MPI 标准和 Map-Reduce 编程模型之间的比较？
因为我已经了解了各种并行范例标准（例如 OpenMP、MPI、OpenCL）的基础知识来编写并行编程。但我对 Map-Reduce 编程模型了解不多。
众所周知，各种流行公司都在遵循 Map-Reduce 编程模型来解决其庞大的数据密集型任务。 MPI 专为大规模并行计算机和工作站集群上的高性能计算而设计。
所以我的第一个困惑是.. 我可以使用 Map-Reduce 模型代替 MPI 标准吗？反之亦然吗？或者这取决于应用程序！
它们之间的确切区别是什么？
Which one is better and when?
As i have learned basics of various parallel paradigm standard such as OpenMP, MPI, OpenCL to write parallel programming. But i don't have much knowledge about Map-Reduce Programming model.
As it is well known that various popular companies are following the Map-Reduce programming model to solve their huge data intensive tasks. As well as MPI was designed for high performance computing on both massively parallel machines and on workstation clusters.
So my first confusion is ..
Can i use the Map-Reduce model instead of MPI standard or vice-versa? or it depends upon the applications!!
What is the exact difference between them?
Which one is better and when?
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您可以将 Map-Reduce 理解为 MPI 功能的子集，因为它有点类似于具有用户定义函数的 MPI 集体操作。因此，您可以使用 MPI 代替 Map-Reduce，但反之则不然，因为在 MPI 中您可以描述更多操作。 Map-Reduce 的主要优点似乎是集中于单一并行概念，从而减少了使用它时需要学习的接口。
You could understand Map-Reduce as a subset of MPI-functionality, as it kind of resembles MPIs collective operations with user-defined functions. Thus you can use MPI instead of Map-Reduce but not vice-versa, as in MPI you can describe many more operations. The main advantage of Map-Reduce seems to be this concentration on this single parallel concept, thereby reducing interfaces that you need to learn in order to use it.