How to Avoid Deadlocks when Using MPI_Send and MPI_Recv with Large Data in C++ скачать в хорошем качестве

How to Avoid Deadlocks when Using MPI_Send and MPI_Recv with Large Data in C++

Discover effective techniques to prevent deadlocks while sending and receiving large data arrays with MPI in C++. Learn best practices for efficient parallel computing. --- This video is based on the question https://stackoverflow.com/q/72827398/ asked by the user 'Mac cchiatooo' ( https://stackoverflow.com/u/16403652/ ) and on the answer https://stackoverflow.com/a/72828942/ provided by the user 'Victor Eijkhout' ( https://stackoverflow.com/u/2044454/ ) 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: MPI send and receive large data to self 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. --- Navigating Deadlocks in MPI with Large Data Transfers When working with the Message Passing Interface (MPI) in C++, a common challenge developers face is the occurrence of deadlocks during data communication. This problem often manifests when sending large data arrays, particularly when the size of the array exceeds a certain threshold, such as 100,000 elements. In this blog, we will explore how to effectively handle this issue and ensure smooth communication within your MPI applications. The Problem Explained In a typical MPI program, using MPI_Send and MPI_Recv can lead to significant challenges when dealing with large data arrays. The primary underlying issue is that these functions block the execution until the communication is complete. This means if one process is waiting to receive data while the other process is held up by a send operation that isn't completed, you may find yourself in a deadlock situation. Example Scenario Consider the following code snippet which attempts to send a large double array using MPI_Send and then immediately receive it using MPI_Recv: [[See Video to Reveal this Text or Code Snippet]] If N is increased to 100,000 or more, the program may exhibit unexpected behavior, including deadlocks, due to the blocking nature of these calls. The Solution To effectively resolve deadlocks during data transfers with large arrays, you can replace MPI_Send and MPI_Recv with their non-blocking counterparts: MPI_Isend and MPI_Irecv. Here’s how you can implement this change step by step. Step 1: Use Non-blocking Calls Using MPI_Isend and MPI_Irecv, the operations are initiated, allowing the program to continue executing without waiting for the communication to complete. Code Adjustment Change your send and receive calls like this: [[See Video to Reveal this Text or Code Snippet]] Here, request_send and request_recv are variables of type MPI_Request that you need to declare beforehand. Step 2: Complete the Operations After initiating the non-blocking sends and receives, it’s essential to ensure that the operations have completed by calling MPI_Wait: [[See Video to Reveal this Text or Code Snippet]] Final Code Example Here’s how your main function would look with these changes: [[See Video to Reveal this Text or Code Snippet]] Conclusion By following the above guidelines, you can effectively prevent deadlocks in your MPI programs, even when dealing with large data arrays. The shift from MPI_Send and MPI_Recv to MPI_Isend and MPI_Irecv enhances data transfer efficiency and minimizes the risks associated with blocking calls. Now that you've armed yourself with this knowledge, you can proceed with greater confidence in your MPI implementations. Happy coding!