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1·In the massive parallel distributed processing system, the load balancing strategies among its nods is one of the key problems.

在大规模并行分布处理系统中，各节点间的负载平衡调度是最常见的关键性问题之一。

2·And the answer is, unlike many, unlike commercially generated computers, the brain works through parallel processing, massively parallel distributed processing.

问题的答案是这样的，与出于商业目的而制造的计算机不同，大脑采用并行加工的方式处理信息，采用广泛分布的并行加工。

3·The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, natural intelligent systems, machine learning algorithms, and artificial neural networks.

这个领域 有很多术语，例如连接机制、并行分布处理、神经计算、自然 智能系统、机器学习算法和人工神经网络。

4·Input data is partitioned in such a way that it can be distributed among a cluster of machines for processing in parallel.

输入数据使用这样一种方法进行分区，即在并行处理的计算机集群中分区的方法。

5·Map refers to the process of chopping input into a smaller set of sub-problems for processing (where these sub-problems are distributed to parallel workers).

映射是指把大量输入处理成更小的子问题集(然后把这些子问题分发给并行的工作系统)。

6·In the initial example above, the key data was user profiles, which could be distributed (partitioned) across a set of machines for parallel processing.

在上例中，关键数据是用户配置文件，可以跨一组机器分布(分区)以进行并行处理。

7·You can think of it as distributed and large-scale cluster computing and as a form of network-distributed parallel processing.

您可以将其视为分布式大规模集群计算和某种形式的网络分布式并行处理。

8·But in a parallel supercomputer with a sparse, distributed memory, the distinction between memory and processing fades.

在采用稀疏分布式内存的超级计算机里，记忆与数据处理之间的差异消失了。

9·The two core components are the Hadoop Distributed File System for storing data and Hadoop MapReduce for writing parallel-processing jobs.

其中两个核心组件是用于存储数据的Hadoop Distributed File System (Hadoop分布式文件系统)和用于写入并行处理任务的Hadoop MapReduce。

10·It is usually prudent that the overhead of distributed and parallel processing should be taken if the resource utilization of the problem at hand is very high.

它通常是谨慎的分布式并行处理和应采取的手，如果在这个问题的资源利用率是非常高的开销。