diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 8e1628b..0d99726 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-08-19T14:06:53","documenter_version":"1.5.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-08-20T12:05:51","documenter_version":"1.5.0"}} \ No newline at end of file diff --git a/dev/LEQ/index.html b/dev/LEQ/index.html index 5ed7f23..355d0a9 100644 --- a/dev/LEQ/index.html +++ b/dev/LEQ/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+- · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/asp/index.html b/dev/asp/index.html index 88067e9..d50cebe 100644 --- a/dev/asp/index.html +++ b/dev/asp/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+- · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/getting_started_with_julia/index.html b/dev/getting_started_with_julia/index.html index 858a95d..7c14823 100644 --- a/dev/getting_started_with_julia/index.html +++ b/dev/getting_started_with_julia/index.html @@ -1,5 +1,5 @@ -Getting started · XM_40017

Getting started

Introduction

The programming of this course will be done using the Julia programming language. Thus, we start by explaining how to get up and running with Julia. After studying this page, you will be able to:

  • Use the Julia REPL,
  • Run serial and parallel code,
  • Install and manage Julia packages.

Why Julia?

Courses related with high-performance computing (HPC) often use languages such as C, C++, or Fortran. We use Julia instead to make the course accessible to a wider set of students, including the ones that have no experience with C/C++ or Fortran, but are willing to learn parallel programming. Julia is a relatively new programming language specifically designed for scientific computing. It combines a high-level syntax close to interpreted languages like Python with the performance of compiled languages like C, C++, or Fortran. Thus, Julia will allow us to write efficient parallel algorithms with a syntax that is convenient in a teaching setting. In addition, Julia provides easy access to different programming models to write distributed algorithms, which will be useful to learn and experiment with them.

Tip

You can run the code in this link to learn how Julia compares to other languages (C and Python) in terms of performance.

Installing Julia

This is a tutorial-like page. Follow these steps before you continue reading the document.

The Julia REPL

Starting Julia

There are several ways of opening Julia depending on your operating system and your IDE, but it is usually as simple as launching the Julia app. With VSCode, open a folder (File > Open Folder). Then, press Ctrl+Shift+P to open the command bar, and execute Julia: Start REPL. If this does not work, make sure you have the Julia extension for VSCode installed. Independently of the method you use, opening Julia results in a window with some text ending with:

julia>

You have just opened the Julia read-evaluate-print loop, or simply the Julia REPL. Congrats! You will spend most of time using the REPL, when working in Julia. The REPL is a console waiting for user input. Just as in other consoles, the string of text right before the input area (julia> in the case) is called the command prompt or simply the prompt.

Basic usage

The usage of the REPL is as follows:

  • You write some input
  • press enter
  • you get the output

For instance, try this

julia> 1 + 1

A "Hello world" example looks like this in Julia

julia> println("Hello, world!")

Try to run it in the REPL.

Help mode

Curious about what the function println does? Enter into help mode to look into the documentation. This is done by typing a question mark (?) into the input field:

julia> ?

After typing ?, the command prompt changes to help?>. It means we are in help mode. Now, we can type a function name to see its documentation.

help?> println

Package and shell modes

The REPL comes with two more modes, namely package and shell modes. To enter package mode type

julia> ]

Package mode is used to install and manage packages. We are going to discuss the package mode in greater detail later. To return back to normal mode press the backspace key several times.

To enter shell mode type semicolon (;)

julia> ;

The prompt should have changed to shell> indicating that we are in shell mode. Now you can type commands that you would normally do on your system command line. For instance,

shell> ls

will display the contents of the current folder in Mac or Linux. Using shell mode in Windows is not straightforward, and thus not recommended for beginners.

Running Julia code

Running more complex code

Real-world Julia programs are not typed in the REPL in practice. They are written in one or more files and included in the REPL. To try this, create a new file called hello.jl, write the code of the "Hello world" example above, and save it. If you are using VSCode, you can create the file using File > New File > Julia File. Once the file is saved with the name hello.jl, execute it as follows

julia> include("hello.jl")
Warning

Make sure that the file "hello.jl" is located in the current working directory of your Julia session. You can query the current directory with function pwd(). You can change to another directory with function cd() if needed. Also, make sure that the file extension is .jl.

The recommended way of running Julia code is using the REPL as we did. But it is also possible to run code directly from the system command line. To this end, open a terminal and call Julia followed by the path to the file containing the code you want to execute.

$ julia hello.jl

The previous line assumes that you have Julia properly installed in the system and that it's usable from the terminal. In UNIX systems (Linux and Mac), the Julia binary needs to be in one of the directories listed in the PATH environment variable. To check that Julia is properly installed, you can use

$ julia --version

If this runs without error and you see a version number, you are good to go!

Note

In this tutorial, when a code snipped starts with $, it should be run in the terminal. Otherwise, the code is to be run in the Julia REPL.

Tip

Avoid calling Julia code from the terminal, use the Julia REPL instead! Each time you call Julia from the terminal, you start a fresh Julia session and Julia will need to compile your code from scratch. This can be time consuming for large projects. In contrast, if you execute code in the REPL, Julia will compile code incrementally, which is much faster. Running code in a cluster (like in DAS-5 for the Julia assignment) is among the few situations you need to run Julia code from the terminal. Visit this link (Julia workflow tips) from the official Julia documentation for further information about how to develop Julia code effectivelly.

Running parallel code

Since we are in a parallel computing course, let's run a parallel "Hello world" example in Julia. Open a Julia REPL and write

julia> using Distributed
+Getting started · XM_40017
Getting started
Introduction
The programming of this course will be done using the Julia programming language. Thus, we start by explaining how to get up and running with Julia. After studying this page, you will be able to:
  • Use the Julia REPL,
  • Run serial and parallel code,
  • Install and manage Julia packages.
Why Julia?
Courses related with high-performance computing (HPC) often use languages such as C, C++, or Fortran. We use Julia instead to make the course accessible to a wider set of students, including the ones that have no experience with  C/C++ or Fortran, but are willing to learn parallel programming. Julia is a relatively new programming language specifically designed for scientific computing. It combines a high-level syntax close to interpreted languages like Python with the performance of compiled languages like C, C++, or Fortran. Thus, Julia will allow us to write efficient parallel algorithms with a syntax that is convenient in a teaching setting. In addition, Julia provides easy access to different programming models to write distributed algorithms, which will be useful to learn and experiment with them.
Tip
You can run the code in this link to learn how Julia compares to other languages (C and Python) in terms of performance.
Installing Julia
This is a tutorial-like page. Follow these steps before you continue reading the document.
The Julia REPL
Starting Julia
There are several ways of opening Julia depending on your operating system and your IDE, but it is usually as simple as launching the Julia app. With VSCode, open a folder (File > Open Folder). Then,  press Ctrl+Shift+P to open the command bar, and execute Julia: Start REPL. If this does not work, make sure you have the Julia extension for VSCode installed. Independently of the method you use, opening Julia results in a window with some text ending with:
julia>
You have just opened the Julia read-evaluate-print loop, or simply the Julia REPL. Congrats! You will spend most of time using the REPL, when working in Julia. The REPL is a console waiting for user input. Just as in other consoles, the string of text right before the input area (julia> in the case) is called the command prompt or simply the prompt.
Basic usage
The usage of the REPL is as follows:
  • You write some input
  • press enter
  • you get the output
For instance, try this
julia> 1 + 1
A "Hello world" example looks like this in Julia
julia> println("Hello, world!")
Try to run it in the REPL.
Help mode
Curious about what the function println does? Enter into help mode to look into the documentation. This is done by typing a question mark (?) into the input field:
julia> ?
After typing ?, the command prompt changes to help?>. It means we are in help mode. Now, we can type a function name to see its documentation.
help?> println
Package and shell modes
The REPL comes with two more modes, namely package and shell modes. To enter package mode type
julia> ]
Package mode is used to install and manage packages. We are going to discuss the package mode in greater detail later. To return back to normal mode press the backspace key several times.
To enter shell mode type semicolon (;)
julia> ;
The prompt should have changed to shell> indicating that we are in shell mode. Now you can type commands that you would normally do on your system command line. For instance,
shell> ls
will display the contents of the current folder in Mac or Linux. Using shell mode in Windows is not straightforward, and thus not recommended for beginners.
Running Julia code
Running more complex code
Real-world Julia programs are not typed in the REPL in practice. They are written in one or more files and included in the REPL. To try this, create a new file called hello.jl, write the code of the "Hello world" example above, and save it. If you are using VSCode, you can create the file using File > New File > Julia File. Once the file is saved with the name hello.jl, execute it as follows
julia> include("hello.jl")
Warning
Make sure that the file "hello.jl" is located in the current working directory of your Julia session. You can query the current directory with function pwd(). You can change to another directory with function cd() if needed.  Also, make sure that the file extension is .jl.
The recommended way of running Julia code is using the REPL as we did. But it is also possible to run code directly from the system command line. To this end, open a terminal and call Julia followed by the path to the file containing the code you want to execute.
$ julia hello.jl
The previous line assumes that you have Julia properly installed in the system and that it's usable from the terminal. In UNIX systems (Linux and Mac), the Julia binary needs to be in one of the directories listed in the PATH environment variable. To check that Julia is properly installed, you can use
$ julia --version
If this runs without error and you see a version number, you are good to go!
Note
In this tutorial, when a code snipped starts with $, it should be run in the terminal. Otherwise, the code is to be run in the Julia REPL.
Tip
Avoid calling Julia code from the terminal, use the Julia REPL instead! Each time you call Julia from the terminal, you start a fresh Julia session and Julia will need to compile your code from scratch. This can be time consuming for large projects. In contrast, if you execute code in the REPL, Julia will compile code incrementally, which is much faster. Running code in a cluster (like in DAS-5 for the Julia assignment) is among the few situations you need to run Julia code from the terminal. Visit this link (Julia workflow tips) from the official Julia documentation for further information about how to develop Julia code effectivelly.
Running parallel code
Since we are in a parallel computing course, let's run a parallel "Hello world" example in Julia. Open a Julia REPL and write
julia> using Distributed
 julia> @everywhere println("Hello, world! I am proc $(myid()) from $(nprocs())")
Here, we are using the Distributed package, which is part of the Julia standard library that provides distributed memory parallel support. The code prints the process id and the number of processes in the current Julia session.
You will probably only see output from 1 process. We need to add more processes to run the example in parallel. This is done with the addprocs function.
julia> addprocs(3)
We have added 3 new processes. Plus the old one, we have 4 processes. Run the code again.
julia> @everywhere println("Hello, world! I am proc $(myid()) from $(nprocs())")
Now, you should see output from 4 processes.
It is possible to specify the number of processes when starting Julia from the terminal with the -p argument (useful, e.g., when running in a cluster).  If you launch Julia from the terminal as
$ julia -p 3
and then run
julia> @everywhere println("Hello, world! I am proc $(myid()) from $(nprocs())")
You should get output from 4 processes as before.
Installing packages
One of the most useful features of Julia is its package manager. It allows one to install Julia packages in a straightforward and platform independent way. To illustrate this, let us consider the following parallel "Hello world" example. This example uses the Message Passing Interface (MPI). We will learn more about MPI later in the course.
Copy the following block of code into a new file named "hello_mpi.jl"
# file hello_mpi.jl
 using MPI
 MPI.Init()
@@ -13,4 +13,4 @@ julia> DataFrame(a=[1,2],b=[3,4])
You should get an error or a
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"

Copy the contents of previous code block into a file called Project.toml and place it in an empty folder named newproject. It is important that the file is named Project.toml. You can create a new folder from the REPL with

julia> mkdir("newproject")

To install all the packages registered in this file you need to activate the folder containing your Project.toml file

(@v1.10) pkg> activate newproject

and then instantiating it

(newproject) pkg> instantiate

The instantiate command will download and install all listed packages and their dependencies in just one click.

Getting help in package mode

You can get help about a particular package operator by writing help in front of it

(@v1.10) pkg> help activate

You can get an overview of all package commands by typing help alone

(@v1.10) pkg> help

Package operations in Julia code

In some situations it is required to use package commands in Julia code, e.g., to automatize installation and deployment of Julia applications. This can be done using the Pkg package. For instance

julia> using Pkg
-julia> Pkg.status()

is equivalent to calling status in package mode.

(@v1.10) pkg> status

Conclusion

We have learned the basics of how to work with Julia. If you want to further dig into the topics we have covered here, you can take a look at the following links:

+julia> Pkg.status()

is equivalent to calling status in package mode.

(@v1.10) pkg> status

Conclusion

We have learned the basics of how to work with Julia. If you want to further dig into the topics we have covered here, you can take a look at the following links:

diff --git a/dev/index.html b/dev/index.html index 4d77c84..d6ebd31 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,5 +1,5 @@ -Home · XM_40017

Programming Large-Scale Parallel Systems (XM_40017)

Welcome to the interactive lecture notes of the Programming Large-Scale Parallel Systems course at VU Amsterdam!

What

This page contains part of the course material of the Programming Large-Scale Parallel Systems course at VU Amsterdam. We provide several lecture notes in jupyter notebook format, which will help you to learn how to design, analyze, and program parallel algorithms on multi-node computing systems. Further information about the course is found in the study guide (click here) and our Canvas page (for registered students).

Note

Material will be added incrementally to the website as the course advances.

Warning

This page will eventually contain only a part of the course material. The rest will be available on Canvas. In particular, the material in this public webpage does not fully cover all topics in the final exam.

How to use this page

You have two main ways of studying the notebooks:

  • Download the notebooks and run them locally on your computer (recommended). At each notebook page you will find a green box with links to download the notebook.
  • You also have the static version of the notebooks displayed in this webpage for quick reference.

How to run the notebooks locally

To run a notebook locally follow these steps:

  • Install Julia (if not done already). More information in Getting started.
  • Download the notebook.
  • Launch Julia. More information in Getting started.
  • Execute these commands in the Julia command line:
julia> using Pkg
+Home · XM_40017
Programming Large-Scale Parallel Systems (XM_40017)
Welcome to the interactive lecture notes of the Programming Large-Scale Parallel Systems course at VU Amsterdam!
What
This page contains part of the course material of the Programming Large-Scale Parallel Systems course at VU Amsterdam. We provide several lecture notes in jupyter notebook format, which will help you to learn how to design, analyze, and program parallel algorithms on multi-node computing systems. Further information about the course is found in the study guide (click here) and our Canvas page (for registered students).
Note
Material will be added incrementally to the website as the course advances.
Warning
This page will eventually contain only a part of the course material. The rest will be available on Canvas. In particular, the material in this public webpage does not fully cover all topics in the final exam.
How to use this page
You have two main ways of studying the notebooks:
  • Download the notebooks and run them locally on your computer (recommended). At each notebook page you will find a green box with links to download the notebook.
  • You also have the static version of the notebooks displayed in this webpage for quick reference.
How to run the notebooks locally
To run a notebook locally follow these steps:
  • Install Julia (if not done already). More information in Getting started.
  • Download the notebook.
  • Launch Julia. More information in Getting started.
  • Execute these commands in the Julia command line:
julia> using Pkg
 julia> Pkg.add("IJulia")
 julia> using IJulia
-julia> notebook()
  • These commands will open a jupyter in your web browser. Navigate in jupyter to the notebook file you have downloaded and open it.
Authors
This material is created by Francesc Verdugo with the help of Gelieza Kötterheinrich. Part of the notebooks are based on the course slides by Henri Bal.
License
All material on this page that is original to this course may be used under a CC BY 4.0 license.
Acknowledgment
This page was created with the support of the Faculty of Science of Vrije Universiteit Amsterdam in the framework of the project "Interactive lecture notes and exercises for the Programming Large-Scale Parallel Systems course" funded by the "Innovation budget BETA 2023 Studievoorschotmiddelen (SVM) towards Activated Blended Learning".

+julia> notebook()
  • These commands will open a jupyter in your web browser. Navigate in jupyter to the notebook file you have downloaded and open it.

Authors

This material is created by Francesc Verdugo with the help of Gelieza Kötterheinrich. Part of the notebooks are based on the course slides by Henri Bal.

License

All material on this page that is original to this course may be used under a CC BY 4.0 license.

Acknowledgment

This page was created with the support of the Faculty of Science of Vrije Universiteit Amsterdam in the framework of the project "Interactive lecture notes and exercises for the Programming Large-Scale Parallel Systems course" funded by the "Innovation budget BETA 2023 Studievoorschotmiddelen (SVM) towards Activated Blended Learning".

diff --git a/dev/jacobi_2D/index.html b/dev/jacobi_2D/index.html index da3d4a9..6a4b1a6 100644 --- a/dev/jacobi_2D/index.html +++ b/dev/jacobi_2D/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+- · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/jacobi_method/index.html b/dev/jacobi_method/index.html index f748556..78b9ad7 100644 --- a/dev/jacobi_method/index.html +++ b/dev/jacobi_method/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+- · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_async/index.html b/dev/julia_async/index.html index e73f6de..ff29016 100644 --- a/dev/julia_async/index.html +++ b/dev/julia_async/index.html @@ -1,10 +1,10 @@ -Asynchronous programming in Julia · XM_40017
+Asynchronous programming in Julia · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_basics/index.html b/dev/julia_basics/index.html index 82d1463..c5abdef 100644 --- a/dev/julia_basics/index.html +++ b/dev/julia_basics/index.html @@ -1,10 +1,10 @@ -Julia Basics · XM_40017
+Julia Basics · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_distributed/index.html b/dev/julia_distributed/index.html index 1ae71c9..efb3b65 100644 --- a/dev/julia_distributed/index.html +++ b/dev/julia_distributed/index.html @@ -1,10 +1,10 @@ -Distributed computing in Julia · XM_40017
+Distributed computing in Julia · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_intro/index.html b/dev/julia_intro/index.html index 02db22d..878bd1e 100644 --- a/dev/julia_intro/index.html +++ b/dev/julia_intro/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+- · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_jacobi/index.html b/dev/julia_jacobi/index.html index eebde6a..fc5b62b 100644 --- a/dev/julia_jacobi/index.html +++ b/dev/julia_jacobi/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+- · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_mpi.ipynb b/dev/julia_mpi.ipynb index 9fdd89f..d8042df 100644 --- a/dev/julia_mpi.ipynb +++ b/dev/julia_mpi.ipynb @@ -15,7 +15,7 @@ "id": "4ac1e5d9", "metadata": {}, "source": [ - "# Distributed computing with MPI" + "# Intro to MPI (point-to-point)" ] }, { @@ -28,11 +28,10 @@ "\n", "In this notebook, we will learn the basics of parallel computing using the Message Passing Interface (MPI) from Julia. In particular, we will learn:\n", "\n", - "- How to run parallel MPI code in Julia\n", - "- How to use basic collective communication directives\n", - "- How to use basic point-to-point communication directives\n", - "\n", - "For further information on how to use MPI from Julia see https://github.com/JuliaParallel/MPI.jl\n" + "- How to use point-to-point communication directives\n", + "- Which are the pros and cons of several types of send and receive functions\n", + "- Which are the common pitfalls when using point-to-point directives\n", + "\n" ] }, { @@ -51,6 +50,19 @@ "- There are Julia bindings via the package MPI.jl https://github.com/JuliaParallel/MPI.jl" ] }, + { + "cell_type": "markdown", + "id": "7c31907f", + "metadata": {}, + "source": [ + "### Before starting this notebook\n", + "\n", + "Read this paper to get an overview of the history and rationale behind MPI:\n", + "\n", + "J.J. Dongarra, S.W. Otto, M. Snir, and D. Walker, David. A message passing standard for MPP and workstations, *Commun. ACM*, 39(7), 84–90, 1996. DOI: [10.1145/233977.234000](https://doi.org/10.1145/233977.234000).\n", + "\n" + ] + }, { "cell_type": "markdown", "id": "99c6febb", @@ -58,10 +70,14 @@ "source": [ "### What is MPI.jl ?\n", "\n", - "- It is not a Julia implementation of the MPI standard\n", - "- It is a wrapper to the C interface of MPI\n", - "- You need a C MPI installation in your system\n", + "We will access MPI via the Julia bindings provided by the [`MPI.jl`]( https://github.com/JuliaParallel/MPI.jl) package. It is worth noting that:\n", "\n", + "- MPI is not a Julia implementation of the MPI standard\n", + "- It is just a wrapper to the C interface of MPI.\n", + "- You need a C MPI installation in your system (MPI.jl downloads one for you when needed).\n", + "\n", + "\n", + "### Why MPI.jl?\n", "\n", "MPI.jl provides a convenient Julia API to access MPI. For instance, this is how you get the id (rank) of the current process.\n", "\n", @@ -83,22 +99,20 @@ }, { "cell_type": "markdown", - "id": "82e6e98f", + "id": "c6c44e2d", "metadata": {}, "source": [ - "### Installing MPI in Julia\n", - "\n", - "The Jupyter Julia kernel installed by IJulia activates the folder where the notebook is located as the default environment, which causes the main process and the worker processes to not share the same environment. Therefore, we need to set the environment as the global environment." + "If you are curious, run next cell to get more information about how `ccall` works." ] }, { "cell_type": "code", "execution_count": null, - "id": "705c2439-c805-4818-be73-342182f7b7a0", + "id": "f1f4c046", "metadata": {}, "outputs": [], "source": [ - "] activate" + "? ccall" ] }, { @@ -106,6 +120,8 @@ "id": "e99c7676-989e-4e91-b65e-ebca2d5626a4", "metadata": {}, "source": [ + "### Installing MPI in Julia\n", + "\n", "MPI can be installed as any other Julia package using the package manager." ] }, @@ -125,7 +141,7 @@ "metadata": {}, "source": [ "
\n", - "Note: The package you have installed it is the Julia interface to MPI, called MPI.jl. Note that it is not a MPI library by itself. It is just a thin wrapper between MPI and Julia. To use this interface, you need an actual MPI library installed in your system such as OpenMPI or MPICH. Julia downloads and installs a MPI library for you, but it is also possible to use a MPI library already available in your system. This is useful, e.g., when running on HPC clusters. See the documentation of MPI.jl for further details. See more information in https://github.com/JuliaParallel/MPI.jl\n", + "Note: Julia will download and install a MPI library for you, but it is also possible to use a MPI library already available in your system. This is useful, e.g., when running on HPC clusters. See the documentation of MPI.jl for further details. See more information in https://github.com/JuliaParallel/MPI.jl\n", "
" ] }, @@ -134,7 +150,9 @@ "id": "9db130db", "metadata": {}, "source": [ - "## Minimal MPI program" + "## Minimal MPI program\n", + "\n", + "An MPI program must, at least, initialize and then finalize MPI. Finalizing MPI in the user code is optional in Julia as there is an internal mechanism that will finalize MPI for your. You can deactivate this mechanism by initializing MPI with `MPI.Init(finalize_atexit=false)`. Once finalized, MPI procedures cannot be used anymore except very few exceptions." ] }, { @@ -143,14 +161,22 @@ "metadata": {}, "source": [ "\n", - "In Julia:\n", + "In Julia (option 1, recommended):\n", "\n", "```julia\n", "using MPI\n", "MPI.Init()\n", "# Your MPI programm here\n", - "# ...\n", - "MPI.Finalize()\n", + "MPI.Finalize() # Optional\n", + "```\n", + "\n", + "In Julia (option 2, advanced):\n", + "\n", + "```julia\n", + "using MPI\n", + "MPI.Init(finalize_atexit=false)\n", + "# Your MPI programm here\n", + "MPI.Finalize() # Mandatory\n", "```\n", "\n", "In C:\n", @@ -166,13 +192,7 @@ "\n", "
\n", "Note: Note that the Julia syntax is almost 1-to-1 to the C one. The key difference is that in Julia MPI routines are written as `MPI.X` where in C are written `MPI_X`.\n", - "
\n", - "\n", - "\n", - "* It is mandatory to initialize MPI before using MPI procedures.\n", - "* In C, all processes must call `MPI_Finalize` before exiting.\n", - "* In Julia, either all or none process must call `MPI.Finalize()`.\n", - "* Once finalized, MPI cannot be re" + "" ] }, { @@ -182,14 +202,16 @@ "source": [ "### An incorrect MPI program\n", "\n", + "It is very easy to end up with incorrect programs when using MPI if you are not careful following the rules. In this notebook, we will see many examples. For instance:\n", + "\n", "```julia\n", "using MPI\n", - "MPI.Init()\n", + "MPI.Init(;finalize_atexit=false)\n", "@assert rand(1:10) != 2\n", "MPI.Finalize()\n", "```\n", "\n", - "In some process `rand(1:10)` might be 2 and the program will stop without reaching `MPI.Finalize()`." + "In some process `rand(1:10)` might be 2 and the program will stop without reaching `MPI.Finalize()` leading to an incorrect program." ] }, { @@ -205,7 +227,7 @@ "id": "4a8ebeff", "metadata": {}, "source": [ - "Premature finalization of a program is done with `MPI.Abort`.\n", + "Premature finalization of a program is done with `MPI_Abort`.\n", "\n", "```julia\n", "using MPI\n", @@ -217,7 +239,11 @@ "MPI.Finalize()\n", "```\n", "\n", - "* There is no need to call `MPI.Abort` in all ranks." + "There is no need to call `MPI_Abort` in all ranks. The MPI library will do its best to stop all MPI ranks even if `MPI_Abort` was called just on a single rank.\n", + "\n", + "
\n", + "Note: Processes are often called ranks, when working with MPI.\n", + "
" ] }, { @@ -227,7 +253,7 @@ "source": [ "### Read the docs\n", "\n", - "Not sure if an MPI routine needs to be called by all the ranks? Read the documentation, and/or the MPI standard." + "Not sure if an MPI routine needs to be called by all ranks? Read the documentation, specially the external links. Run the next cell, open an external link and read the documentation." ] }, { @@ -247,7 +273,7 @@ "source": [ "## Basic information about MPI processes\n", "\n", - "The following cells give information about MPI processes, such as the rank id, the total number of processes and the name of the host running the code respectively. Before calling this functions one needs to initialize MPI." + "The following cells give information about MPI processes, such as the rank id, the total number of processes and the name of the host running the code. Before calling this functions one needs to initialize MPI." ] }, { @@ -261,6 +287,16 @@ "MPI.Init()" ] }, + { + "cell_type": "markdown", + "id": "48f89e38", + "metadata": {}, + "source": [ + "
\n", + "Note: If you get some error or warning when initializing MPI, make sure that you started the notebook using `IJulia`. Go back to the first notebook for specific details.\n", + "
" + ] + }, { "cell_type": "markdown", "id": "fe202985", @@ -280,6 +316,16 @@ "rank = MPI.Comm_rank(comm)" ] }, + { + "cell_type": "markdown", + "id": "cb480322", + "metadata": {}, + "source": [ + "
\n", + "Note: Note that the rank ids start with 0.\n", + "
" + ] + }, { "cell_type": "markdown", "id": "dd40d1dc", @@ -321,7 +367,7 @@ "id": "f1a502a3", "metadata": {}, "source": [ - "Note that this note notebook is not running with different MPI processes (yet). So using MPI will only make sense later when we add more processes." + "Note that this note notebook is running on a single process. So using MPI will only make sense later when we add more processes." ] }, { @@ -331,7 +377,7 @@ "source": [ "### Hello-world example\n", "\n", - "Using these functions we can create the a classic MPI hello world example. This example (or variations thereof) is used in practice to check how MPI ranks are mapped to available processing units in a given computing system.\n" + "Using these functions we can create the a classic MPI hello world example. This example (or variations thereof) are used in practice to check how MPI ranks are mapped to available processing units in a given computing system.\n" ] }, { @@ -357,6 +403,8 @@ "source": [ "### Hello world in C\n", "\n", + "This is the equivalent code in C.\n", + "\n", "```C\n", "#include \n", "#include \n", @@ -391,10 +439,11 @@ "source": [ "### Creating MPI processes (aka ranks)\n", "\n", - "- MPI processes are created with the driver program `mpiexec`\n", - "- `mpiexec` takes an application and runs it on different ranks\n", - "- The application calls MPI directives to communicate between these ranks\n", - "- The application can be Julia running your script in particular.\n" + "MPI processes are created with the driver program `mpiexec`, which comes as part of an MPI installation:\n", + "\n", + "- `mpiexec` takes an application as argument, it creates different processes, and runs the application on these processes.\n", + "- The application will typically call MPI directives to communicate between these ranks\n", + "- In particular, the application can be `julia code.jl`, i.e., Julia running a given script `code.jl`.\n" ] }, { @@ -419,8 +468,8 @@ "source": [ "### Execution model\n", "\n", - "- MPI programs are typically run with a Single Program Multiple Data (SPMD) model\n", - "- But the standard supports Multiple Program Multiple Data (MPMD)" + "- MPI programs are typically run with a Single Program Multiple Data (SPMD) model, which is a particular case of the SIMD model.\n", + "- But the standard supports Multiple Program Multiple Data (MPMD), which is a particular case of the MIMD model." ] }, { @@ -428,9 +477,9 @@ "id": "5f76fb65", "metadata": {}, "source": [ - "### Hello world\n", + "### Hello world example\n", "\n", - "Julia code typically needs to be in a file to run it in with MPI. Let's us write our hello world in a file:" + "Julia code typically needs to be in a file to run it in with MPI. Let's us write our hello world in a file. The next cell will create a file, and write some code in it." ] }, { @@ -450,7 +499,8 @@ "MPI.Finalize()\n", "\"\"\"\n", "filename = tempname()*\".jl\"\n", - "write(filename,code);" + "write(filename,code)\n", + "filename" ] }, { @@ -482,16 +532,6 @@ "" ] }, - { - "cell_type": "markdown", - "id": "cb480322", - "metadata": {}, - "source": [ - "
\n", - "Note: Note that the rank ids start with 0.\n", - "
" - ] - }, { "cell_type": "markdown", "id": "a958c015", @@ -499,7 +539,7 @@ "source": [ "### Another way to launch MPI code\n", "\n", - "In the Hello world example above we have created an auxiliary file to run the code with MPI. This can be annoying specially if you are working in a jupyter notebook. With this other syntax you can skip creating the auxiliary file. we use `quote` which is part of the [meta-programming capabilities of Julia](https://docs.julialang.org/en/v1/manual/metaprogramming/). " + "In the hello world example above we have created an auxiliary file to run the code with MPI. This can be annoying specially if you are working in a jupyter notebook. With this other syntax you can skip creating the auxiliary file. We use `quote` which is part of the [meta-programming capabilities of Julia](https://docs.julialang.org/en/v1/manual/metaprogramming/). " ] }, { @@ -526,7 +566,7 @@ "id": "5ec1c52a", "metadata": {}, "source": [ - "### Data availability" + "## Data availability" ] }, { @@ -534,7 +574,9 @@ "id": "c8a5e099", "metadata": {}, "source": [ - "Note that mpiexec creates new processes which are different from the process running this notebook. In particular, these new processes will not see any variables or function definitions in the current notebook. So, the full MPI program needs to be in the source file passed to Julia or the quote block." + "Note that mpiexec creates new processes which are different from the process running this notebook. In particular, these new processes will not see any variables or function definitions in the current notebook. So, the full MPI program needs to be in the source file passed to Julia or the quote block.\n", + "\n", + "The following cell will fail. Note that the same error message is displayed by all ranks, which makes difficult to read them." ] }, { @@ -558,7 +600,7 @@ "id": "4b455f98", "metadata": {}, "source": [ - "So, the full MPI program needs to be in the source file passed to Julia or the quote block. In practice, long MPI programms are written as Julia packages using several files, which are then loaded by each MPI process. " + "So, the full MPI program needs to be in the source file passed to Julia or the quote block. In practice, long MPI programms are written as Julia packages using several files, which are then loaded by each MPI process. For our simple example, we just need to include the definition of `foo` inside the quote block." ] }, { @@ -584,13 +626,23 @@ "source": [ "## Point-to-point communication\n", "\n", - "MPI provides point-to-point communication directives for arbitrary communication between processes. Point-to-point communications are two-sided: there is a sender and a receiver. Here, we will discuss these basic directives:\n", + "Now we are up and running, and ready to start learning MPI communication primitives. In this notebook we will cover so-called point-to-point communication directives. In a later notebook we will also learn about collective primitives.\n", "\n", - "- `MPI_Send`, and `MPI_Recv!` (*blocking directives*)\n", - "- `MPI_Isend`, and `MPI_Irecv!` (*non-blocking directives, aka incomplete directives*)\n", + "MPI provides point-to-point communication directives for arbitrary communication between processes. Point-to-point communications are two-sided: there is a sender and a receiver. Here, we will discuss different types of directives:\n", + "\n", + "- `MPI_Send`, and `MPI_Recv` (*blocking directives*)\n", + "- `MPI_Isend`, and `MPI_Irecv` (*non-blocking directives*)\n", "- `MPI_Bsend`, `MPI_Ssend`, and `MPI_Rsend` (*advanced communication modes*)" ] }, + { + "cell_type": "markdown", + "id": "0e515109", + "metadata": {}, + "source": [ + "In all cases, these functions are used to send a message from a ranks and receive it in another rank. See next picture." + ] + }, { "attachments": { "fig_p2p.png": { @@ -613,6 +665,8 @@ "source": [ "## Blocking send and receive\n", "\n", + "Let's start with the \"standard\" blocking send and receive.\n", + "\n", "In Julia:\n", "\n", "```julia\n", @@ -627,13 +681,13 @@ " int source, int tag, MPI_Comm comm, MPI_Status *status)\n", "```\n", "\n", - "Key arguments:\n", + "The key arguments are:\n", "\n", "* `sndbuf` data to send.\n", - "* `rcvbuf` space to store the received data.\n", + "* `rcvbuf` space to store the incoming data.\n", "* `source` rank of the sender.\n", "* `dest` rank of the receiver.\n", - "* `tag`. Might be used to distinguish between different kinds of messages (i.e., the \"subject\" in an email)\n" + "* `tag`. Might be used to distinguish between different kinds of messages from the same sender to the same receiver (similar to the \"subject\" in an email).\n" ] }, { @@ -649,7 +703,7 @@ "id": "c2301992", "metadata": {}, "source": [ - "Send 5 integers from rank 2 to rank 3." + "Send 5 integers from rank 2 to rank 3. Note that the same code will be executed in all ranks by `mpiexec`. This is why we need to use if statements to select specific ranks." ] }, { @@ -669,7 +723,7 @@ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n", " end\n", " if rank == 3\n", - " rcvbuf = zeros(Int,5)\n", + " rcvbuf = zeros(Int,5) # Allocate space for the incoming message\n", " MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0)\n", " @show rcvbuf\n", " end\n", @@ -682,7 +736,7 @@ "id": "ee680aa5", "metadata": {}, "source": [ - "### Any source, any tag\n", + "## Any source, any tag\n", "\n", "We can use\n", "\n", @@ -710,7 +764,7 @@ "id": "2add5065", "metadata": {}, "source": [ - "Send 5 integers from rank 2 to rank 3." + "Send 5 integers from rank 2 to rank 3. Since there is only one sender and only one message from this sender, we can receive from any source and with any tag." ] }, { @@ -745,7 +799,7 @@ "id": "7833da72", "metadata": {}, "source": [ - "### Who was the sender? Which was the tag?\n", + "## MPI_Status\n", "\n", "When using `MPI.ANY_SOURCE` and `MPI.ANY_TAG` it might be still useful to know which was the sender and which tag was used. This information is given by a `MPI.Status` object.\n", "\n", @@ -759,6 +813,14 @@ "\n" ] }, + { + "cell_type": "markdown", + "id": "4618ad58", + "metadata": {}, + "source": [ + "### Example" + ] + }, { "cell_type": "code", "execution_count": null, @@ -793,20 +855,24 @@ "id": "590bc407", "metadata": {}, "source": [ - "### Which is the incoming message size?\n", + "## MPI_Probe\n", "\n", - "Note that we need to provide a receive buffer with the right size, but it general we might do not know which is the size of the incoming message. This can be solved using an `MPI_Probe`. It works similar to `MPI_Recv`, but instead of receiving the message only receives information about the message (source, tag, and also message size).\n", + "Note that we need to provide a receive buffer with the right size, but it general we do not know which is the size of the incoming message. This can be solved using an `MPI_Probe`. It works similar to `MPI_Recv`, but instead of receiving the message only receives information about the message (source, tag, and also message size). Like for `MPI_Recv`, `MPI_Probe` will wait (block) until a matching send is posted. More information about the blocking behavior will be given below.\n", "\n", + "\n", + "`MPI_Probe` provides an `MPI_Status` object. We can get the message size from the status object using function `MPI_Get_count`. We can also get the source and tag from the status object as shown before.\n", + "\n", + "In Julia:\n", "```julia\n", "status = MPI.Probe(comm,MPI.Status; source, tag)\n", "count = MPI.Get_count(status, T)\n", "```\n", + "In C:\n", "```C\n", "int MPI_Probe(int source, int tag, MPI_Comm comm, MPI_Status *status)\n", "int MPI_Get_count(const MPI_Status *status, MPI_Datatype datatype, int *count)\n", "```\n", - "\n", - "We can get the message size from the status object using function `MPI_Get_count`. We can also get the source and tag from the status object as shown before." + "\n" ] }, { @@ -837,9 +903,9 @@ " source = MPI.ANY_SOURCE\n", " tag = MPI.ANY_TAG\n", " status = MPI.Probe(comm,MPI.Status; source, tag)\n", - " count = MPI.Get_count(status,Int)\n", + " count = MPI.Get_count(status,Int) # Get incomming message length\n", " println(\"I am about to receive $count integers.\")\n", - " rcvbuf = zeros(Int,count) \n", + " rcvbuf = zeros(Int,count) # Allocate \n", " MPI.Recv!(rcvbuf, comm, MPI.Status; source, tag)\n", " @show rcvbuf\n", " end\n", @@ -849,19 +915,27 @@ }, { "cell_type": "markdown", - "id": "04bdb01f", + "id": "d0add95c", "metadata": {}, "source": [ - "### Blocking semantics\n", + "## Complete operations\n", "\n", - "Functions `MPI_Send` and `MPI_Recv` are *blocking*.\n", + "Functions `MPI_Send`, `MPI_Recv`, and `MPI_Probe` are *complete* operations, meaning that their argument can be used once the function returns.\n", "\n", - "This means:\n", + "In particular:\n", "\n", - "- It is safe to re-write the send buffer once `MPI_Send` returns.\n", + "- It is safe to re-write the send buffer with new data once `MPI_Send` returns.\n", "- The received message is guaranteed to be fully available in the receive buffer once `MPI_Recv` returns." ] }, + { + "cell_type": "markdown", + "id": "ed0c4d76", + "metadata": {}, + "source": [ + "### Example" + ] + }, { "cell_type": "code", "execution_count": null, @@ -891,13 +965,16 @@ }, { "cell_type": "markdown", - "id": "245893cf", + "id": "04bdb01f", "metadata": {}, "source": [ - "However:\n", - "- We cannot assume synchronization between sender and receiver. I.e., *blocking* is not the same as *synchronous*. We cannot assume that a receive has been posted once `MPI_Send` returns as the underlying implementation might copy the send message into an internal buffer and return before any matching `MPI_Recv` started.\n", + "## Blocking operations\n", "\n", - "- A blocking send is not *synchronous*, but we cannot assume that it is *asynchronous*. The underlying implementation might not use any auxiliary buffer and wait for a matching receive. Assuming buffering is erroneous and can lead to dead locks." + "`MPI_Recv` and `MPI_Probe` are *blocking* operations. This means that they will wait for a matching send.\n", + "\n", + "\n", + "\n", + "`MPI_Send` is also often called a blocking send, but this is very misleading. `MPI_Send` might or not wait for a matching `MPI_Recv`. Look into the following example (which in fact is an incorrect MPI program)." ] }, { @@ -907,7 +984,7 @@ "source": [ "### Incorrect program\n", "\n", - "The following program will or will not work depending whether the underlying implementation uses buffering. On my laptop, it works with `n=1`, but leads to a dead lock when `n=10000`. The MPI implementation decided to buffer or not depending on the message size." + "The following program will or will not work depending on the underlying implementation and other factors (i.e. this program has undefined behavior). On my laptop, it works with `n=1`, but leads to a dead lock when `n=10000`." ] }, { @@ -940,12 +1017,39 @@ }, { "cell_type": "markdown", - "id": "8b7eeec2", + "id": "42402ec1", "metadata": {}, "source": [ - "### Correct program\n", + "
\n", + "Tip: You can stop the cell from runnig with Kernel > Interrupt. You might need to click in Interrupt 2 times.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "94c39af5", + "metadata": {}, + "source": [ + "## To buffer or not to buffer outbound messages?\n", "\n", - "We can fix the program by smartly ordering the sends and the receives. This should work for any value of `n` as long as we have enough memory in the system." + "There are two key competing requirements that face implementations of `MPI_Send`.\n", + "\n", + "1. One might want to minimize synchronization time. This is often achieved by copying the outgoing message in an internal buffer and returning from the `MPI_Send` as soon as possible, without waiting for a matching `MPI_Recv`.\n", + "2. One might want to avoid data copies (e.g. for large messages). In this case, one needs to wait for a matching receive and return from the `MPI_Send` when the data has been sent.\n", + "\n", + "Thus, there is a trade-off between memory copied (buffering) and synchronization (wait) time. One cannot minimize both at the same time." + ] + }, + { + "cell_type": "markdown", + "id": "b2cb0a43", + "metadata": {}, + "source": [ + "## Fixing cyclic dependencies\n", + "\n", + "Returning to the incorrect program seen before. Remember that on my laptop it worked for `n=1`, but it failed for `n=10000`. For `n=1` the MPI implementation provably decided that the best trade-off was to buffer the small message and return from the `MPI_Send` as soon as possible. For the large message (`n=10000`) it provably decided to wait for a matching receive in ordeer to avoid copying data. In this case, both ranks were blocked on the call to `MPI_Send` and the receives were never posted leading to a dead lock.\n", + "\n", + "We can fix these cyclic dependences by smartly ordering the sends and the receives:" ] }, { @@ -964,10 +1068,12 @@ " sndbuf = fill(rank,n)\n", " rcvbuf = zeros(Int,n)\n", " if rank == 2\n", + " # We first send and then receive\n", " MPI.Send(sndbuf, comm; dest=3, tag=0)\n", " MPI.Recv!(rcvbuf, comm, MPI.Status; source=3, tag=0)\n", " end\n", " if rank == 3\n", + " # We first receive and then send\n", " MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0)\n", " MPI.Send(sndbuf, comm; dest=2, tag=0)\n", " end\n", @@ -981,7 +1087,7 @@ "id": "2b37ed85", "metadata": {}, "source": [ - "Another solution is to use `MPI_Sendrecv`." + "Another solution is to use `MPI_Sendrecv`, which will handle the ordering for us." ] }, { @@ -1012,77 +1118,153 @@ }, { "cell_type": "markdown", - "id": "4fca501c", + "id": "228a2067", "metadata": {}, "source": [ "## Communication modes\n", "\n", - "In all cases, it is safe to reuse the send buffer once the corresponding send returns. I.e., all the following sends are *complete* MPI operations. However, there are some important differences.\n", - "\n", - "### Standard\n", - "\n", - "* `MPI_Send` \n", - "* Programmer cannot make any assumptions whether the message is buffered or synchronous.\n", - "* This is up to the system.\n", - "\n", - "### Buffered\n", - "\n", - "* `MPI_Bsend`\n", - "* Programmer provides additional internal buffer space with function `MPI_Buffer_attach`.\n", - "* `MPI_Bsend` completes when message is copied into a local buffer.\n", - "* Erroneous if buffer space is insufficient.\n", - "\n", - "### Synchronous\n", - "\n", - "* `MPI_Ssend`\n", - "* The send will only return if a matching receive was posted.\n", - "* No buffering, no extra copy, but easy to get deadlocks\n", - "* It can be started whether or not a matching receive was posted.\n", - "\n", - "### Ready\n", - "\n", - "* `MPI_Rsend`\n", - "* It may be started only if the matching receive is already posted.\n", - "* Erroneous if there is no matching receive yet.\n", - "* Otherwise, same as an `MPI_Ssend`.\n", + "What if we as users want to decide when to buffer a message or when to avoid data copies? This is provided by so-called *communication* modes. There are four modes: standard, buffered, synchronous, and ready provided by the respective functions `MPI_Send`, `MPI_Bsend`, `MPI_Ssend`, and `MPI_Rsend`, respectively. \n", "\n", "\n", - " All these send types are matched with a `MPI_Recv`. I.e., there is no `MPI_Brecv`, `MPI_Srecv`, `MPI_Rrecv`. For further information about the communication modes, refer to [this section](https://www.mpi-forum.org/docs/mpi-2.2/mpi22-report/node53.htm) of the MPI standard.\n", - " \n", + "All these MPI functions are *complete* MPI operations, and therefore, it is safe to re-write the send buffer once the corresponding send returns. All these send types are matched with an `MPI_Recv`. I.e., there is no `MPI_Brecv`, `MPI_Srecv`, `MPI_Rrecv`. " + ] + }, + { + "cell_type": "markdown", + "id": "4fca501c", + "metadata": {}, + "source": [ " \n", "
\n", "Note: `MPI_Bsend`, `MPI_Ssend`, and `MPI_Rsend` are not exposed in the Julia bindings via a high-level interface like for `MPI.Send`, but they can be accessed using the low-level bindings in the submodule `MPI.API` (not shown in this notebook).\n", "
\n", " \n", - " \n" + " " ] }, { "cell_type": "markdown", - "id": "7a935b14", + "id": "126ce58c", + "metadata": {}, + "source": [ + "### Standard mode\n", + "\n", + "* Function `MPI_Send` \n", + "* Programmer cannot make any assumptions whether the message is buffered or not. This is up to the system.\n", + "* Assuming buffering might lead to incorrect programs (as the example above).\n", + "* Assuming that a matching receive started is also incorrect." + ] + }, + { + "cell_type": "markdown", + "id": "014ef71f", + "metadata": {}, + "source": [ + "### Buffered\n", + "\n", + "* Function `MPI_Bsend`\n", + "* Programmer provides additional internal buffer space with function `MPI_Buffer_attach`.\n", + "* `MPI_Bsend` completes when message is copied into a local buffer.\n", + "* Erroneous if buffer space is insufficient.\n", + "* It does not wait for a matching receive." + ] + }, + { + "cell_type": "markdown", + "id": "bf456a3e", + "metadata": {}, + "source": [ + "### Synchronous\n", + "\n", + "* Function `MPI_Ssend`\n", + "* It waits for a matching receive and it is guaranteed that the receive started.\n", + "* No extra data copy\n", + "* Easy to get deadlocks\n", + "* It can be started whether or not a matching receive was posted." + ] + }, + { + "cell_type": "markdown", + "id": "a1bd72c3", + "metadata": {}, + "source": [ + "### Ready\n", + "\n", + "* Function `MPI_Rsend`\n", + "* It may be started only if the matching receive is already posted. This allows the underlying implementation to skip some operations, such as a [handshake](https://en.wikipedia.org/wiki/Handshake_(computing)).\n", + "* Erroneous if there is no matching receive yet.\n", + "* Otherwise, the same as the synchronous `MPI_Ssend`." + ] + }, + { + "cell_type": "markdown", + "id": "4a206008", + "metadata": {}, + "source": [ + " \n", + "For further information about the communication modes, refer to [this section](https://www.mpi-forum.org/docs/mpi-2.2/mpi22-report/node53.htm) of the MPI standard.\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "e12f3d48", "metadata": {}, "source": [ "## Non-blocking send and receive\n", "\n", - "- They return immediately.\n", - "- The send and receive is not completed when the function returns. I.e. they are *incomplete* operations.\n", - "- `MPI_Wait` used to wait for completion of the send and/or receive.\n", "\n", - "- Used to overlap communication and computation (\"latency-hiding\").\n", + "In addition to all the send and receive function seen so far, MPI also provide two extra functions: `MPI_Isend` and `MPI_Irecv`.\n", "\n", + "In Julia:\n", "\n", "```julia\n", "request = MPI.Isend(sndbuf, comm; dest, tag)\n", "request = MPI.Irecv!(rcvbuf, comm; source, tag)\n", "```\n", "\n", + "In C:\n", "```C\n", "int MPI_Isend(const void *sndbuf, int count, MPI_Datatype datatype, int dest,\n", " int tag, MPI_Comm comm, MPI_Request *request)\n", "int MPI_Irecv(void *rcvbuf, int count, MPI_Datatype datatype,\n", " int source, int tag, MPI_Comm comm, MPI_Request *request)\n", "\n", - "```" + "```\n", + "\n", + "These functions are *non-blocking* as they return immediately without waiting for a matching send or receive." + ] + }, + { + "cell_type": "markdown", + "id": "64938903", + "metadata": {}, + "source": [ + "## Incomplete operations\n", + "\n", + "Functions `MPI_Isend` and `MPI_Irecv` are *incomplete* operations, hence the `I` in `MPI_Isend` and `MPI_Irecv`. This means that, when these functions return, there is no guarantee that the underlying operation has finished. Function `MPI_Wait` should be used to wait for completion of the send and/or receive.\n", + "\n", + "In particular:\n", + "\n", + "- One needs to wait for completion before reseting the send buffer\n", + "- One needs to wait for completion before using the receive buffer\n", + "\n", + "Otherwise your program will be erroneous.\n" + ] + }, + { + "cell_type": "markdown", + "id": "c52a5959", + "metadata": {}, + "source": [ + "## Latency hiding\n", + "\n", + "One of the key benefits of using `MPI_Isend` and `MPI_Irecv` is that they allow one to overlap communication and computation, also called as latency-hiding. For instance, one can post an `MPI_Irecv` to start receiving a message, which will return without waiting for a matching send. One can do local work, while waiting for a matching send and while the message is transferred. And finally, one calls `MPI_Wait` to wait for the completion of the receive operation.\n", + "\n", + "\n", + "
\n", + "Note: `MPI_Bsend` also provides a way of doing latency hiding on the send side (at the expense of copying the message). `MPI_Isend` allows you doing latency hiding on the send side without copying data, and `MPI_Irecv` allows you doing latency hiding on the receive side.\n", + "
\n" ] }, { @@ -1111,13 +1293,13 @@ " if rank == 2\n", " sndbuf = [1,2,3,5,8]\n", " request = MPI.Isend(sndbuf, comm; dest=3, tag=0)\n", - " work()\n", + " work() # Overlap local work with communication / synchronization \n", " MPI.Wait(request)\n", " end\n", " if rank == 3\n", " rcvbuf = zeros(Int,5)\n", " request = MPI.Irecv!(rcvbuf, comm; source=2, tag=0)\n", - " work()\n", + " work() # Overlap local work with communication / synchronization\n", " MPI.Wait(request)\n", " @show rcvbuf\n", " end\n", @@ -1132,11 +1314,7 @@ "source": [ "### Incorrect program\n", "\n", - "This program in incorrect both on the send and the receive side.\n", - "\n", - "- One needs to wait for completion before reseting the send buffer\n", - "- One needs to wait for completion before using the receive buffer\n", - "\n" + "This program in incorrect both on the send and the receive side. " ] }, { @@ -1172,9 +1350,31 @@ "id": "4cc264f1", "metadata": {}, "source": [ - "### Which is the incoming message size?\n", + "## MPI_Iprobe\n", "\n", - "If we use `MPI_Probe` we miss the opportunity to do local work before a send is started." + "Remember that we used `MPI_Probe` to query for the size of an incoming message. However, if we use `MPI_Probe` we miss the opportunity to do local work before a matching send started, i.e. we cannot do latency hiding.\n", + "\n", + "This can be fixed using an `MPI_Iprobe`, i.e., an incomplete probe. It allows us to check for incoming messages without blocking.\n", + "\n", + "In Julia:\n", + "```julia\n", + "ismsg, status = MPI.Iprobe(comm, MPI.Status; source, tag)\n", + "```\n", + "\n", + "In C:\n", + "```C\n", + "int MPI_Iprobe(int source, int tag, MPI_Comm comm, int *flag,\n", + " MPI_Status *status)\n" + ] + }, + { + "cell_type": "markdown", + "id": "05a72541", + "metadata": {}, + "source": [ + "### Example\n", + "\n", + "Rank 2 sleeps for some time before the send starts. On the receive side, we will block on the call to `MPI_Probe` waiting for the send to start." ] }, { @@ -1216,14 +1416,7 @@ "id": "6761b4fa", "metadata": {}, "source": [ - "We can fix this using an `MPI_Iprobe`. It allows us to check for incoming messages without blocking.\n", - "\n", - "```julia\n", - "ismsg, status = MPI.Iprobe(comm, MPI.Status; source, tag)\n", - "```\n", - "```C\n", - "int MPI_Iprobe(int source, int tag, MPI_Comm comm, int *flag,\n", - " MPI_Status *status)\n" + "This other version uses `MPI_Iprobe` to do some local work wile waiting for the incoming message." ] }, { @@ -1248,7 +1441,7 @@ " if rank == 3\n", " while true\n", " ismsg, status = MPI.Iprobe(comm, MPI.Status; source=2, tag=0)\n", - " if ismsg\n", + " if ismsg # If the send started, get message size and start the receive\n", " count = MPI.Get_count(status,Int)\n", " rcvbuf = zeros(Int,count)\n", " reqrcv = MPI.Irecv!(rcvbuf, comm; source=2, tag=0)\n", @@ -1257,531 +1450,21 @@ " @show rcvbuf\n", " break\n", " end\n", - " work() # Do work while waiting for an incoming message.\n", + " work() # Otherwise do work while waiting for an incoming message.\n", " end\n", " end\n", "end\n", "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "c168ca8e", - "metadata": {}, - "outputs": [], - "source": [ - "? MPI.Iprobe" - ] - }, { "cell_type": "markdown", - "id": "6caa8d74", + "id": "30fc748f", "metadata": {}, "source": [ - "## MPI Communicators\n", + "## Summary\n", "\n", - "In MPI, a **communicator** represents a group of processes that can communicate with each other. `MPI_COMM_WORLD` (`MPI.COMM_WORLD` from Julia) is a built-in communicator that represents all processes available in the MPI program. Custom communicators can also be created to group processes based on specific requirements or logical divisions. The **rank** of a processor is a unique (integer) identifier assigned to each process within a communicator. It allows processes to distinguish and address each other in communication operations.\n", - "\n", - "### Duplicating a communicator\n", - "\n", - "It is a good practice to not using the built-in communicators directly, and use a copy instead with `MPI.Comm_dup`. Different libraries using the same communicator can lead to unexpected interferences." - ] - }, - { - "cell_type": "markdown", - "id": "c87b3c82", - "metadata": {}, - "source": [ - "## Collective communication\n", - "\n", - "MPI provides collective communication functions for communication involving multiple processes. Some usual collective directives are:\n", - "\n", - "- `MPI.Gather`: Gathers data from all processes to a single process.\n", - "- `MPI.Scatter`: Distributes data from one process to all processes.\n", - "- `MPI.Bcast`: Broadcasts data from one process to all processes.\n", - "- `MPI.Barrier`: Synchronizes all processes.\n", - "\n", - "See more collective directives available from Julia here: https://juliaparallel.org/MPI.jl/stable/reference/collective/\n" - ] - }, - { - "cell_type": "markdown", - "id": "97dc2886", - "metadata": {}, - "source": [ - "### Gather\n", - "\n", - "Each rank sends a message to the root rank (the root rank also sends a message to itself). The root rank receives all these values in a buffer (e.g. a vector)." - ] - }, - { - "attachments": { - "g13884.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "4bdf9c02", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f8a70c6", - "metadata": {}, - "outputs": [], - "source": [ - "code = quote\n", - " using MPI; MPI.Init()\n", - " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n", - " nranks = MPI.Comm_size(comm)\n", - " rank = MPI.Comm_rank(comm)\n", - " root = 0\n", - " snd = 10*(rank+2)\n", - " println(\"I am sending $snd\")\n", - " rcv = MPI.Gather(snd,comm;root)\n", - " if rank == root\n", - " println(\"I have received: $rcv\")\n", - " end\n", - "end\n", - "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);" - ] - }, - { - "cell_type": "markdown", - "id": "8b4254d1", - "metadata": {}, - "source": [ - "### Scatter\n", - "\n", - "The root rank contains a buffer (e.g., a vector) of values (one value for each rank in a communicator). Scatter sends one value to each rank (the root rank also receives a value). The root rank can be any process in a communicator." - ] - }, - { - "attachments": { - "g13389.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "74d5b606", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ea413fd", - "metadata": {}, - "outputs": [], - "source": [ - "code = quote\n", - " using MPI; MPI.Init()\n", - " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n", - " nranks = MPI.Comm_size(comm)\n", - " rank = MPI.Comm_rank(comm)\n", - " root = 0\n", - " rcv = Ref(0) \n", - " if rank == root\n", - " snd = [10*(i+1) for i in 1:nranks]\n", - " println(\"I am sending: $snd\")\n", - " else\n", - " snd = nothing\n", - " end \n", - " MPI.Scatter!(snd,rcv,comm;root)\n", - " println(\"I have received: $(rcv[])\")\n", - "end\n", - "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);" - ] - }, - { - "cell_type": "markdown", - "id": "ed8da7f9", - "metadata": {}, - "source": [ - "### Bcast (broadcast)\n", - "\n", - "Similar to scatter, but we send the same message to all processes." - ] - }, - { - "attachments": { - "g1657.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "26b56c81", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4de15781", - "metadata": {}, - "outputs": [], - "source": [ - "code = quote\n", - " using MPI; MPI.Init()\n", - " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n", - " nranks = MPI.Comm_size(comm)\n", - " rank = MPI.Comm_rank(comm)\n", - " root = 0\n", - " buffer = Ref(0)\n", - " if rank == root\n", - " buffer[] = 20\n", - " println(\"I am sending: $(buffer[])\")\n", - " end \n", - " MPI.Bcast!(buffer,comm;root)\n", - " println(\"I have received: $(buffer[])\")\n", - "end\n", - "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);" - ] - }, - { - "cell_type": "markdown", - "id": "6de37cc9", - "metadata": {}, - "source": [ - "### Barrier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff61f64a", - "metadata": {}, - "outputs": [], - "source": [ - "code = quote\n", - " using MPI; MPI.Init()\n", - " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n", - " sleep(rand(1:3))\n", - " MPI.Barrier(comm)\n", - " println(\"Done!\")\n", - "end\n", - "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);" - ] - }, - { - "cell_type": "markdown", - "id": "ac9a5b52", - "metadata": {}, - "source": [ - "## Point-to-Point communication\n", - "\n", - "\n", - "MPI also provides point-to-point communication directives for arbitrary communication between processes. Point-to-point communications are two-sided: there is a sender and a receiver. Here, we will discuss these basic directives:\n", - "\n", - "- `MPI.Isend`, and `MPI.Irecv!` (*non-blocking directives*)\n", - "- `MPI.Send`, and `MPI.Recv!` (*blocking directives*)\n", - "\n", - "\n", - "Non-blocking directives return immediately and return an `MPI.Request` object. This request object can be queried with functions like `MPI.Wait`. It is mandatory to wait on the request object before reading the receive buffer, or before writing again on the send buffer.\n", - "\n", - "For blocking directives, it is save to read/write from/to the receive/send buffer once the function has returned. By default, blocking directives might wait (or might not wait) for a matching send/receive. \n", - "For fine control, MPI offers advanced *blocking* directives with different blocking behaviors (called communication modes, see section 3.9 of the MPI standard 4.0). Blocking communication will be discussed later in the course.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "26879a46", - "metadata": {}, - "source": [ - "### Blocking communication\n", - "\n", - "If we start a receive before a matching send, we will block in the call to `MPI.Recv!`. Run the next cell and note that the message is not printed since the process is blocked at `MPI.Recv!` waiting for a matching send." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68aa9ada", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 4 begin\n", - " buffer = Ref(0)\n", - " comm = MPI.COMM_WORLD\n", - " MPI.Recv!(buffer,comm;source=2-2,tag=0)\n", - " println(\"I have received $(buffer[]).\")\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "ace9c787", - "metadata": {}, - "source": [ - "If you run the next cell containing the corresponding send, the communication will take place." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e18347cd", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 2 begin\n", - " buffer = Ref(2)\n", - " comm = MPI.COMM_WORLD\n", - " MPI.Send(buffer,comm;dest=4-2,tag=0)\n", - " println(\"I have send $(buffer[]). It is now safe to overwite the buffer.\")\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "0a038200", - "metadata": {}, - "source": [ - "### MPI does not integrate well with Julia Tasks\n", - "\n", - "MPI blocks without yielding (we cannot switch to other Julia tasks). Run next cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7da7d27", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 4 begin\n", - " buffer = Ref(0)\n", - " comm = MPI.COMM_WORLD\n", - " MPI.Recv!(buffer,comm;source=2-2,tag=0)\n", - " println(\"I have received $(buffer[]).\")\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "530b673b", - "metadata": {}, - "source": [ - "Now try to spawn other tasks on process 4 by running next cell. This task will not be served yet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae1165ac", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 4 println(\"Hello!\");" - ] - }, - { - "cell_type": "markdown", - "id": "c722d71e", - "metadata": {}, - "source": [ - "We first need to unlock the receive with a matching send. Then the task printing \"Hello!\" will be finally served." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9da6f76e", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 2 begin\n", - " buffer = Ref(2)\n", - " comm = MPI.COMM_WORLD\n", - " MPI.Send(buffer,comm;dest=4-2,tag=0)\n", - " println(\"I have send $(buffer[]). It is now safe to overwite the buffer.\")\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "6f4fa1cd", - "metadata": {}, - "source": [ - "### Non-blocking communication" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6e2fbbf", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 4 begin\n", - " buffer = Ref(0)\n", - " comm = MPI.COMM_WORLD\n", - " req = MPI.Irecv!(buffer,comm;source=2-2,tag=0)\n", - " println(\"Not yet safe to read the buffer\")\n", - " MPI.Wait(req)\n", - " println(\"I have received $(buffer[]).\")\n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4aa426ff", - "metadata": {}, - "outputs": [], - "source": [ - "@spawnat 2 begin\n", - " buffer = Ref(2)\n", - " comm = MPI.COMM_WORLD\n", - " req = MPI.Isend(buffer,comm;dest=4-2,tag=0)\n", - " println(\"Not yet safe to write the buffer\")\n", - " MPI.Wait(req)\n", - " println(\"I have send $(buffer[]). It is now safe to overwite the buffer.\")\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "750fdacb", - "metadata": {}, - "source": [ - "### Example\n", - "\n", - "The first rank generates a message and sends it to the last rank. The last rank receives the message and multiplies it by a coefficient. The last rank sends the result back to the first rank." - ] - }, - { - "attachments": { - "g5369.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "b479bbd4", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dccc3f6a", - "metadata": {}, - "outputs": [], - "source": [ - "@everywhere workers() begin\n", - " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n", - " rank = MPI.Comm_rank(comm)\n", - " nranks = MPI.Comm_size(comm)\n", - " snder = 0\n", - " rcver = nranks-1\n", - " buffer = Ref(0)\n", - " if rank == snder\n", - " msg = 10*(rank+2)\n", - " println(\"I am sending: $msg\")\n", - " buffer[] = msg\n", - " req = MPI.Isend(buffer,comm;dest=rcver,tag=0)\n", - " MPI.Wait(req)\n", - " req = MPI.Irecv!(buffer,comm,source=rcver,tag=0)\n", - " MPI.Wait(req)\n", - " msg = buffer[]\n", - " println(\"I have received: $msg\")\n", - " end\n", - " if rank == rcver\n", - " req = MPI.Irecv!(buffer,comm,source=snder,tag=0)\n", - " MPI.Wait(req)\n", - " msg = buffer[]\n", - " println(\"I have received: $msg\")\n", - " coef = (rank+2)\n", - " msg = msg*coef\n", - " println(\"I am sending: $msg\")\n", - " buffer[] = msg\n", - " req = MPI.Isend(buffer,comm;dest=snder,tag=0)\n", - " MPI.Wait(req)\n", - " end\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "162682c1", - "metadata": {}, - "source": [ - "
\n", - "Important: In non-blocking communication, use MPI.Wait() before modifying the send buffer or using the receive buffer.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9dc6c3fb", - "metadata": {}, - "source": [ - "### Example (with blocking directives)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a046d7dd", - "metadata": {}, - "outputs": [], - "source": [ - "@everywhere workers() begin\n", - " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n", - " rank = MPI.Comm_rank(comm)\n", - " nranks = MPI.Comm_size(comm)\n", - " snder = 0\n", - " rcver = nranks-1\n", - " buffer = Ref(0)\n", - " if rank == snder\n", - " msg = 10*(rank+2)\n", - " println(\"I am sending: $msg\")\n", - " buffer[] = msg\n", - " MPI.Send(buffer,comm;dest=rcver,tag=0)\n", - " MPI.Recv!(buffer,comm,source=rcver,tag=0)\n", - " msg = buffer[]\n", - " println(\"I have received: $msg\")\n", - " end\n", - " if rank == rcver\n", - " MPI.Recv!(buffer,comm,source=snder,tag=0)\n", - " msg = buffer[]\n", - " println(\"I have received: $msg\")\n", - " coef = (rank+2)\n", - " msg = msg*coef\n", - " println(\"I am sending: $msg\")\n", - " buffer[] = msg\n", - " MPI.Send(buffer,comm;dest=snder,tag=0)\n", - " end\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "d9bdfa02", - "metadata": {}, - "source": [ - "
\n", - "Important: Blocking directives might look simpler to use, but they can lead to dead locks if the sends and receives are not issued in the right order. Non-blocking directives can also lead to dead locks, but when waiting for the request, not when calling the send/receive functions.\n", - "
" + "We have seen different ways of sending and receiving messages with MPI, each one with its pros and cons. You as user should decide which is the best option for a given problem. We also learned how to get information about an incoming messages with `MPI_Status`, `MPI_Probe`, and `MPI_Iprobe`. In addition, we saw how easy is to write incorrect programs if you do not follow the semantics of the MPI operations properly.\n" ] }, { @@ -1794,11 +1477,66 @@ }, { "cell_type": "markdown", - "id": "a8e1c623", + "id": "bd491de3", "metadata": {}, "source": [ "### Exercise 1\n", "\n", + "Implement the parallel matrix-matrix multiplication (Algorithm 3) in previous notebook using MPI instead of `Distributed`. Use this function signature:\n", + "\n", + "```julia\n", + "function matmul_mpi_3!(C,A,B)\n", + "```\n", + "\n", + "Assume that the input matrices `A` and `B` are given only on rank 0, the other ranks get dummy matrices with zero rows and zero columns to save memory. You need to communicate the required parts to other ranks. For simplicity you can assume that `A` and `B` are square matrices and that the number of rows is a multiple of the number of processes (on rank 0). The result `C` should be overwritten only on rank 0. You can use the following cell to implement and check your result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "265c4593", + "metadata": {}, + "outputs": [], + "source": [ + "code = quote\n", + " using MPI\n", + " MPI.Init()\n", + " function matmul_mpi_3!(C,A,B)\n", + " # Implement here\n", + " end\n", + " function testit(load)\n", + " comm = MPI.COMM_WORLD\n", + " rank = MPI.Comm_rank(comm)\n", + " if rank == 0\n", + " P = MPI.Comm_size(comm)\n", + " N = load*P\n", + " else\n", + " N = 0\n", + " end\n", + " A = rand(N,N)\n", + " B = rand(N,N)\n", + " C = similar(A)\n", + " matmul_mpi_3!(C,A,B)\n", + " if rank == 0\n", + " if !(C ≈ A*B)\n", + " println(\"Check not passed\")\n", + " else\n", + " println(\"Check passed!\")\n", + " end\n", + " end\n", + " end\n", + " testit(100)\n", + "end\n", + "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);" + ] + }, + { + "cell_type": "markdown", + "id": "a8e1c623", + "metadata": {}, + "source": [ + "### Exercise 2\n", + "\n", "Implement this \"simple\" algorithm (the telephone game):\n", "\n", "Rank 0 generates a message (an integer). Rank 0 sends the message to rank 1. Rank 1 receives the message, increments the message by 1, and sends the result to rank 2. Rank 2 receives the message, increments the message by 1, and sends the result to rank 3. Etc. The last rank sends back the message to rank 0 closing the ring. See the next figure. Implement the communications using MPI. Do not use `Distributed`.\n" diff --git a/dev/julia_mpi/index.html b/dev/julia_mpi/index.html index 8ba0dd6..4f72bd7 100644 --- a/dev/julia_mpi/index.html +++ b/dev/julia_mpi/index.html @@ -1,10 +1,10 @@ -- · XM_40017
+MPI (point-to-point) · XM_40017
Tip
  • - Download this notebook and run it locally on your machine [recommended]. Click here. + Download this notebook and run it locally on your machine [highly recommended]. Click here.
@@ -14,4 +14,4 @@ var myIframe = document.getElementById("notebook"); iFrameResize({log:true}, myIframe); }); -
+
diff --git a/dev/julia_mpi_src/index.html b/dev/julia_mpi_src/index.html index a6c6bf2..1f08b98 100644 --- a/dev/julia_mpi_src/index.html +++ b/dev/julia_mpi_src/index.html @@ -7530,7 +7530,7 @@ a.anchor-link { @@ -7543,11 +7543,10 @@ a.anchor-link { @@ -7571,18 +7570,31 @@ a.anchor-link { + @@ -7710,12 +7721,13 @@ a.anchor-link { @@ -7737,7 +7749,7 @@ a.anchor-link { @@ -7759,7 +7772,7 @@ a.anchor-link { @@ -7784,7 +7797,7 @@ a.anchor-link { @@ -7804,6 +7817,19 @@ a.anchor-link { + + @@ -7928,7 +7967,8 @@ a.anchor-link { @@ -8076,26 +8117,13 @@ a.anchor-link { - @@ -8141,6 +8169,7 @@ a.anchor-link { @@ -8171,7 +8200,7 @@ a.anchor-link { @@ -8202,16 +8231,28 @@ a.anchor-link { + @@ -8292,7 +8334,7 @@ a.anchor-link { MPI.Send(sndbuf, comm; dest=3, tag=0) end if rank == 3 - rcvbuf = zeros(Int,5) + rcvbuf = zeros(Int,5) # Allocate space for the incoming message MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0) @show rcvbuf end @@ -8310,7 +8352,7 @@ a.anchor-link { @@ -8382,7 +8424,7 @@ a.anchor-link { + + - - @@ -8678,37 +8771,24 @@ a.anchor-link { + + + @@ -8923,7 +9115,7 @@ a.anchor-link { if rank == 3 while true ismsg, status = MPI.Iprobe(comm, MPI.Status; source=2, tag=0) - if ismsg + if ismsg # If the send started, get message size and start the receive count = MPI.Get_count(status,Int) rcvbuf = zeros(Int,count) reqrcv = MPI.Irecv!(rcvbuf, comm; source=2, tag=0) @@ -8932,7 +9124,7 @@ a.anchor-link { @show rcvbuf break end - work() # Do work while waiting for an incoming message. + work() # Otherwise do work while waiting for an incoming message. end end end @@ -8942,597 +9134,14 @@ a.anchor-link { - - +