GPU加速与非加速的深度学习张量计算对比Demo，使用PyTorch展示关键差异

import torch

import time

创建大型随机张量 (10000x10000)

tensor_size = 10000

x_cpu = torch.randn(tensor_size, tensor_size)

x_gpu = x_cpu.cuda() # 转移到GPU

CPU矩阵乘法

start = time.time()

result_cpu = torch.mm(x_cpu, x_cpu.t())

cpu_time = time.time() - start

GPU矩阵乘法

torch.cuda.synchronize() # 确保GPU计时准确

start = time.time()

result_gpu = torch.mm(x_gpu, x_gpu.t())

torch.cuda.synchronize()

gpu_time = time.time() - start

print(f"CPU计算时间: {cpu_time:.4f}秒")

print(f"GPU计算时间: {gpu_time:.4f}秒")

print(f"加速比: {cpu_time/gpu_time:.1f}倍")