File size: 2,555 Bytes
e3b6c9f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import matplotlib.pyplot as plt
import numpy as np
import os
# Set style
plt.style.use('ggplot')
# 1. LLM Reasoning Latency Plot
labels = ['Baseline (No LoRA)', 'Fine-Tuned (LoRA)']
x86_latency = [12.96, 24.36]
graviton_latency = [10.21, 25.03]
x = np.arange(len(labels))
width = 0.35
fig, ax = plt.subplots(figsize=(8, 6))
rects1 = ax.bar(x - width/2, x86_latency, width, label='Local x86 CPU', color='#E24A33')
rects2 = ax.bar(x + width/2, graviton_latency, width, label='AWS Graviton (ARM NEON)', color='#348ABD')
ax.set_ylabel('Latency per command (seconds)')
ax.set_title('LLM Reasoning Latency: x86 vs AWS Graviton')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
# Add values on top of bars
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height}s',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.tight_layout()
plt.savefig('latency_chart.png', dpi=300)
plt.close()
# 2. Vision Speed (FPS) Plot
x86_fps = [9.06, 9.42]
graviton_fps = [5.27, 6.67]
fig, ax = plt.subplots(figsize=(8, 6))
rects1 = ax.bar(x - width/2, x86_fps, width, label='Local x86 CPU', color='#E24A33')
rects2 = ax.bar(x + width/2, graviton_fps, width, label='AWS Graviton (ARM NEON)', color='#348ABD')
ax.set_ylabel('Frames Per Second (FPS)')
ax.set_title('YOLO Vision Speed: x86 vs AWS Graviton')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
autolabel(rects1)
autolabel(rects2)
plt.tight_layout()
plt.savefig('fps_chart.png', dpi=300)
plt.close()
# 3. CPU Hotspots Plot
hotspot_labels = [
'ggml_gemv_q4_K_8x8_q8_K',
'ggml_gemv_q5_K_8x8_q8_K',
'ggml_gemv_q6_K_8x8_q8_K',
'ggml_vec_dot_f16',
'ggml_gemm_q4_K_8x8_q8_K',
'jit_sve_conv_fwd_kernel'
]
hotspot_times = [10290, 8080, 5878, 5296, 3963, 1150]
fig, ax = plt.subplots(figsize=(10, 6))
y_pos = np.arange(len(hotspot_labels))
ax.barh(y_pos, hotspot_times, align='center', color='#988ED5')
ax.set_yticks(y_pos)
ax.set_yticklabels(hotspot_labels)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('CPU Time (ms)')
ax.set_title('Top Execution Hotspots on AWS Graviton (Arm Performix)')
for i, v in enumerate(hotspot_times):
ax.text(v + 100, i + 0.1, str(v), color='black', fontweight='bold')
plt.tight_layout()
plt.savefig('hotspots_chart.png', dpi=300)
plt.close()
print("Charts successfully generated.")
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