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Parallel Processing in Python – A Practical Guide with Examples

• October 31, 2018
• Selva Prabhakaran

Parallel processing is a mode of operation where the task is executed simultaneously in multiple processors in the same computer. It is meant to reduce the overall processing time. In this tutorial, you’ll understand the procedure to parallelize any typical logic using python’s multiprocessing module.

1. Introduction

Parallel processing is a mode of operation where the task is executed simultaneously in multiple processors in the same computer. It is meant to reduce the overall processing time.

However, there is usually a bit of overhead when communicating between processes which can actually increase the overall time taken for small tasks instead of decreasing it.

In python, the multiprocessing module is used to run independent parallel processes by using subprocesses (instead of threads).

It allows you to leverage multiple processors on a machine (both Windows and Unix), which means, the processes can be run in completely separate memory locations. By the end of this tutorial you would know:

• How to structure the code and understand the syntax to enable parallel processing using multiprocessing ?
• How to implement synchronous and asynchronous parallel processing?
• How to parallelize a Pandas DataFrame?
• Solve 3 different usecases with the multiprocessing.Pool() interface.

2. How many maximum parallel processes can you run?

The maximum number of processes you can run at a time is limited by the number of processors in your computer. If you don’t know how many processors are present in the machine, the cpu_count() function in multiprocessing will show it.

3. What is Synchronous and Asynchronous execution?

In parallel processing, there are two types of execution: Synchronous and Asynchronous.

A synchronous execution is one the processes are completed in the same order in which it was started. This is achieved by locking the main program until the respective processes are finished.

Asynchronous, on the other hand, doesn’t involve locking. As a result, the order of results can get mixed up but usually gets done quicker.

There are 2 main objects in multiprocessing to implement parallel execution of a function: The Pool Class and the Process Class.

• Pool.map() and Pool.starmap()
• Pool.apply()
• Pool.map_async() and Pool.starmap_async()
• Pool.apply_async() )
• Process Class

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.

4. Problem Statement: Count how many numbers exist between a given range in each row

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.

Solution without parallelization

Let’s see how long it takes to compute it without parallelization.

For this, we iterate the function howmany_within_range() (written below) to check how many numbers lie within range and returns the count.

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5. How to parallelize any function?

The general way to parallelize any operation is to take a particular function that should be run multiple times and make it run parallelly in different processors.

To do this, you initialize a Pool with n number of processors and pass the function you want to parallelize to one of Pool s parallization methods.

multiprocessing.Pool() provides the apply() , map() and starmap() methods to make any function run in parallel.

So what’s the difference between apply() and map() ?

Both apply and map take the function to be parallelized as the main argument.

But the difference is, apply() takes an args argument that accepts the parameters passed to the ‘function-to-be-parallelized’ as an argument, whereas, map can take only one iterable as an argument.

So, map() is really more suitable for simpler iterable operations but does the job faster.

We will get to starmap() once we see how to parallelize howmany_within_range() function with apply() and map() .

5.1. Parallelizing using Pool.apply()

Let’s parallelize the howmany_within_range() function using multiprocessing.Pool() .

5.2. Parallelizing using Pool.map()

Pool.map() accepts only one iterable as argument.

So as a workaround, I modify the howmany_within_range function by setting a default to the minimum and maximum parameters to create a new howmany_within_range_rowonly() function so it accetps only an iterable list of rows as input.

I know this is not a nice usecase of map() , but it clearly shows how it differs from apply() .

5.3. Parallelizing using Pool.starmap()

In previous example, we have to redefine howmany_within_range function to make couple of parameters to take default values.

Using starmap() , you can avoid doing this.

How you ask?

Like Pool.map() , Pool.starmap() also accepts only one iterable as argument, but in starmap() , each element in that iterable is also a iterable.

You can to provide the arguments to the ‘function-to-be-parallelized’ in the same order in this inner iterable element, will in turn be unpacked during execution.

So effectively, Pool.starmap() is like a version of Pool.map() that accepts arguments.

6. Asynchronous Parallel Processing

The asynchronous equivalents apply_async() , map_async() and starmap_async() lets you do execute the processes in parallel asynchronously, that is the next process can start as soon as previous one gets over without regard for the starting order.

As a result, there is no guarantee that the result will be in the same order as the input.

6.1 Parallelizing with Pool.apply_async()

apply_async() is very similar to apply() except that you need to provide a callback function that tells how the computed results should be stored.

However, a caveat with apply_async() is, the order of numbers in the result gets jumbled up indicating the processes did not complete in the order it was started.

A workaround for this is, we redefine a new howmany_within_range2() to accept and return the iteration number ( i ) as well and then sort the final results.

It is possible to use apply_async() without providing a callback function.

Only that, if you don’t provide a callback, then you get a list of pool.ApplyResult objects which contains the computed output values from each process.

From this, you need to use the pool.ApplyResult.get() method to retrieve the desired final result.

6.2 Parallelizing with Pool.starmap_async()

You saw how apply_async() works.

Can you imagine and write up an equivalent version for starmap_async and map_async ?

The implementation is below anyways.

7. How to Parallelize a Pandas DataFrame?

So far you’ve seen how to parallelize a function by making it work on lists.

But when working in data analysis or machine learning projects, you might want to parallelize Pandas Dataframes, which are the most commonly used objects (besides numpy arrays) to store tabular data.

When it comes to parallelizing a DataFrame , you can make the function-to-be-parallelized to take as an input parameter:

• one row of the dataframe
• one column of the dataframe
• the entire dataframe itself

The first 2 can be done using multiprocessing module itself.

But for the last one, that is parallelizing on an entire dataframe, we will use the pathos package that uses dill for serialization internally.

First, lets create a sample dataframe and see how to do row-wise and column-wise paralleization.

Something like using pd.apply() on a user defined function but in parallel.

We have a dataframe. Let’s apply the hypotenuse function on each row, but running 4 processes at a time.

To do this, we exploit the df.itertuples(name=False) .

By setting name=False , you are passing each row of the dataframe as a simple tuple to the hypotenuse function.

That was an example of row-wise parallelization.

Let’s also do a column-wise parallelization.

For this, I use df.iteritems() to pass an entire column as a series to the sum_of_squares function.

Now comes the third part – Parallelizing a function that accepts a Pandas Dataframe, NumPy Array, etc. Pathos follows the multiprocessing style of: Pool > Map > Close > Join > Clear.

Check out the pathos docs for more info.

Thanks to notsoprocoder for this contribution based on pathos.

If you are familiar with pandas dataframes but want to get hands-on and master it, check out these pandas exercises .

8. Exercises

Problem 1: Use Pool.apply() to get the row wise common items in list_a and list_b .

9. Conclusion

Hope you were able to solve the above exercises, congratulations if you did! In this post, we saw the overall procedure and various ways to implement parallel processing using the multiprocessing module. The procedure described above is pretty much the same even if you work on larger machines with many more number of processors, where you may reap the real speed benefits of parallel processing. Happy coding and I’ll see you in the next one !

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Parallel Processing in Python

Parallel processing can increase the number of tasks done by your program which reduces the overall processing time. These help to handle large scale problems.

In this section we will cover the following topics:

Introduction to parallel processing

• Multi Processing Python library for parallel processing
• IPython parallel framework

For parallelism, it is important to divide the problem into sub-units that do not depend on other sub-units (or less dependent). A problem where the sub-units are totally independent of other sub-units is called embarrassingly parallel .

For example, An element-wise operation on an array. In this case, the operation needs to aware of the particular element it is handling at the moment.

In another scenario, a problem which is divided into sub-units have to share some data to perform operations. These results in the performance issue because of the communication cost.

There are two main ways to handle parallel programs:

In shared memory, the sub-units can communicate with each other through the same memory space. The advantage is that you don’t need to handle the communication explicitly because this approach is sufficient to read or write from the shared memory. But the problem arises when multiple process access and change the same memory location at the same time. This conflict can be avoided using synchronization techniques.

Threads are one of the ways to achieve parallelism with shared memory. These are the independent sub-tasks that originate from a process and share memory. Due to Global Interpreter Lock (GIL) , threads can’t be used to increase performance in Python. GIL is a mechanism in which Python interpreter design allow only one Python instruction to run at a time . GIL limitation can be completely avoided by using processes instead of thread. Using processes have few disadvantages such as less efficient inter-process communication than shared memory, but it is more flexible and explicit.

Multiprocessing for parallel processing

Using the standard multiprocessing module, we can efficiently parallelize simple tasks by creating child processes. This module provides an easy-to-use interface and contains a set of utilities to handle task submission and synchronization.

Process and Pool Class

By subclassing multiprocessing.process, you can create a process that runs independently. By extending the __init__ method you can initialize resource and by implementing Process.run() method you can write the code for the subprocess. In the below code, we see how to create a process which prints the assigned id:

To spawn the process, we need to initialize our Process object and invoke Process.start() method. Here Process.start() will create a new process and will invoke the Process.run() method.

The code after p.start() will be executed immediately before the task completion of process p. To wait for the task completion, you can use Process.join() .

Here’s the full code:

Pool class can be used for parallel execution of a function for different input data. The multiprocessing.Pool() class spawns a set of processes called workers and can submit tasks using the methods apply/apply_async and map/map_async . For parallel mapping, you should first initialize a multiprocessing.Pool() object. The first argument is the number of workers; if not given, that number will be equal to the number of cores in the system.

Let see by an example. In this example, we will see how to pass a function which computes the square of a number. Using Pool.map() you can map the function to the list and passing the function and the list of inputs as arguments, as follows:

When we use the normal map method, the execution of the program is stopped until all the workers completed the task. Using map_async() , the AsyncResult object is returned immediately without stopping the main program and the task is done in the background. The result can be retrieved by using the AsyncResult.get() method at any time as shown below:

Pool.apply_async assigns a task consisting of a single function to one of the workers. It takes the function and its arguments and returns an AsyncResult object.

IPython Parallel Framework

IPython parallel package provides a framework to set up and execute a task on single, multi-core machines and multiple nodes connected to a network. In IPython.parallel, you have to start a set of workers called Engines which are managed by the Controller. A controller is an entity that helps in communication between the client and engine. In this approach, the worker processes are started separately, and they will wait for the commands from the client indefinitely.

Ipcluster shell commands are used to start the controller and engines.

After the above process, we can use an IPython shell to perform task in parallel. IPython comes with two basic interfaces:

Direct Interface

• Task-based Interface

Direct Interface allows you to send commands explicitly to each of the computing units. This is flexible and easy to use. To interact with units, you need to start the engine and then an IPython session in a separate shell. You can establish a connection to the controller by creating a client. In the below code, we import the Client class and create an instance:

Here, Client.ids will give list of integers which give details of available engines.

Using Direct View instance, you can issue commands to the engine. Two ways we can get a direct view instance:

As a final step, you can execute commands by using the DirectView.execute method.

The above command will be executed individually by each engine. Using the get method you can get the result in the form of an AsyncResult object.

As shown above, you can retrieve the data by using the DirectView.pull method and send the data by using the DirectView.push method.

Task-based interface

The task-based interface provides a smart way to handle computing tasks. From the user point of view, this has a less flexible interface but it is efficient in load balancing on the engines and can resubmit the failed jobs thereby increasing the performance.

LoadBalanceView class provides the task-based interface using load_balanced_view method.

Using the map and apply method we can run some tasks. In LoadBalanceView the task assignment depends upon how much load is present on an engine at the time. This ensures that all engines work without downtime.

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Parallelization tutorial

Training materials for parallelization with Python, R, Julia, MATLAB and C/C++, including use of the GPU with Python and Julia. See the top menu for pages specific to each language.

2.1 Basics of OpenMP

2.2 calling openmp-based c code from r, 2.3 more advanced use of openmp in c, 3.1 mpi overview, 3.2 basic syntax for mpi in c, 3.3 starting mpi-based jobs.

View the Project on GitHub berkeley-scf/tutorial-parallelization

This project is maintained by berkeley-scf , the UC Berkeley Statistical Computing Facility

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Parallel processing in C/C++

Some long-standing tools for parallelizing C, C++, and Fortran code are openMP for writing threaded code to run in parallel on one machine and MPI for writing code that passages message to run in parallel across (usually) multiple nodes.

2 Using OpenMP threads for basic shared memory programming in C

It’s straightforward to write threaded code in C and C++ (as well as Fortran) to exploit multiple cores. The basic approach is to use the OpenMP protocol.

Here’s how one would parallelize a loop in C/C++ using an OpenMP compiler directive. In this case we are parallelizing the outer loop; the iterations of the outer loop are done in parallel, while the iterations of the inner loop are done serially within a thread. As with foreach in R, you only want to do this if the iterations do not depend on each other. The code is available as a C++ program (but the core of the code is just C code) in testOpenMP.cpp .

We would compile this program as follows

The main thing to be aware of in using OpenMP is not having different threads overwrite variables used by other threads. In the example above, variables declared within the #pragma directive will be recognized as variables that are private to each thread. In fact, you could declare int i before the compiler directive and things would be fine because OpenMP is smart enough to deal properly with the primary looping variable. But big problems would ensue if you had instead written the following code:

Note that we do want x declared before the compiler directive because we want all the threads to write to a common x (but, importantly, to different components of x ). That’s the point!

We can also be explicit about what is shared and what is private to each thread:

The easiest path here is to use the Rcpp package. In this case, you can write your C++ code with OpenMP pragma statements as in the previous subsection. You’ll need to make sure that the PKG_CXXFLAGS and PKG_LIBS environment variables are set to include -f openmp so the compilation is done correctly. More details/examples linked to from this Stack overflow post .

The goal here is just to give you a sense of what is possible with OpenMP.

The OpenMP API provides three components: compiler directives that parallelize your code (such as #pragma omp parallel for ), library functions (such as omp_get_thread_num() ), and environment variables (such as OMP_NUM_THREADS )

OpenMP constructs apply to structured blocks of code. Blocks may be executed in parallel or sequentially, depending on how one uses the OpenMP pragma statements. One can also force execution of a block to wait until particular preceding blocks have finished, using a barrier .

Here’s a basic “Hello, world” example that illustrates how it works (the full program is in helloWorldOpenMP.cpp ):

The parallel directive starts a team of threads, including the main thread, which is a member of the team and has thread number 0. The number of threads is determined in the following ways - here the first two options specify four threads:

#pragma omp parallel NUM_THREADS (4) // set 4 threads for this parallel block

omp_set_num_threads(4) // set four threads in general

the value of the OMP_NUM_THREADS environment variable

a default - usually the number of cores on the compute node

Note that in #pragma omp parallel for , there are actually two instructions, parallel starts a team of threads, and for farms out the iterations to the team. In our parallel for invocation, we could have done it more explicitly as:

We can also explicitly distribute different chunks of code amongst different threads as seen here and in the full program in sectionsOpenMP.cpp .

Here Work1, {Work2 + Work3} and Work4 are done in parallel, but Work2 and Work3 are done in sequence (on a single thread).

If one wants to make sure that all of a parallized calculation is complete before any further code is executed you can insert #pragma omp barrier .

Note that a #pragma for statement includes an implicit barrier as does the end of any block specified with #pragma omp parallel .

You can use nowait if you explicitly want to prevent threads from waiting at an implicit barrier: e.g., #pragma omp parallel sections nowait or #pragma omp parallel for nowait

One should be careful about multiple threads writing to the same variable at the same time (this is an example of a race condition). In the example below, if one doesn’t have the #pragma omp critical directive two threads could read the current value of result at the same time and then sequentially write to result after incrementing their local copy, which would result in one of the increments being lost. A way to avoid this is with the critical directive (for single lines of code you can also use atomic instead of critical ), as seen here and in the full program in criticalOpenMP.cpp :

You should also be able to use syntax like the following for the parallel for declaration (in which case you shouldn’t need the #pragma omp critical ):

I believe that doing this sort of calculation where multiple threads write to the same variable may be rather inefficient given time lost in waiting to have access to result, but presumably this would depend on how much time is spent in myFun() relative to the reduction operation.

There are multiple MPI implementations, of which openMPI and mpich are very common. openMPI is quite common, and we’ll use that.

In MPI programming, the same code runs on all the machines. This is called SPMD (single program, multiple data). As we saw a bit with the pbdR code, one invokes the same code (same program) multiple times, but the behavior of the code can be different based on querying the rank (ID) of the process. Since MPI operates in a distributed fashion, any transfer of information between processes must be done explicitly via send and receive calls (e.g., MPI_Send , MPI_Recv , MPI_Isend , and MPI_Irecv ). (The ``MPI_’’ is for C code; C++ just has Send , Recv , etc.)

The latter two of these functions ( MPI_Isend and MPI_Irecv ) are so-called non-blocking calls. One important concept to understand is the difference between blocking and non-blocking calls. Blocking calls wait until the call finishes, while non-blocking calls return and allow the code to continue. Non-blocking calls can be more efficient, but can lead to problems with synchronization between processes.

In addition to send and receive calls to transfer to and from specific processes, there are calls that send out data to all processes ( MPI_Scatter ), gather data back ( MPI_Gather ) and perform reduction operations ( MPI_Reduce ).

Debugging MPI code can be tricky because communication can hang, error messages from the workers may not be seen or readily accessible, and it can be difficult to assess the state of the worker processes.

Here’s a basic hello world example The code is also in mpiHello.c .

There are C ( mpicc ) and C++ ( mpic++ ) compilers for MPI programs ( mpicxx and mpiCC are synonyms). I’ll use the MPI C++ compiler even though the code is all plain C code.

Then we’ll run the executable via mpirun . Here the code will just run on my single machine, called arwen . See Section 3.3 for details on how to run on multiple machines.

Here’s the output we would expect:

To actually write real MPI code, you’ll need to go learn some of the MPI syntax. See quad_mpi.c and quad_mpi.cpp , which are example C and C++ programs (for approximating an integral via quadrature) that show some of the basic MPI functions. Compilation and running are as above:

And here’s the output we would expect:

MPI-based executables require that you start your process(es) in a special way via the mpirun command. Note that mpirun , mpiexec and orterun are synonyms under openMPI .

The basic requirements for starting such a job are that you specify the number of processes you want to run and that you indicate what machines those processes should run on. Those machines should be networked together such that MPI can ssh to the various machines without any password required.

3.3.1 Running an MPI job with machines specified manually

There are two ways to tell mpirun the machines on which to run the worker processes.

First, we can pass the machine names directly, replicating the name if we want multiple processes on a single machine. In the example here, these are machines accessible to me, and you would need to replace those names with the names of machines you have access to. You’ll need to set up SSH keys so that you can access the machines without a password.

Alternatively, we can create a file with the relevant information.

One can also just duplicate a given machine name as many times as desired, rather than using slots .

3.3.2 Running an MPI job within a Slurm job

If you are running your code as part of a job submitted to Slurm, you generally won’t need to pass the machinefile or np arguments as MPI will get that information from Slurm. So you can simply run your executable, in this case first checking which machines mpirun is using:

3.3.3 Additional details

To limit the number of threads for each process, we can tell mpirun to export the value of OMP_NUM_THREADS to the processes. E.g., calling a C program, quad_mpi :

There are additional details involved in carefully controlling how processes are allocated to nodes, but the default arguments for mpirun should do a reasonable job in many situations.

Hackerrank Certification Test -Parallel Processing

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August 17, 2023 12 min to read

HackerRank Algorithms Solutions using Python and C++(CPP)

An algorithm is a set of instructions that are used to accomplish a task, such as finding the largest number in a list, removing all the red cards from a deck of playing cards, sorting a collection of names, figuring out an average movie rating from just your friend’s opinion

It’s an essential part of programming. It comes under the fundamentals of computer science. It gives us the advantage of writing better and more efficient code in less time. It is a key topic when it comes to Software Engineering interview questions so as developers, we must know Algorithms

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Solution to Algorithms HackerRank solution

HackerRank Algorithms Solution using Python & C++. This post is about HackerRank Algorithms Solutions in C++ & Python. All the problems are solved in Python 3 and C++.

Solve Me First - HackerRank solution in Python and C++

Problem Statement: the sum of the above two integers

Simple Array Sum - HackerRank solution in Python and C++

Problem Statement: Print the sum of the array’s elements as a single integer.

Compare the Triplets - HackerRank solution in Python and C++

Problem Statement: Complete the function compareTriplets in the editor below. It must return an array of two integers, the first being Alice’s score and the second being Bob’s.

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• a: an array of integers representing Alice’s challenge rating
• b: an array of integers representing Bob’s challenge rating

A very big sum - HackerRank solution in Python and C++

Problem Statement : Complete the aVeryBigSum function in the editor below. It must return the sum of all array elements. aVeryBigSum has the following parameter(s):

• ar: an array of integers.

Diagonal difference - HackerRank solution in Python and C++

Problem Statement: Given a square matrix, calculate the absolute difference between the sums of its diagonals.

Plus minus - HackerRank solution in Python and C++

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Staircase- HackerRank solution in python and c++

Problem Statement: Complete the staircase function in the editor below. It should print a staircase as described above.

the staircase has the following parameter(s):

• n: an integer

Mini-max sum - HackerRank solution in Python and C++

Given five positive integers, find the minimum and maximum values that can be calculated by summing exactly four of the five integers. Then print the respective minimum and maximum values as a single line of two space-separated long integers.

Birthday cake Candles- HackerRank Solution in Python and C++

You are in charge of the cake for your niece’s birthday and have decided the cake will have one candle for each year of her total age. When she blows out the candles, she’ll only be able to blow out the tallest ones. Your task is to find out how many candles she can successfully blow out.

Grading Students

Complete the function grading students in the editor below. It should return an integer array consisting of rounded grades

Reverse Integer

Thanks for reading this post. I hope you like this post. If you have any questions, then feel free to comment below.

Single-layer Neural Networks in Machine Learning (Perceptrons)

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HackerRank Minimum Time Required problem solution

In this HackerRank Minimum Time Required Interview preparation kit problem you need to complete the minimumTimefunction.

Problem solution in Python programming.

Problem solution in Java Programming.

Problem solution in c++ programming..

Posted by: YASH PAL

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The upper bound is wrong, I think this solution might cover all hackerrank's test cases, but consider a test case where machines = [3, 3, 3] and goal is 10 For that, upper bound according to solution will be 10 days and lower bound will be 10 days, while loop doesn't run and it returns lower which is 10 days, but in 10 days it will not get completed, on 9th day, 9 items will be completed, but on 10th day nothing will be completed, the next set of items will only get completed on 12th day, so answer will be 12

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