What is the translation of " PARALLELISM " in Chinese?

• 并行性
• 并行
• parallelism
• 的并行性
• 平行
• 并行度
• 并行是
• 的并行化
• 平行度:
• 的并行在
• 并行约

Examples of using Parallelism in English and their translations into Chinese

Dynamic Parallelism.
动态并行GPU.

Parallelism of< 10 arc seconds.
平行度:优于10arcseconds.

Internal Parallelism in SSDs.
SSD中的内部并行.

Map reduce and data parallelism.
MapReduce和数据并行.

Parallelism to a non-negative integer.
Parallelism设置为非负整数。

Hyper- Q Dynamic Parallelism.
Hyper-Q、动态并行.

Parallelism: better than 20 arc seconds.
平行度:优于20arcseconds.

GPUs are uniquely suited for data parallelism.
CUDA非常适合数据并行.

Parallelism: better than 20 arc seconds.
平行度:小于20arcseconds.

Concurrency and Parallelism- A technical point of view.
并发与并行-一种技术观点.

Parallelism does not require two tasks to exist.
并行不需要两个任务的存在。

The smallest unit of parallelism is the partition of a topic.
最小的并行单位是主题的分区。

Parallelism and concurrency need different tools→.
并行和并发需要不同的工具.

Generally, it can be processed according to the parallelism requirement.
一般来说，可以根据并行性的要求进行处理。

Parallelism configuration property to change the default.
Parallelism配置属性来改变默认值。

Julia does not impose any particular style of parallelism on the user.
Julia不会在用户上强加任何特殊的并行性。

Parallelism will not always result in faster execution times.
并行性并不总是会导致更快的执行时间。

For more information, see max degree of parallelism Option.
有关更多信息，请参见maxdegreeofparallelism选项。

Parallelism, or leave it unset and it will default to 1.
Parallelism，或者不设置它，它默认为1。

ISO/IEC DTS 19570:xxxx Parallelism TS part 2 Early development.
ISO/IECDTS19570:xxxx并行TS部分2早期开发。

Parallelism will not always result in faster execution times.
因此，并行程序并不总是会导致更快的执行时间。

For more information, see the topic max degree of parallelism Option.
有关更多信息，请参见maxdegreeofparallelism选项。

True parallelism on a single-core machine is impossible to achieve.
真正的并行在单核机器上是不可能的。

Or it will speed up quickly, through massive parallelism and hardware improvements.
要么就将通过大规模并行和硬件改进而获得快速提速。

While parallelism is always explicit, it need not be intrusive.
尽管并行是显式的，但它并不需要成为侵入式的。

True parallelism on a single-core machine is impossible to achieve.
真正的并行在单核机器上是不可能实现的。

When the parallelism changes, we will reassign buckets to tasks.
当并行性改变时，我们将重新分配任务的bucket。

Parallelism on the other hand arise when the execution can be truly simultaneous.
并行在另一方面是在真正地同时执行时出现。

Both task parallelism and data parallelism can be achieved using OpenMP in this way.
通过这种方式，使用OpenMP可以实现任务并行和数据并行。