056223333333,2.0500099528156066
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1056,onnxruntime,9.777039993333332,0.7107724979235902
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1088,onnx2code-gemm-naive,292.2926318666667,9.702008574620628
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1088,onnx2code-loop-tiling,42.027352369999996,1.749770538149051
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1088,onnx2code-libxsmm,67.41834954333333,4.8664865627514065
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1088,tensorflow,26.594176163333334,2.0905433452247073
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1088,onnxruntime,10.497412073333331,0.8809854735765934
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1120,onnx2code-gemm-naive,338.5233844933333,10.164223333108087
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1120,onnx2code-loop-tiling,45.16896703,2.3841331282466762
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1120,onnx2code-libxsmm,76.79927917333333,5.7278331098052515
140+
1120,tensorflow,28.83353773333333,2.563366119458253
141+
1120,onnxruntime,11.659171516666666,1.8744377435576556
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1152,onnx2code-gemm-naive,403.66976146333326,9.52804313250809
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1152,onnx2code-loop-tiling,49.305075323333334,3.73440690467644
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1152,onnx2code-libxsmm,86.43004374333333,6.511510521143248
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1152,tensorflow,30.32093784666667,2.2242678975026324
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1152,onnxruntime,12.255513253333332,0.9498209524135391
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1184,onnx2code-gemm-naive,416.8149896066667,16.73399445325064
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1184,onnx2code-loop-tiling,53.818254503333335,4.412354834290176
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1184,onnx2code-libxsmm,95.98643558,6.660626441719355
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1184,tensorflow,32.997734083333334,3.954762346023894
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1184,onnxruntime,13.61072438,0.9931182019533201
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1216,onnx2code-gemm-naive,449.9982161666666,11.71757127478388
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1216,onnx2code-loop-tiling,57.79504892,3.78510541217508
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1216,onnx2code-libxsmm,113.02410599,6.211236515543298
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1216,tensorflow,33.57050304333334,3.4069599306920897
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1216,onnxruntime,14.462370676666664,1.1351487701683944
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1248,onnx2code-gemm-naive,533.9181180966667,19.01682602485039
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1248,onnx2code-loop-tiling,61.468259516666656,2.3679392876943126
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1248,onnx2code-libxsmm,128.34663120999997,6.3470097444951215
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1248,tensorflow,36.713297913333335,2.8748869668129955
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1248,onnxruntime,15.35723634,1.0219832136925788

‎evaluation/results_gemm/analysis.Rmd

Lines changed: 1 addition & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ theme_set(theme(text=element_text(family="LM Roman 10")))
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```
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```{r}
14-
datos <- read.csv("C:/Users/mlomb/Desktop/onnx2code/evaluation/results_gemm.csv")
14+
datos <- read.csv("C:/Users/mlomb/Desktop/onnx2code/evaluation/results_gemm/10th.csv")
1515
datos$runtime <- factor(datos$runtime, labels=c("naïve", "libxsmm", "onnx2code", "onnxruntime", "tensorflow"))
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datosCON <- datos[datos$runtime == "onnx2code",]
@@ -34,14 +34,3 @@ ggsave("gemm.pdf", width = 8, height = 4, device=cairo_pdf)
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```
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37-
```{r}
38-
ggplot(datos, aes(x=as.factor((mr*nr)),y=time))+
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geom_boxplot() +
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ylab("Tiempo (ms)") +
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facet_grid(~gen)
42-
ggsave("mv_x_nu.pdf", width = 12, height = 4, device=cairo_pdf)
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```
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```{r}
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47-
```

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