81 lines
1.7 KiB
Python
81 lines
1.7 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib
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from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
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matplotlib.style.use('ggplot')
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# Roofline Plot
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#log x
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i = 32
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xlbl = []
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while i > 1:
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xlbl.append("1/"+repr(i))
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i //= 2
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xlbl.append(repr(i)) #1
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i += 1
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while i<=64:
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xlbl.append(repr(i))
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i *= 2
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print(xlbl)
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# memory
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values = []
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bandwidth = 10.6
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peak = 86.4
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basepeak = 54.4
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ymem = []
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ypeak = []
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ybasepeak = []
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yeight = []
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ysix = []
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for i in np.arange(0,64,0.1):
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if bandwidth*i < peak:
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values.append(bandwidth*i)
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else:
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values.append(peak)
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i=1/32
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while i<=64:
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if i < 16 and i > 4:
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yeight.append(21.78)
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ysix.append(peak)
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else:
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yeight.append(None)
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ysix.append(None)
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if bandwidth*i < peak and bandwidth*i*2 < peak:
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ymem.append(bandwidth*i)
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ypeak.append(None)
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elif bandwidth*i < peak and bandwidth*i*2 > peak:
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ypeak.append(peak)
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ymem.append(bandwidth*i)
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else:
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ypeak.append(peak)
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ymem.append(None)
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i*=2
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#plot data
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#data = pd.Series(data=values, name='Peak Memory Bandwidth', index=np.arange(0,64,0.1))
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data = {'Peak Memory Bandwidth': pd.Series(ymem, index=xlbl), 'Peak Floating-Point Performance (Turbo)': pd.Series(ypeak, index=xlbl), 'Best 8 OI Kernel': pd.Series(yeight, index=xlbl), 'Best 1/16 OI Kernel': pd.Series(yeight, index=xlbl)}
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df = pd.DataFrame(data)
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ax = df.plot()
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ax.set_xlabel("Operational Itensity (FLOP/Byte)")
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ax.set_ylabel("Attainable GFLOP/s")
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ax.set_yscale('log', basey=2)
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ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))
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print(repr(data))
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plt.show()
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