[Quy Anh] «Elliot» Nguyen
160 lines
3.3 KiB
Python
160 lines
3.3 KiB
Python
import matplotlib.pyplot as plt
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from matplotlib.ticker import FuncFormatter
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import numpy as np
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ram_beta = 1.91
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l1d_l2_beta = 64
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l3_beta = 32
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pi_scalar = 6.0
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pi_vector = 48.0
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I_vals = np.logspace(-50, 8, 500, base=2)
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plt.figure(figsize=(10, 7))
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plt.gca().set_facecolor('lightgrey')
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plt.plot(I_vals, ram_beta * I_vals, 'k-', label='_nolegend_')
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plt.plot(I_vals, l1d_l2_beta * I_vals, 'k-', label='_nolegend_')
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plt.plot(I_vals, l3_beta * I_vals, 'k-', label='_nolegend_')
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ram_bound_x_pos = 0.25
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ram_bound_y_pos = ram_beta * ram_bound_x_pos + 0.175
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plt.text(
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ram_bound_x_pos,
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ram_bound_y_pos,
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f'Read β mem. ({ram_beta} bytes/cycle)',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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rotation=20.5,
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)
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l1d_l2_bound_x_pos = 0.25
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l1d_l2_bound_y_pos = l1d_l2_beta * l1d_l2_bound_x_pos + 3.5
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plt.text(
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l1d_l2_bound_x_pos,
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l1d_l2_bound_y_pos,
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f'Read β L1D/L2 ({l1d_l2_beta} bytes/cycle)',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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rotation=20.5,
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)
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l3_bound_x_pos = 0.375
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l3_bound_y_pos = l3_beta * l3_bound_x_pos + 3.5
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plt.text(
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l3_bound_x_pos,
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l3_bound_y_pos,
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f'Read β L3 ({l3_beta} bytes/cycle)',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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rotation=20.5,
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)
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plt.plot(I_vals, [pi_scalar] * len(I_vals), 'k-', label='_nolegend_')
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plt.text(
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85,
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pi_scalar + 1.5,
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f'Peak π seq. ({pi_scalar} flops/cycle)',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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)
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plt.plot(I_vals, [pi_vector] * len(I_vals), 'k-', label='_nolegend_')
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plt.text(
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85,
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pi_vector + 12.5,
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f'Peak π par. ({pi_vector} flops/cycle)',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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)
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def fraction_formatter(x, _pos):
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if x == 0:
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return '0'
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log2_x = np.log2(x)
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if abs(log2_x - round(log2_x)) < 1e-6:
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exponent = int(round(log2_x))
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if exponent < 0:
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denominator = 2**(-exponent)
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return f'{1/denominator:.2f}'
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elif exponent == 0:
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return '1'
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else:
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return str(int(x))
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return f'{x:.2f}'
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formatter = FuncFormatter(fraction_formatter)
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plt.xscale('log', base=2)
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plt.yscale('log', base=2)
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plt.gca().xaxis.set_major_formatter(formatter)
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plt.gca().yaxis.set_major_formatter(formatter)
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plt.xlabel('Operational Intensity [flops/byte]', fontsize=10)
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plt.title("""Roofline Plot for the Social Force Model
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CPU: AMD Ryzen AI 7 350 w/ Radeon 860M @ 2 GHz
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Compiler: g++ (Debian 15.2.0-16) 15.2.0
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Command: ./main 100 10000
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Performance [flops/cycle]""",
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loc='left',
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size=10)
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plt.scatter([53], [0.0936126],
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c='black',
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marker='o',
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edgecolors='black',
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label='baseline (-O0)')
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plt.text(
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53,
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0.0936126 - 0.03,
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f'0.09 flops/cycle',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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)
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plt.scatter([53], [0.627801],
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c='black',
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marker='s',
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edgecolors='black',
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label='baseline (-O3 -march=native)')
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plt.text(
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53,
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0.627801 - 0.2,
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f'0.63 flops/cycle',
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color='black',
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va='center',
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ha='center',
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fontsize=8,
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)
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plt.legend(loc='lower right',
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fontsize=11,
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frameon=True,
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facecolor='white',
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edgecolor='black')
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plt.grid(True, which="both", ls="-", color='white', alpha=0.8, linewidth=1)
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plt.xlim(0.05, 200)
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plt.ylim(0.0005, 100)
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plt.tight_layout()
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plt.show()
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