mpi 中的 darray 和 subarray 有什么区别？
我有一个用于并行编程类的并行 I/O 项目，并且我必须实现派生数据类型。我不太清楚darray和subarray之间的区别。 darray 是否可以从动态分配的数组派生？主要区别是什么？
I have a parallel I/O project for parallel programming class, and I have to implement derived datatypes. I didn't clearly understand the difference between darray and subarray. Can darray be derived from dynamically allocated arrays or not? And what is the main difference?
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子数组可让您描述较大多维数组的单个块/切片。如果每个 MPI 任务都有大型全局数组的单个切片/块（或者如果您在任务之间通信本地数组块），那么 MPI_Type_create_subarray 就是最佳选择；语法非常简单。对于求解常规网格上的偏微分方程之类的问题，这种分布非常常见 - 每个处理器都有自己的全局网格块，其中尽可能多的网格单元位于本地。在 MPI-IO 的情况下，每个 MPI 任务将创建一个与其全局数组的一部分相对应的子数组，并将其用作视图，将其部分域读入/写出到包含所有数据的文件。
MPI_Type_create_darray 允许比单块每个更复杂的分布式数组模式。对于分布式线性代数计算，逐行分布一些矩阵可能是有意义的 - 例如，如果有 5 个 mpi 任务，任务 0 获取第 0、5、10 行...，任务 1 获取第 1、6 行， 11，等等。其他矩阵可能按列分布；或者您可以将它们分布在行、列或两者的块中。这些数据分布与不幸的 HPF 中提供的数据分布相同，它允许您定义数据- 以这种方式逐个数组地并行布局数组。
我自己使用 MPI_Type_create_darray 的唯一方法，实际上也是我见过它使用的唯一方法，是创建一个大型矩阵的 MPI 文件视图，以将数据分布在 块循环 方式，以便可以读取文件，然后使用 scalapack 对分布式矩阵进行并行线性代数运算。
Subarray lets you describe a single block/slice of a larger multidimensional array. If every MPI task has a single slice/block of a large global array, (or if you are communicating chunks of local arrays between tasks) then MPI_Type_create_subarray is the way to go; the syntax is very straightforward. For solving things like PDEs on regular meshes, this distribution is very common - each processor has it's own chunk of the global grid, with as many of its grid cells local as possible. In the case of MPI-IO, each MPI task would create a subarray corresponding to it's piece of the global array, and use that as it's view to read in / write out its part of the domain to the file containing all of the data.
MPI_Type_create_darray allows more complex distributed array patterns than single-chunk-each. For distributed linear algebra computations, it might make sense to distribute some matrices row-by-row -- say, if there's 5 mpi tasks, task 0 gets row 0, 5, 10... and task 1 gets row 1, 6, 11, and so on. Other matrices might get distributed by columns; or you could distribute them in blocks of rows, columns, or both. These data distributions are the same as were available in the ill-fated HPF, which let you define data-parallel layouts of arrays in this way, on an array-by-array basis.
The only way I've ever used MPI_Type_create_darray myself, and indeed the only way I've ever seen it used, is to create an MPI file view of a large matrix to distribute the data in a block-cyclic fashion, so that one can read the file in and then use scalapack to do parallel linear algebra operations on the distributed matrix.