th 26 - Why Multiprocessing.Pool() is slower than regular functions

Why Multiprocessing.Pool() is slower than regular functions

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th?q=Multiprocessing - Why Multiprocessing.Pool() is slower than regular functions

Have you ever encountered a situation where your program ran slower when you used the multiprocessing.Pool() method instead of regular functions? If so, then you are not alone. This issue is a common problem that developers face when they try to apply parallel computing techniques to speed up their Python code.

The reason why multiprocessing.Pool() is slower than regular functions is due to its inherent overhead. When you use multiprocessing.Pool(), you are creating subprocesses that run in parallel to execute the functions. However, the creation and management of these subprocesses require extra time and resources, which can slow down the overall execution time of your program.

Furthermore, the way that multiprocessing.Pool() works can also have a negative impact on the performance of your code. The method works by dividing the input data into smaller chunks and distributing them among the subprocesses. However, this distribution process can cause communication overhead and data transfer delays, which can again create more time and resource overhead.

To learn more about the drawbacks of multiprocessing.Pool() and how it compares to other parallel computing methods in Python, read our comprehensive guide on the topic. We’ll discuss the pros and cons of various techniques, and provide you with best practices that can help you avoid common pitfalls and maximize the performance of your parallelized code.

th?q=Multiprocessing - Why Multiprocessing.Pool() is slower than regular functions
“Multiprocessing.Pool() Slower Than Just Using Ordinary Functions” ~ bbaz


Python multiprocessing is a popular solution that enables developers to execute and distribute programs across multiple processors or cores of a single machine. Multiprocessing is used to speed up programs where CPU usage is the limiting factor. While multiprocessing is a powerful tool, it may not always be the best option for performance. In some cases, multiprocessing.Pool() can be slower than executing regular functions, and in this post, we will explore why this is the case.

Multiprocessing vs Regular Functions

Multiprocessing is a way of breaking down a task into multiple parts and executing these parts simultaneously on different processors or cores. Each part is assigned a different process, and communication between these processes is managed through IPC (Inter-process Communication).

On the other hand, regular functions operate within a single Python process, i.e., all code executes on a single processor or core. Regular functions do not rely on IPC for communication.

Table Comparison

Multiprocessing.Pool() Regular Functions
Uses IPC for communication between processes No IPC used
Can utilize multiple cores or processors Only uses a single processor or core
May have overhead costs associated with IPC No additional overhead costs

Overhead Costs

One of the reasons why multiprocessing.Pool() can be slower than regular functions is because there are overhead costs associated with IPC (Inter-process Communication). When using multiprocessing.Pool(), the Python interpreter must create new processes, allocate memory for them, and communicate between these processes. These overhead costs add up, and in cases where the task is relatively small, they may prove to be prohibitively expensive.

Memory Usage

An additional factor that may contribute to slower performance when using multiprocessing.Pool() is increased memory usage. In order to operate across multiple processes, each process created by multiprocessing.Pool() requires a copy of the parent process memory space. This can result in significant memory usage, especially when working with large datasets.

Pickling Overhead

When creating new processes with multiprocessing.Pool(), each function call and its arguments must be pickled and sent to the new process over IPC. The process then unpickles the data and executes the function. This pickling and unpickling process can add significant overhead, especially when working with large datasets or complex data structures.

Function Execution Time

In some cases, the actual time taken to execute a function in multiprocessing.Pool() may be slower than executing the same function in a regular function. This may be due to a number of factors, including the overhead costs and increased memory usage associated with multiprocessing. When running multiple functions, the overhead costs can increase, leading to further degradation in performance.

Communication Overhead

Another factor that can impact the performance of multiprocessing.Pool() is communication overhead. As mentioned earlier, IPC is used to communicate between processes in multiprocessing. This communication can have significant overhead costs, especially when working with large amounts of data. In cases where communication takes longer than the actual computation time, the benefits of multiprocessing may not outweigh the costs.


Python multiprocessing.Pool() can be an effective way to speed up programs when CPU usage is the limiting factor. However, in some cases, the overhead costs associated with multiprocessing can lead to slower performance compared to regular functions. Factors such as increased memory usage, pickling overhead, function execution time, and communication overhead can all contribute to slower performance when using multiprocessing.Pool(). It is important to carefully consider these factors before deciding whether to use multiprocessing.Pool() or regular functions.

As we conclude this article, we would like to emphasize on the importance of understanding the trade-offs between using multiprocessing.Pool() and regular functions. While it might be tempting to use multiprocessing to speed up your code, it’s important to note that there are certain cases where this approach might not be the best solution.

One of the main reasons why multiprocessing.Pool() might be slower than regular functions is because of the overhead involved in creating processes. For instance, if you’re performing a trivial operation that takes only a few milliseconds, the time taken by the process creation and termination will eventually become more significant than the actual operation being performed.

Another reason why multiprocessing.Pool() might end up being slower than regular functions is due to the overhead involved in data transfer between processes. Specifically, every time a process needs to communicate with another one, it has to serialize and deserialize data, which can take a considerable amount of time.

In conclusion, while multiprocessing.Pool() has its benefits, it’s essential to take into account the potential performance issues that come with this approach. At the end of the day, the right solution will depend on the specifics of your use case, and it’s always recommended to benchmark different approaches to determine the most optimal one.

People also ask about why multiprocessing.Pool() is slower than regular functions:

  1. What is multiprocessing.Pool()?
  2. Why is it slower compared to regular functions?
  3. Are there any benefits of using multiprocessing.Pool() despite the slower speed?


  • Multiprocessing.Pool() is a method in Python that allows for parallel processing of functions.
  • It may be slower than regular functions because there is an overhead involved in creating and managing multiple processes.
  • Despite the slower speed, multiprocessing.Pool() can still be beneficial when dealing with computationally intensive tasks or when processing large amounts of data. It allows for the parallel processing of tasks, which can greatly reduce the overall time needed to complete a task.