javajava实现线程池(附完整源码)
时间: 2023-07-08 16:43:32 浏览: 58
, 512, 7, 7] 0
Conv2d-116 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-117 [-1, 512, 7,下面是一个简单的Java实现线程池的示例代码:
```java
import java.util.LinkedList;
import java.util 7] 1,024
ReLU-118 [-1, 512, 7, 7] 0
Conv2d-119 [-1, 2048, 7, 7] 1,048,576.List;
public class ThreadPool {
private final int nThreads;
private final List<Worker> workers;
private final List<Runnable>
BatchNorm2d-120 [-1, 2048, 7, 7] 4,096
taskQueue;
public ThreadPool(int nThreads) {
this.nThreads = nThreads;
this.workers = new LinkedList<>();
this ReLU-121 [-1, 2048, 7, 7] 0
Bottleneck-122 [-.taskQueue = new LinkedList<>();
for (int i = 0; i < nThreads; i++) {
Worker worker = new Worker1, 2048, 7, 7] 0
Linear-123 [-1, 1000] ();
workers.add(worker);
worker.start();
}
}
public void execute(Runnable task) {
synchronized (taskQueue 2,049,000
================================================================
Total params: 25,557,032
Trainable params: 25,557,) {
taskQueue.add(task);
taskQueue.notify();
}
}
private class Worker extends Thread {
public void run032
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 342.76
Params size (MB): 97.47
Estimated Total Size (MB): 440.80
() {
while (true) {
Runnable task = null;
synchronized (taskQueue) {
while (taskQueue.isEmpty()) {
try {
taskQueue.wait();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
task =----------------------------------------------------------------
```
其中 `Total params` 表示网络参数的总数量,本例中为 25,557,032;`Estimated Total Size` 表示模型的总大小,本例中为 440.80 MB;`Forward/backward pass size taskQueue.remove(0);
}
try {
task.run();
} catch (RuntimeException e) {
e.printStackTrace();
` 表示前向和反向传播时需要的内存大小,本例中为 342.76 MB;`Params }
}
}
}
}
```
在这个例子中,我们使用了一个 List 来存储线程池中 size` 表示模型参数的大小,本例中为 97.47 MB。FLOPs 可以通过 `torchprofile的所有工作线程。每个工作线程是一个 Worker 对象,它是一个内部类,继承了` 库来计算,具体实现略有复杂,这里不再赘述。