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Intel
intel.com › developers › tools › oneapi › training › heterogeneous programming using numba-data parallel python*
Heterogeneous Programming Using Data Parallel Extension for Numba* for AI
This course is designed for Python developers who want to learn the basics of data parallel Python for data parallel and heterogeneous hardware (such as CPU and GPU) without leaving the Python ecosystem or compromising on performance.
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Machine Learning Plus
machinelearningplus.com › blog › parallel processing in python – a practical guide with examples
Parallel Processing in Python - A Practical Guide with Examples | ML+
April 20, 2022 - Let’s take up a typical problem and implement parallelization using the above techniques. In this tutorial, we stick to the Pool class, because it is most convenient to use and serves most common practical applications. The first problem is: Given a 2D matrix (or list of lists), count how many numbers are present between a given range in each row. We will work on the list prepared below. ... import numpy as np from time import time # Prepare data np.random.RandomState(100) arr = np.random.randint(0, 10, size=[200000, 5]) data = arr.tolist() data[:5]
Discussions
How to Do Data Parallelization in Python? - Stack Overflow
Threading in Python is actually not parallel at all and would slow things down, because the Global Interpreter Lock only executes one Python instruction at a time regardless of the number of threads. It's only suitable for doing something while waiting for I/O on another thread. True multiprocessing would work, but sharing the data ... More on stackoverflow.com
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Coverage with Pytest in parallel doesn't collect data

Use pytest-cov, and read this: https://pytest-cov.readthedocs.io/en/latest/xdist.html

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🌐 r/learnpython
2
3
September 3, 2024
How useful is knowledge of parallel programming in ML? [D]
Depends on what parallel computation you are referring to CUDA knowledge is ofc useful and valued. But NVIDIA's tool chain is really its own walled garden. It is difficult for outsiders to outdo NVIDIA themselves. If you refer to parallel programming as something close to distributed data processing, then yes it is pretty useful. Though this is more on case by case basis. Overall, I feel the job market is edging towards people with system integration skills rather deep domain expertise, due to the aforementioned NVIDIA dynamics, but I could be wrong on this one as well. More on reddit.com
🌐 r/MachineLearning
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January 21, 2022
Help with Celery and Parallel Processing a Loop of Data.
Hello! I'm a bot! It looks to me like your post might be better suited for r/learnpython , a sub geared towards questions and learning more about python. That said, I am a bot and it is hard to tell. Please follow the subs rules and guidelines when you do post there, it'll help you get better answers faster. Show r/learnpython the code you have tried and describe where you are stuck. You can also ask this question in the Python discord , a large, friendly community focused around the Python programming language, open to those who wish to learn the language or improve their skills, as well as those looking to help others. README | FAQ | this bot is written and managed by u/IAmKindOfCreative This bot is currently under development and experiencing changes to improve its usefulness More on reddit.com
🌐 r/Python
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3
July 29, 2019
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Intelpython
intelpython.github.io › DPEP › main
Data Parallel Extensions for Python — Data Parallel Extensions for Python* 0.1 documentation
Data Parallel Extensions for Python* extend numerical Python capabilities beyond CPU and allow even higher performance gains on data parallel devices, such as GPUs.
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Python
wiki.python.org › moin › ParallelProcessing
Parallel Processing and Multiprocessing in Python
Dask - Dask is a flexible library for parallel computing in Python. It offers · Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. “Big Data” collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments.
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Towards Data Science
towardsdatascience.com › home › latest › parallelize your python code to save time on data processing
Parallelize your python code to save time on data processing | Towards Data Science
January 28, 2025 - import concurrent.futures import urllib.request URLS = ['http://www.foxnews.com/', 'http://www.cnn.com/', 'http://europe.wsj.com/', 'http://www.bbc.co.uk/', 'http://some-made-up-domain.com/'] # Retrieve a single page and report the URL and contents def load_url(url, timeout): with urllib.request.urlopen(url, timeout=timeout) as conn: return conn.read() # Use a with statement to ensure threads are cleaned up promptly with concurrent.futures.ThreadPoolExecutor(4) as executor: future_to_url = {executor.submit(load_url, url, 60): url for url in URLS} for future in concurrent.futures.as_completed(f
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OneUptime
oneuptime.com › home › blog › how to process datasets with parallel jobs in python
How to Process Datasets with Parallel Jobs in Python
January 23, 2026 - For very large datasets, process in chunks to control memory usage and provide progress feedback. # chunked_parallel.py # Process large datasets in chunks from concurrent.futures import ProcessPoolExecutor, as_completed from typing import List, Iterator, Callable, TypeVar T = TypeVar('T') def chunk_list(items: List[T], chunk_size: int) -> Iterator[List[T]]: """Split a list into chunks of specified size""" for i in range(0, len(items), chunk_size): yield items[i:i + chunk_size] def process_chunk(chunk: List[dict]) -> List[dict]: """ Process a chunk of items.
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GitHub
github.com › pydata › parallel-tutorial
GitHub - pydata/parallel-tutorial: Parallel computing in Python tutorial materials · GitHub
Tabular data with dataframes · Download this repository: git clone https://github.com/pydata/parallel-tutorial · or download as a zip file. Install Anaconda (large) or Miniconda (small) Create a new conda environment: conda env create -f environment.yml source activate parallel # Linux OS/X activate parallel # Windows · If you want to use Spark (this is a large download): conda install -c conda-forge pyspark · Test your installation: python -c "import concurrent.futures, dask, jupyter" We will generate a dataset for use locally.
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Berkeley
computing.stat.berkeley.edu › tutorial-parallelization › parallel-python.html
Parallel processing in Python
Dask and Ray are powerful packages for parallelization that allow one to parallelize tasks in similar fashion to ipyparallel. But they also provide additional useful functionality: Dask allows one to work with large datasets that are split up across multiple processes on (potentially) multiple nodes, providing Spark/Hadoop-like functionality.
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Chicagobooth
wiki.chicagobooth.edu › display › GRID › Data+Parallelism+in+Python
Data Parallelism in Python - grid - Booth IT Wiki
July 11, 2018 - The multiprocessing module in Python provides users the ability to distribute multiple inputs to a single function over multiple processors (data parallelism).
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Medium
medium.com › @iTheRakeshP › mastering-parallel-data-processing-python-5ee20ec6e117
Mastering Parallel Data Processing: Python | by Rakesh Parmar | Medium
October 13, 2025 - The architecture shows how chunks are distributed across workers automatically, with each worker handling validation, transformation, and database insertion independently. This eliminates the Global Interpreter Lock (GIL) limitations of threading. ... Worker Processes: Configurable 1–16 processes (default: 4) — each worker is a separate Python process · Threads per Worker: 1–8 threads per worker (default: 2) — enables nested parallelism within each process
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Python
docs.python.org › 3 › library › multiprocessing.html
multiprocessing — Process-based parallelism
The multiprocessing module also introduces the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism).
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Stack Overflow
stackoverflow.com › questions › 40274793 › how-to-do-data-parallelization-in-python
How to Do Data Parallelization in Python? - Stack Overflow
Top answer
1 of 1
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If you have multiple cores and processing each 2 dimensional list is expensive operation you could use Pool from multiprocessing. Here's a short example that squares numbers in different process:

import multiprocessing as mp

A = [[[1,2,3],[4,5,6],[7,8,9]],[[2,4,1],[1,4,6],[1,2,4]]]

def square(l):
    return [[x * x for x in sub] for sub in l]

pool = mp.Pool(processes=mp.cpu_count())
res = pool.map(square, A)

print res

Output:

[[[1, 4, 9], [16, 25, 36], [49, 64, 81]], [[4, 16, 1], [1, 16, 36], [1, 4, 16]]]

Pool.map will behave like built-in map while splitting the iterable to worker processes. It also has third parameter called chunksize that defines how big chunks are submitted to workers.

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Ohio Supercomputer Center
osc.edu › resources › getting_started › howto › howto_pytorch_distributed_data_parallel_ddp
HOWTO: PyTorch Distributed Data Parallel (DDP) | Ohio Supercomputer Center
August 16, 2024 - from torch.nn.parallel import DistributedDataParallel as DDP class Trainer: def __init__(self, trainer_config: TrainerConfig, model, optimizer, train_dataset, test_dataset=None): ...
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KDnuggets
kdnuggets.com › 2022 › 07 › parallel-processing-large-file-python.html
Parallel Processing Large File in Python - KDnuggets
If you are only working with a tabular dataset and want to improve your processing performance, then I will suggest you try Dask, datatable, and RAPIDS · Parallel batch processing in Python | by Dennis Bakhuis | Towards Data Science
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Anl
alcf.anl.gov › events › module-1-introduction-data-parallel-essentials-python
Module 1: Introduction to Data Parallel Essentials for Python | Argonne Leadership Computing Facility
April 13, 2022 - The Data Parallel essentials for Python workshop demonstrates high-performing code targeting Intel XPUs using Python. The talk will introduce basics of Numba and how to write parallel Python programs using Numba. The talk also introduces Numba-dppy with examples of how to write data-parallel code inside numba.jit decorated functions and offload them to a SYCL device.
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Towards Data Science
towardsdatascience.com › home › data science › parallelization in python: the easy way
Parallelization in Python: The Easy Way | Towards Data Science
October 26, 2022 - In this article, I want to show that this does not have to be the case. In simple scenarios, code can be simple to parallelize. Python is known to be an easy-to-understand programming language, and parallel code can also be easy to read and implement.
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Stanford
rcpedia.stanford.edu › _user_guide › parallel_processing_python
Parallel Processing Python - Research Computing Resources
This library has several methods to help you parallelize your code. The most common example is using the Pool object. In general, the Pool object works by applying a processing function you've created to a number of items you need processed. Take the following example: Python Code · from multiprocessing import Pool def f(x): return x*x data = [1,2,3,4] with Pool(5) as p: results = p.map(f, data) This code will open a Pool of 5 processes, and execute the function f over every item in data in parallel.
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Medium
medium.com › swlh › 5-step-guide-to-parallel-processing-in-python-ac0ecdfcea09
5-Steps Guide to Parallel Processing in Python | by Kurt F. | The Startup | Medium
September 10, 2020 - When it comes to parallelizing a DataFrame, you can apply it to the dataframe as an input parameter for the function to be parallelized, a row, a column, or the whole dataframe.
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Anyscale
anyscale.com › blog › parallelizing-python-code
Guide to Parallelizing Python Code
November 22, 2023 - There are several common ways to parallelize Python code. You can launch several application instances or a script to perform jobs in parallel. This approach is great when you don’t need to exchange data between parallel jobs.
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Ohio Supercomputer Center
osc.edu › resources › getting_started › howto › howto_run_python_in_parallel
HOWTO: Run Python in Parallel | Ohio Supercomputer Center
March 5, 2025 - We can improve performace of python calculation by running python in parallel. In this turtorial we will be making use of the multithreading library to run python code in parallel. Multiprocessing is part of the standard python library distribution on versions python/2.6 and above so no additonal instalation is required (Pitzer both offers 2.7 and above and Cardinal offers 3.9 and above so this should not be an issue).