我写过了Eratosthenes的筛子,它应该是平行的,但事实并非如此。当我增加线程数时,计算时间不会越来越少。有什么想法吗?
主要课程
import java.util.Date;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class ConcurrentTest {
public static void main(String[] args) throws InterruptedException {
Sieve task = new Sieve();
int x = 1000000;
int threads = 4;
task.setArray(x);
Long beg = new Date().getTime();
ExecutorService exec = Executors.newCachedThreadPool();
for (int i = 0; i < threads; i++) {
exec.execute(task);
}
exec.shutdown();
Long time = 0L;
// Main thread is waiting until all threads are terminated
// ( it means that computing is done)
while (true)
if (exec.isTerminated()) {
time = new Date().getTime() - beg;
break;
}
System.out.println("Time is " + time);
}
}
Sieve class
import java.util.concurrent.ConcurrentHashMap;
public class Sieve implements Runnable {
private ConcurrentHashMap<Integer, Boolean> array =
new ConcurrentHashMap<Integer, Boolean>();
private int x;
public void run() {
while(true){
// I am getting synchronized number to check if it's prime
int n = getCounter();
// If no more numbers to check, stop loop
if( n == -1)
break;
// If HashMap contains number, we can further
if(!array.containsKey(n))continue;
for (int i = 2 * n; i <= x; i += n) {
// Compound numbers are removed from HashMap, Eg. 6, 12 and much more.
array.remove(i);
}
}
}
private synchronized int getCounter(){
if( counter < x)
return counter++;
else return -1;
}
public void setArray(int x) {
this.x = x;
for (int i = 2; i <= x; i++)
array.put(i, false);
}
}
我用不同数量的线程进行了一些测试。结果如下:
Nr of threads 1 Time is 1850, 1795, 1825
Nr of threads 2 Time is 1845, 1836, 1814
Nr of threads 3 Time is 1767, 1820, 1756
Nr of threads 4 Time is 1732, 1840, 2083
Nr of threads 5 Time is 1791, 1795, 1803
Nr of threads 6 Time is 1825, 1728, 1707
Nr of threads 7 Time is 1754, 1729, 1686
Nr of threads 8 Time is 1760, 1717, 1817
Nr of threads 9 Time is 1721, 1699, 1673
Nr of threads 10 Time is 1661, 1722, 1718
答案 0 :(得分:4)
当我增加线程数时,计算时间就没有了 下
tl; dr :您的问题规模太小。如果将x增加到10000000,差异将变得更加明显。不过,它们不会是你所期待的。
我在八核机器上尝试了你的代码,稍作修改:
我尝试使用fixed thread pool,cached thread pool和fork join pool启动Sieve任务,并比较线程变量的不同值的结果。
我在x = 10000000的机器上看到以下结果(以毫秒为单位):
Thread count = 1 2 4 8 16 Fixed thread pool = 5451 3866 3639 3227 3120 Cached thread pool= 5434 3763 3709 3258 3078 Fork-join pool = 6732 3670 3735 3190 3102
这些结果向我们展示了从单个执行线程更改为两个线程的明显好处。但是,额外线程的好处会迅速下降。有一个有趣的高原,从2到4个线程,边际效益高达16个。
此外,您还可以看到不同的线程机制具有不同的初始开销:我没想到Fork-Join池的启动成本比其他机制要多得多。
因此,正如所写的那样,对于小而非平凡的问题集,你不应该期望通过两个线程获益。
如果您想增加其他线程的好处,那么您需要查看当前的实现。例如,当我使用AtomicInteger从同步的getCounter()切换到incrementAndGet()时,我消除了synchronized方法的开销。结果是我的所有四个线程数都下降了1000毫秒。