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。

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。

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。

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。

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。