307
个编辑
更改
跳到导航
跳到搜索
第180行：
第180行： + 第187行：
第188行： − 并行处理器和分布式计算处理器由同时工作的多个处理器组成。不同模型之间的差异主要体现在处理器之间的信息传输方式上。通常情况下，在并行计算中，处理器之间的数据传输/数据传输非常快，而在并行计算中，数据传输通过网络完成，因此速度要慢得多。+ 第195行：
第196行： − 在处理器上进行计算所需的时间至少是单个处理器所需时间的商。事实上，这个理论上的最优界限永远不可能达到，因为有些子任务不能并行化，有些处理器可能不得不等待来自另一个处理器的结果。+ − 第203行：
第203行： − 因此，主要的复杂性问题是如何设计算法，使得计算时间乘以处理器数量的乘积尽可能接近在单个处理器上进行同一计算所需的时间。+ − −
→Parallel and distributed computation
===Parallel and distributed computation===
===Parallel and distributed computation===
并行和分布式计算
{{main|Parallel computing|Distributed computing}}
{{main|Parallel computing|Distributed computing}}
Parallel and distributed computing consist of splitting computation on several processors, which work simultaneously. The difference between the different model lies mainly in the way of transmitting information between processors. Typically, in parallel computing the data transmission between processors is very fast, while, in distributed computing, the data transmission is done through a network and is therefore much slower.
Parallel and distributed computing consist of splitting computation on several processors, which work simultaneously. The difference between the different model lies mainly in the way of transmitting information between processors. Typically, in parallel computing the data transmission between processors is very fast, while, in distributed computing, the data transmission is done through a network and is therefore much slower.
并行处理器和分布式计算处理器由同时工作的多个处理器组成。不同模型之间的差异主要体现在处理器之间的信息传输方式上。通常情况下，在并行计算中处理器之间的数据传输非常快，而在分布计算中，数据传输通过'''<font color="#ff8000">网络 network</font>'''完成，因此速度要慢得多。
The time needed for a computation on processors is at least the quotient by of the time needed by a single processor. In fact this theoretically optimal bound can never be reached, because some subtasks cannot be parallelized, and some processors may have to wait a result from another processor.
The time needed for a computation on processors is at least the quotient by of the time needed by a single processor. In fact this theoretically optimal bound can never be reached, because some subtasks cannot be parallelized, and some processors may have to wait a result from another processor.
在{{mvar|N}}个处理器上进行计算所需的时间至少是单个处理器所需时间的{{mvar|N}}的商。事实上，这个理论上的最优界限永远不可能达到，由于有些子任务不能并行化，部分处理器不得不先等待另一个处理器的结果。
The main complexity problem is thus to design algorithms such that the product of the computation time by the number of processors is as close as possible to the time needed for the same computation on a single processor.
The main complexity problem is thus to design algorithms such that the product of the computation time by the number of processors is as close as possible to the time needed for the same computation on a single processor.
因此，主要的复杂度问题是如何设计算法，使得计算时间与处理器数量的乘积尽可能接近在单个处理器上进行同一计算所需的时间。
===Quantum computing===
===Quantum computing===