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f0d6538 | 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 | import argparse
import torch
import time
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=int, required=True)
parser.add_argument('--time', type=int, required=True)
parser.add_argument('--gpu_mem', type=int, default=2, help='GB')
parser.add_argument('--usage', type=int, default=50, help='soft gpu usage')
args = parser.parse_args()
device = f'cuda:{args.device}'
torch.cuda.set_device(device)
# ===============================
# 1. 显存占位（只占，不频繁访问）
# ===============================
num_floats = args.gpu_mem * (1 << 30) // 4
_gpu_mem_holder = torch.empty(num_floats, device=device)
# ===============================
# 2. 小 tensor：温和 GPU kernel
# ===============================
# 小、cache-friendly、不吃带宽
x = torch.rand(512, 512, device=device)
start_time = time.time()
while True:
# -------------------------------
# GPU soft burn
# -------------------------------
for _ in range(args.usage):
# 比 sin 更温和，不用特殊函数单元
x = x * 1.0001 + 0.0001
# 强制让调度器看到 GPU 在干活
torch.cuda.synchronize()
# -------------------------------
# 防止 stdout idle（部分平台需要）
# -------------------------------
# if int(time.time() - start_time) % 60 == 0:
# print(f"[GPU {args.device}] alive", flush=True)
if args.time > 0 and time.time() - start_time > args.time:
break
if __name__ == '__main__':
main() |