【玩转GPU】GPU云服务器的功能与用途详解

```python
# 导入PyTorch等深度学习框架
import torch
import torch.nn as nn

python
# 定义神经网络模型
class MLP(nn.Module): 
    def __init__(self, input_size, hidden_size, output_size):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 准备训练数据    
X_train = torch.randn(5000, 28*28) 
y_train = torch.randn(5000, 10)

# 创建模型和优化器
model = MLP(28*28, 500, 10)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
loss_fn = nn.MSELoss()

# 模型训练
for epoch in range(100):
    optimizer.zero_grad()
    
    # 前向传播获得预测
    pred = model(X_train)
    
    # 计算loss
    loss = loss_fn(pred, y_train) 
    
    # 反向传播
    loss.backward()
    
    # 参数更新
    optimizer.step()

c++
// CUDA加速N体仿真模型

#include <cuda_runtime.h>

#define N 1024
#define threadsPerBlock 256 

// 初始化位 运动
__global__ void bodyForce(float* pos, float* vel) {
  
  int i = threadIdx.x + blockIdx.x * blockDim.x;
  
  float Fx = 0.f, Fy = 0.f; 
  
  for (int j = 0; j < N; j++) {
    // 计算粒子间力
    Fx += G * pos[i].x * pos[j].x / r*r;
    Fy += G * pos[i].y * pos[j].y / r*r;
  }
  
  vel[i].x += dt * Fx;
  vel[i].y += dt * Fy;
}

int main() {

  float* pos, *vel;
  cudaMallocManaged(&pos, N*sizeof(float2));
  cudaMallocManaged(&vel, N*sizeof(float2));

  // 执行N体仿真 
  for (int i = 0; i < steps; i++) {
    
    // 初始化位 运动
    bodyForce<<<(N+255)/256, 256>>>(pos, vel); 

    // 更新位
    integrate<<<gird, threads>>>(pos, vel);

  }

  cudaDeviceSynchronize();
  cudaFree(pos); cudaFree(vel);

}

python
# 使用Blender Python API进行GPU渲染

import bpy

# 切换Cycles渲染引擎
bpy.context.scene.render.engine = 'CYCLES' 

# 创建材质球
mat = bpy.data.materials.new(name="Material") 
mat.use_nodes = True
bsdf = mat.node_tree.nodes["Principled BSDF"]

#  UV球体对象
bpy.ops.mesh.primitive_uv_sphere_add()
obj = bpy.context.active_object
obj.data.materials.append(mat)

# 设置渲染参数
bpy.context.scene.cycles.device = 'GPU'
bpy.data.scenes['Scene'].render.tile_x = 512
bpy.data.scenes['Scene'].render.tile_y = 512

# GPU运算渲染
bpy.ops.render.render()  

python
# Ethereum Ethash挖矿代码 

from web3 import Web3, HTTPProvider
from web3.contract import ConciseContract

RPC_URL = 'https://mainnet.infura.io/v3/xxx' 
web3 = Web3(HTTPProvider(RPC_URL))

# 生成EthashDAG数据
epoch_number = web3.eth.getBlock('latest').number // 30000
EthashMiner.build_cache(epoch_number) 

miner = EthashMiner(web3)

for i in range(10):
  # 挖矿 
  miner.mine(1)

c#
// Unity游戏引擎发送RTMP流

using UnityEngine;
using UnityEngine.UI;

public class Streamer : MonoBehaviour {

  WebCamDevice[] devices;
  
  void Start() {
    devices = WebCamTexture.devices;  

    // 初始化WebCamTexture
    m_texture = new WebCamTexture(devices[0].name);

    // 播放游戏视角画面
    m_texture.Play(); 
  }

  void Update() {
    // 发送RTMP流
    m_texture.SendWebCamFrameToRTMPServer("rtmp://xxx"); 
  }
}