Скачать или смотреть Resolving numpy matrix multiplication issues in HPC through multithreading

Discover how to solve the issue of `numpy` matrix multiplication failing during multiprocessing on `HPC` environments, and learn why `multithreading` can be a viable alternative.
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Handling Numpy Matrix Multiplication in High-Performance Computing Environments

When working with High-Performance Computing (HPC) environments, it’s common to run into issues that don’t appear in local development. One prevalent problem many users encounter is related to matrix multiplication using numpy, particularly when utilizing multiprocessing. If you've recently tried parallelizing your numpy operations on an HPC system and hit a wall with your matrix multiplication hanging indefinitely, you’re not alone. In this guide, we'll dissect the problem and offer a working solution.

Understanding the Problem

The initial question arises from trying to compute the product of two dense matrices with dimensions (2500, 208) and (208, 2500) using numpy in a parallel computing context. While the operation performs beautifully on a single process or local multiprocessing, it hangs indefinitely when launched on an HPC system.

Scenario Breakdown

Two Matrices: Size (2500, 208) and (208, 2500)

Local Machine: Multiplication succeeds using multiprocessing

HPC Environment: Processes stuck when attempting the same multiplication

Attempted Libraries: numpy.matmul and A.dot(B), including direct calls to the BLAS library's dgemm function

Environment and Resources Allocated

The setup for launching the Python script on the HPC system is as follows:

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

Here, the agents parameter indicates how many parallel processes should be spawned for execution.

Analyzing the Underlying Causes

There can be several reasons why your multiprocessing task stalls on HPC while working seamlessly in other environments:

Resource Allocation: The way processes are managed on HPC can influence performance. During parallel execution, resources may not be efficiently distributed.

Library Conflicts: The differences in how libraries like LAPACK are configured on different systems can lead to unexpected behavior in parallel executions.

Thread Safety Issues: numpy operations can sometimes result in issues when combined with multiprocessing due to the libraries managing their threads differently.

The Solution: Switching to Multithreading

Despite the frustration that arises from these complications, there is a straightforward workaround that can often resolve the issue—using multithreading instead of multiprocessing.

Why Multithreading Works

Shared Memory: Multithreading utilizes shared memory space, thereby reducing overhead related to separate memory management for each process. This can lead to improved performance with matrix operations, especially in environments where memory management is constrained.

Less Overhead: The overhead associated with starting new processes can be significant. Threads are more lightweight, resulting in faster execution times in scenarios like matrix multiplication.

Implementation Example

Instead of implementing the multiprocessing approach, modify your code to utilize threading as shown below:

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

Conclusion

If you find yourself facing severe performance issues with numpy matrix multiplications in HPC environments using multiprocessing, consider shifting to multithreading. While the original reason behind the multiprocessing block is unclear, the shift to threads has proven effective and efficient in overcoming the issue.

By optimizing your approach to leverage the strengths of multithreading, you can enhance the performance of computational tasks while alleviating the frustration of endless WAIT states in your matrix operations. Happy coding!