Top 10 Libraries and Packages for Parallel Processing in Python
Want to parallelize the heavy workload and not even parallelize but also distribute the work on different clusters??
Hola Everyone, i think you are doing extremely well. Today we are discussing about Top 10 python libraries and frameworks for parallelizing and for work distribution. Let’s start :)
Threading Module
As you all know that native python is very slow while compared to other programming languages. Because it is a single threaded , you might have also notice while running on a google colab (A very nice cloud platform), still yet usage of 5% of your CPU.
What is a Thread ?
It’s Simply a Small unit of your work.
It’s not seems like a Parallelizing the work but separately a concurrency protocol, which means that if one thread running on a CPU if that thread wants to wait for some resource in that waiting time CPU is in ideal so for high efficiency usage of CPU another thread is on the way . This is so called MULTI-TASKING
Multi-Processing
In the previous section of multithreading using Threading module , i have missed on point that threading only work in a single processor of your machine . If your machine having multiple cores then there low efficiency usage of your memory :(
This Problem overcomes when we are went’s through multi-processing.
Let me introduce one Guy , GIL -> Global Interpreter Lock
In Python by default the entire code is executed on a single Core, because of the intermediary lock which is GIL it doesn’t allow the code to run on multiple cores. By using multiprocessing library we can by pass the Python Global Interpreter Lock and run on multiple Cores.
So by using of built-in module of multiprocessing , so we can bypass the GIL to perform multi-processing of section of code to execute on mulitple cores in our machine.
Joblib
Joblib is also a light-weight python package for parallel computation, and it is mainly used for numpy data structures.
Joblib is simply a library for parallel the python functions and run the code on multi-processing.
- The architecture of the code execution is basically same as multi-processing module but difference it has a capability of using mulitple backend and prefers.
Dask
From the whole of this blog content Dask is one of the best module for not only parallelizing but also for distributive computing. This package have lot of content to explore . In this blog i will try to explain all of the content :)
In the December 2014, “Matthew Rocklin” whose actually started to develop this beautiful package for parallel computing and right now there are 500+ contributors for this free open source python library.
The best thing about this is used for computing the large set of data which is not fit on our modern computer memory . And the beautiful thing is that Dask has it’s own data structures like numpy arrays and pandas like dataframe.
Dask is also used to distrubute the work on cluster which was defined by the user rules , like how many workers on this cluster or how many threads_per_worker should be assigned.
Let me illustrate the distributed work on my local machine
- My First Ananconda Prompt is my Client who is Schedule the work.
- I have assigned two workers , who are: 1 row second column prompt and 2 row first column prompt.
- And the last Prompt is my python code to compute huge mathematics.
In this way we can create a cluster for ur own design based on your machine capacity :)
This all about the Dask but there is a lot to explore but for basic exploring it’s fine , if you want to more content definetly i will suggest their official doc.
Ray
Ray is also same architecture of Dask but the key difference is Dask is centralized Client Scheduler but Ray is decentralized one, which means it each worker has it’s own scheduler so that in any wring situation whole cluster is not damaged.
Ray is basically a decorator formed wrapper python code. so that we can easily parallize the existing python code.
Dispy
It let’s you distribute the total python programs or just individual programs or functions across a cluster as i told you previously.
Dispy syntax somewhat equals to the multi-processing module in that you explicitly create a cluster , where in multi-processing is to create a pool of processes.
Pandaral·lel
Pandaral.lel , the name itself suggests you that is to parallalize the pandas framework while you using pandas dataframe.
Mainly it will help you in data analysis. This will parallalize the work on multiple nodes.
Note : This module is only work for Windows
Ipyparallel
Ipyparallel is another tightly focused multiprocessing and task-distribution system, specifically for parallelizing the execution of Jupyter notebook code across a cluster. Projects and teams already working in Jupyter can start using Ipyparallel immediately.
Ipyparallel supports many approaches to parallelizing code. On the simple end, there’s map, which applies any function to a sequence and splits the work evenly across available nodes. For more complex work, you can decorate specific functions to always run remotely or in parallel.
pyCUDA
This library useful when you have NVIDIA’s GPU . It let’s you connect your work-load to the GPU Cuda Cores and execute on a parallel way.
Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions.
pySpark
Spark is great for scaling up data science tasks and workloads! As long as you’re using Spark data frames and libraries that operate on these data structures, you can scale to massive data sets that distribute across a cluster. However, there are some scenarios where libraries may not be available for working with Spark data frames, and other approaches are needed to achieve parallelization with Spark. This post discusses three different ways of achieving parallelization in PySpark:
- Native Spark: if you’re using Spark data frames and libraries (e.g. MLlib), then your code we’ll be parallelized and distributed natively by Spark.
- Thread Pools: The multiprocessing library can be used to run concurrent Python threads, and even perform operations with Spark data frames.
- Pandas UDFs: A new feature in Spark that enables parallelized processing on Pandas data frames within a Spark environment.
