使用Pytorch测试cuda设备的性能(单卡或多卡并行)
GPU:使用gpu-burn压测GPU
使用 torch.ones
测试CUDA设备。
import torch
import time
def cuda_benchmark(device_id, N=1000000):
# 指定要使用的显卡设备
torch.cuda.set_device(device_id)
# 创建输入数据
data = torch.ones(N).cuda()
# 启动CUDA操作,并记录执行时间
start_time = time.time()
for i in range(10000):
data += 1
torch.cuda.synchronize() # 等待CUDA操作执行完成
end_time = time.time()
# 将结果从GPU内存下载到主机内存
result = data.cpu().numpy()
# 打印Benchmark结果和执行时间
print(f"Benchmark结果:{result[:10]}")
print(f"执行时间:{end_time - start_time} 秒")
if __name__ == '__main__':
# 测试第一块显卡
device_id = 0
cuda_benchmark(device_id, 10000000)
使用自带的 CUDABenchmarkModel
测试CUDA设备。
import torch
import torch.nn as nn
import time
class CUDABenchmarkModel(nn.Module):
def __init__(self):
super(CUDABenchmarkModel, self).__init__()
self.fc = nn.Linear(10, 10).cuda()
def forward(self, x):
return self.fc(x)
def cuda_benchmark(device_ids, N=10000000):
# 创建模型
model = CUDABenchmarkModel()
model = nn.DataParallel(model, device_ids=device_ids)
# 创建输入数据
data = torch.ones(N, 10).cuda()
# 启动CUDA操作,并记录执行时间
start_time = time.time()
for i in range(10000):
output = model(data)
torch.cuda.synchronize() # 等待CUDA操作执行完成
end_time = time.time()
# 打印执行时间
print(f"执行时间:{end_time - start_time} 秒")
if __name__ == '__main__':
# 同时测试3块显卡
device_ids = [0, 1, 3]
cuda_benchmark(device_ids=device_ids)
使用nccl多进程的方式测试CUDA设备。
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
import time
def cuda_benchmark(device_id, N=10000000):
# 指定要使用的显卡设备
torch.cuda.set_device(device_id)
print(f"该GPU的核心数量为:{torch.cuda.get_device_properties(device_id).multi_processor_count}")
# 创建输入数据
data = torch.ones(N).cuda()
# 启动CUDA操作,并记录执行时间
start_time = time.time()
for i in range(10000):
data += 1
torch.cuda.synchronize() # 等待CUDA操作执行完成
end_time = time.time()
# 将结果从GPU内存下载到主机内存
result = data.cpu().numpy()
# 打印Benchmark结果和执行时间
print(f"Benchmark结果:{result[:10]}")
print(f"执行时间:{end_time - start_time} 秒")
def main(num):
# 初始化多进程
mp.spawn(run, args=(num,), nprocs=num)
def run(rank,world_size):
"""每个进程的入口函数"""
# 初始化进程组
dist.init_process_group("nccl", init_method="tcp://127.0.0.1:23456", rank=rank, world_size=world_size)
# 指定设备ID
device_id = rank
# 在多个GPU上并行执行操作
model = cuda_benchmark(device_id)
if __name__ == '__main__':
# 同时启用3个进程(一个进程对应一块显卡)
device_numbers = 3
main(device_numbers)
gpu_burn 代码仓库:https://github.com/wilicc/gpu-burn
git clone https://github.com/wilicc/gpu-burn
cd gpu-burn
make
yoyo@yoyo:~/360Downloads/gpu-burn$ make COMPUTE=8.6
g++ -O3 -Wno-unused-result -I/usr/local/cuda/include -std=c++11 -DIS_JETSON=false -c gpu_burn-drv.cpp
PATH="/opt/ros/kinetic/bin:/home/yoyo/360Downloads/cmake-3.21.1-linux-x86_64/bin:/home/yoyo/miniconda3/condabin:/home/yoyo/bin:/home/yoyo/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/local/cuda/bin::." /usr/local/cuda/bin/nvcc -I/usr/local/cuda/include -arch=compute_86 -ptx compare.cu -o compare.ptx
g++ -o gpu_burn gpu_burn-drv.o -O3 -lcuda -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib64/stubs -L/usr/local/cuda/lib -L/usr/local/cuda/lib/stubs -Wl,-rpath=/usr/local/cuda/lib64 -Wl,-rpath=/usr/local/cuda/lib -lcublas -lcudart
安装成功
编译成功后,将生成 gpu_burn
二进制文件。
yoyo@yoyo:~/360Downloads/gpu-burn$ ./gpu_burn -h
GPU Burn
Usage: gpu-burn [OPTIONS] [TIME]
-m X Use X MB of memory.
-m N% Use N% of the available GPU memory. Default is 90%
-d Use doubles
-tc Try to use Tensor cores
-l Lists all GPUs in the system
-i N Execute only on GPU N
-c FILE Use FILE as compare kernel. Default is compare.ptx
-stts T Set timeout threshold to T seconds for using SIGTERM to abort child processes before using SIGKILL. Default is 30
-h Show this help message
Examples:
gpu-burn -d 3600 # burns all GPUs with doubles for an hour
gpu-burn -m 50% # burns using 50% of the available GPU memory
gpu-burn -l # list GPUs
gpu-burn -i 2 # burns only GPU of index 2
测试单卡:
yoyo@yoyo:~/360Downloads/gpu-burn$ ./gpu_burn 120
Using compare file: compare.ptx
Burning for 120 seconds.
GPU 0: NVIDIA GeForce RTX 3060 (UUID: GPU-a460cb29-b0ea-f6a5-b261-590f0a23f79e)
Initialized device 0 with 12050 MB of memory (11457 MB available, using 10311 MB of it), using FLOATS
Results are 268435456 bytes each, thus performing 38 iterations
10.8% proc'd: 76 (7234 Gflop/s) errors: 0 temps: 57 C
Summary at: 2024年 09月 05日 星期四 22:53:05 CST
25.0% proc'd: 190 (7106 Gflop/s) errors: 0 temps: 62 C
Summary at: 2024年 09月 05日 星期四 22:53:22 CST
37.5% proc'd: 266 (7019 Gflop/s) errors: 0 temps: 65 C
Summary at: 2024年 09月 05日 星期四 22:53:37 CST
50.0% proc'd: 380 (7000 Gflop/s) errors: 0 temps: 67 C
Summary at: 2024年 09月 05日 星期四 22:53:52 CST
测试多卡:
export CUDA_VISIBLE_DEVICES=0,1
./gpu_burn 100