我可不是多处理器！
在多处理器的系统中，它们确实可以同时运行。
On a multiprocessor system, they really can run simultaneously.
如何知道自己在运行单处理器还是多处理器内核？
How do I know if I am running a uniprocessor kernel or a multiprocessor kernel?
subpool：加速多处理器系统的对象分配。
Subpool: Speeds up object allocation on systems with very large Numbers of processors.
如果你对触屏了无兴趣，那么考虑下多处理器内核吧。
If you're not interested in touchscreens, think multiple processor cores.
而且，load可以更好地利用多处理器机器上的并行性。
Also, load can better exploit parallelism on multiprocessor machines.
这是在多处理器主机中良好应用PHP的一种方法。
This is one way to use PHP to advantage on a multiprocessing host.
要使用多处理器系统的功能，通常需要使用多线程构造应用程序。
To exploit the power of multiprocessor systems, applications are generally structured using multiple threads.
构建大型的多处理器网络可能占用相当大的空间并消耗很多电量。
Building a large multiprocessor network can take considerable space and power.
由于这种多处理器架构的流行，很多供应商都生产了CMP设备。
Given the popularity of this multiprocessor architecture, many vendors produce CMP devices.
它被迅速地移植到多种平台，并得到更新，包括支持多处理器配置。
It was quickly ported to multiple platforms and was updated to include support for multiprocessor installations.
这个简单的示例可以确定被分析的转储是否是在多处理器机器上创建的。
This simple example determines if the dump being analyzed was created on a multiprocessor machine.
当达到那个极限时，可使用“越多越好”的方法应用多处理器。
When you reach that limit, you can use the more-is-better approach and apply multiple processors.
POWER4 是首款 64位对称多处理器，于 2001 年发布。
The POWER4 architecture was the first 64-bit symmetric multiprocessor, released in 2001.
在带有三个或更多处理器的系统上，程序运行至完成并生成一个结果。
The program runs to completion and produces a result on a system with three or more processors.
不幸的是，多处理器的性能并不与单个处理器性能的总和成线性比例。
Unfortunately, performance doesn't scale linearly with the aggregate performance of the individual processors.
这些新收集器是为了解决在多处理器系统中垃圾收集器成为伸缩性瓶颈这一问题的。
These new collectors address the problem of the garbage collector being a scalability bottleneck on multiprocessor systems.
第二类并行问题是如何设计程序，从而有效地使用同时可用的资源，例如多处理器。
The second type of concurrency problem is how to design programs to effectively use resources that are available concurrently, such as multiple processors.
同样的过滤器可以在不同的GPU和CPU架构上高效运行，包括多核与多处理器系统。
Allows the same filter to run efficiently on different GPU and CPU architectures, including multi-core and multiprocessor systems.
考虑一个例子，由于工作负载激增，报表分区和事务分区都请求更多处理器资源。
Consider an example where the both the Report and Transaction servers require more processor power because of peaking workloads.
另外，一个服务器的多个克隆模型也能运行与一台机子上，充分利用了多处理器的结构特点。
In addition, multiple clones of a single server can run on a single machine, taking advantage of multiprocessor architectures.
在多处理器系统中，Linux试图模拟分派到可用处理器的单个全局RT线程队列的行为。
On multiprocessor systems, Linux tries to emulate the behavior of a single global queue of RT threads that are dispatched to available processors.
考虑一下如下事实：今天的多核、多处理器系统与昨日的单核的庞然大物相较而言已是相去甚远。
Consider this: Today's multicore, multiprocessor systems are a far cry from yesterday's single-core behemoths.
它会把数据分发到多个输出中，从而达到提升多核和多处理器服务器环境下性能的目的。
It's intended to improve performance in multi-core and multi-processor server environments by distributing data to multiple outputs.
十五年前，多处理器系统是高度专用系统，要花费数十万美元(大多数具有两个到四个处理器)。
Fifteen years ago, multiprocessor systems were highly specialized systems costing hundreds of thousands of dollars (and most of them had two to four processors).
假定php页面需要计算两只股票价格，可能还需要将两者相比较，并且底层主机刚好是多处理器。
Suppose a PHP page needs to calculate two stock prices, perhaps to compare them, and the underlying host happens to be a multiprocessor.
此外，由极快的主板架构支持的多处理器架构的出现，似乎使得分布式文件系统问题变得不太重要。
In addition, the appearance of multiple-processor architectures supported by extremely fast motherboard architectures seems to make distributed file system issues a lesser priority.
如果此程序在单处理器或卸载的多处理器上运行，则每个线程打印的for循环迭代计数大致相同。
If this program is run on a uniprocessor or unloaded multiprocessor system, each thread prints out close to the same for loop iteration count. In one run, the program printed.
如果此程序在单处理器或卸载的多处理器上运行，则每个线程打印的for循环迭代计数大致相同。
If this program is run on a uniprocessor or unloaded multiprocessor system, each thread prints out close to the same for loop iteration count. In one run, the program printed.
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