Скачать или смотреть Mastering MPI4PY: How to Properly Scatter a Matrix Across Processes

Discover how to efficiently scatter a matrix using MPI4PY with simple steps and clear code examples to solve common issues in parallel processing.
---
This video is based on the question https://stackoverflow.com/q/65082585/ asked by the user 'garbage_collector' ( https://stackoverflow.com/u/14498840/ ) and on the answer https://stackoverflow.com/a/65091119/ provided by the user 'Joe Todd' ( https://stackoverflow.com/u/2984058/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: MPI4PY: Scatter a matrix

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Mastering MPI4PY: How to Properly Scatter a Matrix Across Processes

When working with parallel computing using MPI and mpi4py, a common challenge is efficiently distributing data across multiple processes. If you're looking to scatter a matrix such that each process receives specific chunks of the data in a structured manner, you’re in the right place! In this post, we’ll dive into the problem faced and how to correctly implement the solution.

The Problem

Imagine you have a 4x4 matrix and two processes. Your goal is to scatter the matrix so that each process receives two columns, but not in the default, contiguous manner provided by Scatterv. Here's the matrix we are starting with:

[[See Video to Reveal this Text or Code Snippet]]

You need each process to get its columns like this:

Process 0: Columns 1 and 2

Process 1: Columns 3 and 4

However, the default output was:

[[See Video to Reveal this Text or Code Snippet]]

Instead, you wanted:

[[See Video to Reveal this Text or Code Snippet]]

Understanding the Solution

To achieve the desired output, we need to manipulate our data before using Scatterv. The problem arises because Scatterv treats data as a flat block of memory, ignoring the structured format of a matrix. Here’s how we can address this issue step by step:

Step 1: Data Preparation

First, we need to reshape our data into a 1D array that contains the columns in the desired order. We can do this by following these steps:

Split the Matrix: Use np.split() to break the matrix into sub-arrays along the desired axis (in this case, columns).

Flatten the Sub-arrays: Employ np.ravel() to convert these sub-arrays into a flat format.

Join the Arrays: Finally, concatenate these arrays back into a single 1D array.

Step 2: Code Implementation

Here's the refined version of the code that implements the above steps:

[[See Video to Reveal this Text or Code Snippet]]

Code Explanation

Imports: The necessary libraries are imported at the beginning.

Setup MPI: The communicator and process rank are defined.

Data Preparation: The root process (rank 0) prepares the matrix, splits, flattens, and concatenates it into a send_data array.

Scatter: Scatterv is used to distribute the data from send_data to recvbuf in each process.

Output: Each process prints its received buffer.

Putting It Together

By splitting, flattening, and then concatenating your matrix correctly, you can ensure that each process receives the intended columns. This approach effectively changes how the data is ordered and allows Scatterv to work as desired.

Conclusion

Using mpi4py to scatter matrices can be tricky, but with proper data manipulation, you can successfully distribute your matrices as needed. By following the structured approach outlined above, you can overcome the limitations imposed by the linear processing of Scatterv. Happy coding, and may your parallel computing endeavors be efficient and effective!