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63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
import matplotlib.pyplot as plt
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from sklearn.linear_model import LinearRegression
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import numpy as np
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Y = [1100, 2150, 3200, 4250, 5300, 6350, 7400, 8450, 9500]
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YO1 = [796, 1546, 2296, 3046, 3796, 4546, 5296, 6046, 6796]
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YO3 = [764, 1514, 2264, 3014, 3764, 4514, 5264, 6014, 6764]
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Y_ARM = [912, 1736 , 2560 , 3352, 4216, 5304 , 5840, 6704, 7464]
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Y_ARMO3 = [173, 329, 480, 633, 789, 945, 1098, 1308, 1406]
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Y_gcc = [2296, 5119, 6715, 9078, 10830, 12541, 15041, 16780, 18883]
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Y_O3 = [165, 287, 380, 559, 900, 901, 776, 1122, 1574]
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f_RISCV1 = 50e6
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f_RISCV2 = 100e6
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f_ARM = 800e6
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f_PC = 2400e6
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X = [50, 100, 150, 200, 250, 300, 350, 400, 450]
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plt.scatter(X, Y, color="b", label="RISCV -O0")
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plt.scatter(X, YO1, color="b", marker="x", label="RISCV -O1")
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plt.scatter(X, YO3, color="b", marker="^", label="RISCV -O3")
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plt.scatter(X, Y_ARM, color="g", label="ARM -O0")
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plt.scatter(X, Y_ARMO3, color="g", marker="x",label="ARM -O3")
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plt.scatter(X, Y_gcc, color="r", label="desktop -O0 2,4Ghz")
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plt.scatter(X, Y_O3, color="r",marker="x", label="desktop -O3 2,4Ghz")
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plt.xlim([0, 500])
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plt.ylim([50, 20000])
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plt.legend()
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plt.title("Cycles d'exécution en fonction de n_max")
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plt.ylabel("Cycles")
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plt.xlabel("N_max")
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plt.show()
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plt.savefig("fibonacci_cycles.png")
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for i in range(9) :
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Y[i] = (Y[i]*1e9)/f_RISCV1
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YO1[i] = (YO1[i]*1e9)/f_RISCV1
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YO3[i] = (YO3[i]*1e9)/f_RISCV1
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Y_ARM[i] = (Y_ARM[i]*1e9)/f_ARM
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Y_ARMO3[i] = (Y_ARMO3[i]*1e9)/f_ARM
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Y_gcc[i] = (Y_gcc[i]*1e9)/f_PC
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Y_O3[i] = (Y_O3[i]*1e9)/f_PC
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plt.scatter(X, Y, color="b", label="RISCV -O0")
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plt.scatter(X, YO1, color="b", marker="x", label="RISCV -O1")
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plt.scatter(X, YO3, color="b", marker="^", label="RISCV -O3")
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plt.scatter(X, Y_ARM, color="g", label="ARM -O0")
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plt.scatter(X, Y_ARMO3, color="g", marker="x",label="ARM -O3")
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plt.scatter(X, Y_gcc, color="r", label="desktop -O0 2,4Ghz")
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plt.scatter(X, Y_O3, color="r",marker="x", label="desktop -O3 2,4Ghz")
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plt.yscale("log")
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plt.xlim([0, 500])
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plt.ylim([50, 500000])
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plt.legend()
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plt.title("Temps d'exécution en fonction de n_max")
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plt.ylabel("T (ns)")
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plt.xlabel("N_max")
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plt.show()
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plt.savefig("fibonacci_temps.png") |