It continues until no COBOL program is active in the execution stack for any of the threads.
这种情况将一直持续下去，直到任何线程的执行堆栈中再也没有活动的COBOL程序。
The number of threads in the group should be no larger than the depth of the deepest lock hierarchy possible in the system.
组中线程的数量应该不大于系统中最深的锁层次结构的深度。
The same pinning of threads with larger packets seems to provide less advantage, but no obvious disadvantage compared to the original Suggestions from the WebSphere MQ Low Latency Messaging team.
与WebSphereMQLowLatencyMessaging团队的原始建议相比，具有较大数据包的线程的相同固定没有什么优势，但是同样也没有什么明显缺点。
Note that all the methods, even get(), need to be synchronized for the class to be thread-safe, to ensure that no updates are lost, and that all threads see the most recent value of the counter.
注意所有方法，甚至需要同步get()，使类成为线程安全的类，从而确保没有任何更新信息丢失，所有线程都看到计数器的最新值。
And remember, thanks to DWR's exploitation of Jetty Continuations, no threads are tied up on the server while the client is waiting for a new event to arrive.
请记住，正是由于DWR使用了JettyContinuations，当客户机等待新事件到来时不会占用服务器上面的线程。
Creating 100,000 weightless threads is no problem on an older Windows 98 Pentium II laptop with only 64 MB of memory (at one million threads, long disk churning occurs).
在一台比较老的只有64MB内存的Windows 98Pentiumii膝上型电脑上创建100,000个轻便线程是轻而易举的(如果达到了一百万个线程，就会出现长时间的磁盘“猛转”)。
To make better use of CPU time, we can create a pool of NHRTs to process the data. By maintaining a pool of running threads, we have no thread startup and shutdown overhead when running.
为更好地利用CPU时间，我们可以创建一个nhrt池来处理数据，通过维护一个运行中线程的池，在运行的时候就不存在任何线程启动和停止开销了。
Instead you get entirely different concurrent programming models that include no mention of threads or locks.
相反，它提供了与线程和锁无关的、完全不同的并发编程模型。
This shows that there were plenty of free connections available and no threads were waiting for a connection, which produces much faster response times.
这表明有大量空闲连接可用，且没有线程在等待连接，从而实现了更快的响应速度。
Ideally no two threads can try to modify the same piece of data at the same time, dirty reads are not possible, and deadlocks are automatically detected and handled.
理想情况下，多个线程不能同时访问同一块数据，脏读将不复存在，死锁则会被自动监测和处理。
The number of worker threads is three times your number of CPUs, but it's no less than 5 and no larger than 30.
工作线程的数量是CPU数量的三倍，但是不小于5和不大于30。
Because the TimerCallback function gets called on a worker thread, there are no skipped beats (assuming the availability of worker threads).
因为TimerCallback功能也是在工作者线程上被调用，没有一个跳动被跳过(假设有工作者线程可用)。
Finally, I walked though each tag in my list of tags, obtained the tweets, and printed them out. Because no threads were used, this code executes each search serially, as shown in Figure 1.
最后，我遍历了标记列表中的每个标记，获取了tweets并将它们打印出来。
Yes it is a bad practice when you put no upper bound on the number of threads (or generally resources).
是的，这是一个糟糕的练习当你把线程的数量没有上限(或一般参考资料)。
If the internal state cannot change, there is no chance for different threads to see inconsistent views of the data. Immutable types can be exported from your objects safely.
如果内部状态不能被改变，那么对于不同的线程来说，就没有机会看到这个数据的不同值。
There is currently no cure but there are several very promising threads of research underway that could lead to treatment, of which this is one.
目前还没有治愈，但有几个非常有希望的线程正在进行的研究有可能导致治疗，其中这是其中之一。
What if we had not only a pool of hardware resources hundreds wide that could handle thousands of threads in flight at a time with no software overhead?
如果我们已经不仅是一个游泳池的硬件资源数百广泛，可以处理数千个线程在飞行的时间，没有软件开销？
If a thread is in upgradeable mode, and there are no threads waiting to enter write mode, any number of other threads can enter read mode, even if there are threads waiting to enter upgradeable mode.
如果有一个线程处于可升级读模式，并且没有任何线程等待进入写模式，那么任意数量的线程可以进入读模式，即使有线程在等待进入可升级读模式。
In a multithreaded scenario using IOCPs, the control flow of a thread function is not straightforward, because there is no relationship between threads and communications.
在一个多线程的情况下使用IOCPs，控制流动的一个线程函数不是简单的，因为没有任何关系，线程和通信。
Is equivalent to a number of threads at the same time on a map data changes, there will be no memory conflict, what is the hidden problem?
难道就只有我一个觉得不会出错么？多线程修改数据只要不是同一个地方并不需要锁，楼主只要关键不变不增不减取出值修改应该是没问题的。
Is equivalent to a number of threads at the same time on a map data changes, there will be no memory conflict, what is the hidden problem?
难道就只有我一个觉得不会出错么？多线程修改数据只要不是同一个地方并不需要锁，楼主只要关键不变不增不减取出值修改应该是没问题的。
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