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import torch
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from datetime import datetime
import subprocess
import time
import psutil
import re
from collections import deque
import threading
class FP16Benchmark:
def __init__(self):
self.max_temp = 85
self.temperatures = deque(maxlen=200)
self.tflops_history = deque(maxlen=200)
self.load_level = deque(maxlen=200)
self.peak_tflops = 0
self.running = True
self.current_load = 1
self.matrix_size = 10240
self.num_operations = 1
self.num_streams = 1
self.fig, (self.ax1, self.ax2, self.ax3) = plt.subplots(3, 1, figsize=(14, 10))
self.fig.suptitle('BENCHMARK FP16 (HALF PRECISION) - Radeon Pro VII', fontsize=16, weight='bold')
self.last_temp_check = time.time()
self.temp_rising_fast = False
def get_gpu_temp(self):
try:
result = subprocess.run(['sensors'], capture_output=True, text=True, timeout=0.5)
for line in result.stdout.split('\n'):
if 'edge:' in line.lower():
match = re.search(r'([+-]?\d+\.?\d*)\s*°C', line)
if match:
return float(match.group(1))
except:
return 0
return 0
def check_system_health(self):
try:
start = time.time()
cpu = psutil.cpu_percent(interval=0.05)
response = time.time() - start
if response > 0.4 or cpu > 95:
return False
return True
except:
return False
def calculate_tflops(self, matrix_size, elapsed_time, num_ops, num_streams):
operations = 2 * (matrix_size ** 3) * num_ops * num_streams
return (operations / elapsed_time) / 1e12
def increase_load(self):
if self.current_load < 10:
self.current_load += 1
if self.current_load >= 2 and self.num_streams < 4:
self.num_streams += 1
if self.current_load >= 4 and self.num_operations < 30:
self.num_operations += 5
if self.current_load >= 6 and self.matrix_size < 16384:
self.matrix_size = min(self.matrix_size + 1024, 16384)
def decrease_load(self):
if self.current_load > 1:
self.current_load -= 1
if self.matrix_size > 8192:
self.matrix_size = max(self.matrix_size - 512, 8192)
if self.num_operations > 5:
self.num_operations = max(self.num_operations - 5, 1)
def stress_gpu(self):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type == 'cpu':
print("❌ ERRO: GPU não detectada!")
self.running = False
return
props = torch.cuda.get_device_properties(0)
print(f"🎯 GPU: {torch.cuda.get_device_name(0)}")
print(f"💾 VRAM: {props.total_memory / 1e9:.1f} GB")
print(f"🔥 Modo: FP16 (Half Precision)")
print(f"📊 TFLOPS Teórico FP16: ~26.88")
print(f"⚠️ Limite de temperatura: {self.max_temp}°C\n")
streams = [torch.cuda.Stream() for _ in range(4)]
last_temp = 0
stable_cycles = 0
while self.running:
current_time = time.time()
if current_time - self.last_temp_check > 0.05:
temp = self.get_gpu_temp()
self.last_temp_check = current_time
if len(self.temperatures) > 0:
temp_delta = temp - last_temp
if temp_delta > 2:
self.temp_rising_fast = True
else:
self.temp_rising_fast = False
if temp >= self.max_temp:
print(f"\n🚨 TEMPERATURA: {temp}°C - ABORTANDO!")
self.running = False
break
if temp >= self.max_temp - 3:
print(f"\n⚠️ ALERTA! Temp: {temp}°C - Reduzindo...")
self.decrease_load()
self.decrease_load()
if self.temp_rising_fast and temp > 75:
self.decrease_load()
last_temp = temp
else:
temp = last_temp
if not self.check_system_health():
print(f"\n🚨 SISTEMA INSTÁVEL!")
self.running = False
break
try:
torch.cuda.synchronize()
start = time.time()
for i in range(self.num_streams):
with torch.cuda.stream(streams[i]):
# FP16 - HALF PRECISION
a = torch.randn(self.matrix_size, self.matrix_size, device=device, dtype=torch.float16)
b = torch.randn(self.matrix_size, self.matrix_size, device=device, dtype=torch.float16)
for _ in range(self.num_operations):
c = torch.mm(a, b)
a = b
b = c
torch.cuda.synchronize()
elapsed = time.time() - start
tflops = self.calculate_tflops(self.matrix_size, elapsed,
self.num_operations, self.num_streams)
self.temperatures.append(temp)
self.tflops_history.append(tflops)
self.load_level.append(self.current_load)
if tflops > self.peak_tflops:
self.peak_tflops = tflops
print(f"TFLOPS: {tflops:6.2f} | Temp: {temp:5.1f}°C | Load: {self.current_load}/10 | "
f"Matrix: {self.matrix_size} | Ops: {self.num_operations} | Peak: {self.peak_tflops:.2f}", end='\r')
if temp < 75 and stable_cycles > 10:
self.increase_load()
stable_cycles = 0
elif temp < 80:
stable_cycles += 1
else:
stable_cycles = 0
time.sleep(0.02)
except RuntimeError as e:
if "out of memory" in str(e):
print(f"\n⚠️ VRAM cheia - Reduzindo...")
self.decrease_load()
torch.cuda.empty_cache()
else:
print(f"\n🚨 ERRO: {e}")
self.running = False
break
except Exception as e:
print(f"\n🚨 ERRO: {e}")
self.running = False
break
def update_plot(self, frame):
if len(self.tflops_history) == 0:
return
self.ax1.clear()
self.ax2.clear()
self.ax3.clear()
if len(self.tflops_history) > 0:
self.ax1.plot(list(self.tflops_history), 'b-', linewidth=2.5, label='TFLOPS FP16')
self.ax1.axhline(y=self.peak_tflops, color='g', linestyle='--', linewidth=2,
label=f'Peak: {self.peak_tflops:.2f}')
self.ax1.axhline(y=26.88, color='orange', linestyle=':', linewidth=2,
label='Teórico: 26.88')
self.ax1.set_ylabel('TFLOPS (FP16)', fontsize=12, weight='bold')
self.ax1.set_title('Performance FP16 (Half Precision)', fontsize=12, weight='bold')
self.ax1.legend(loc='upper left')
self.ax1.grid(True, alpha=0.3)
self.ax1.set_ylim(0, 30)
if len(self.temperatures) > 0:
temps = list(self.temperatures)
self.ax2.plot(temps, 'r-', linewidth=2.5)
self.ax2.axhline(y=self.max_temp, color='red', linestyle='--', linewidth=2,
label=f'LIMITE: {self.max_temp}°C')
self.ax2.fill_between(range(len(temps)), temps, self.max_temp,
where=[t >= self.max_temp - 5 for t in temps],
alpha=0.3, color='orange')
self.ax2.set_ylabel('Temperatura (°C)', fontsize=12, weight='bold')
self.ax2.set_title('Monitoramento Térmico', fontsize=12, weight='bold')
self.ax2.legend(loc='upper left')
self.ax2.grid(True, alpha=0.3)
self.ax2.set_ylim(30, 95)
if len(self.load_level) > 0:
loads = list(self.load_level)
self.ax3.plot(loads, 'purple', linewidth=2.5)
self.ax3.fill_between(range(len(loads)), loads, alpha=0.3, color='purple')
self.ax3.set_ylabel('Carga (1-10)', fontsize=12, weight='bold')
self.ax3.set_xlabel('Amostras', fontsize=12, weight='bold')
self.ax3.set_title('Intensidade', fontsize=12, weight='bold')
self.ax3.grid(True, alpha=0.3)
self.ax3.set_ylim(0, 11)
if not self.running and len(self.tflops_history) > 0:
efficiency = (self.peak_tflops / 26.88) * 100
self.ax1.text(0.5, 0.5,
f'🏆 PEAK: {self.peak_tflops:.2f} TFLOPS\n'
f'📊 Eficiência: {efficiency:.1f}%',
transform=self.ax1.transAxes, fontsize=20,
ha='center', va='center', color='darkgreen', weight='bold',
bbox=dict(boxstyle='round,pad=1', facecolor='lightgreen', alpha=0.9))
def run(self):
stress_thread = threading.Thread(target=self.stress_gpu)
stress_thread.daemon = True
stress_thread.start()
ani = animation.FuncAnimation(self.fig, self.update_plot,
interval=300, cache_frame_data=False)
plt.tight_layout()
plt.show()
stress_thread.join(timeout=2)
print(f"\n\n{'='*70}")
print(f"{'BENCHMARK FP16 - RESULTADO FINAL':^70}")
print(f"{'='*70}")
print(f"🏆 PEAK TFLOPS (FP16): {self.peak_tflops:.2f}")
print(f"📊 Teórico FP16: 26.88 TFLOPS")
print(f"📈 Eficiência: {(self.peak_tflops / 26.88) * 100:.1f}%")
print(f"🌡️ Temp Máxima: {max(self.temperatures) if self.temperatures else 0:.1f}°C")
print(f"🔥 Carga Máxima: {max(self.load_level) if self.load_level else 0}/10")
print(f"{'='*70}\n")
if __name__ == "__main__":
bench = FP16Benchmark()
bench.run()
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