From fbad81866b0e8ba0190191347e0ae1ad6378a783 Mon Sep 17 00:00:00 2001 From: carstenbauer Date: Fri, 12 Jul 2024 14:35:56 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20=20@=206ecce?= =?UTF-8?q?4f30ee1f60e67f206e56e57da0b1dfe2bfe=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 2 +- Manifest.toml | 40 ++++++++++----------- index.html | 2 +- package-lock.json | 8 ++--- package.json | 2 +- search/index.html | 2 +- sitemap.xml | 42 +++++++++++++---------- sysadmin_julia/index.html | 4 +-- user_faq/index.html | 4 +-- user_gettingstarted/index.html | 2 +- user_hpcprofiling/index.html | 2 +- user_hpcprofiling/intel_vtune/index.html | 4 +-- user_hpcprofiling/paraver_1.jpg | Bin 0 -> 147794 bytes user_hpcprofiling/paraver_2.gif | Bin 0 -> 33169 bytes user_hpcsystems/index.html | 2 +- user_jlls/index.html | 2 +- user_vscode/index.html | 20 +++++++++++ 17 files changed, 82 insertions(+), 56 deletions(-) create mode 100644 user_hpcprofiling/paraver_1.jpg create mode 100644 user_hpcprofiling/paraver_2.gif create mode 100644 user_vscode/index.html diff --git a/404.html b/404.html index 1b8e739..0ed4a2f 100644 --- a/404.html +++ b/404.html @@ -1 +1 @@ - 404: File not found

Julia On HPC Clusters

by the Julia HPC community



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Julia On HPC Clusters

by the Julia HPC community



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Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file diff --git a/Manifest.toml b/Manifest.toml index 4aa2023..bdb3aa3 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -15,21 +15,21 @@ uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.BitFlags]] -git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" +git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" -version = "0.1.8" +version = "0.1.9" [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73" +git-tree-sha1 = "b8fe8546d52ca154ac556809e10c75e6e7430ac8" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.4" +version = "0.7.5" [[deps.ConcurrentUtilities]] deps = ["Serialization", "Sockets"] -git-tree-sha1 = "6cbbd4d241d7e6579ab354737f4dd95ca43946e1" +git-tree-sha1 = "ea32b83ca4fefa1768dc84e504cc0a94fb1ab8d1" uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" -version = "2.4.1" +version = "2.4.2" [[deps.Dates]] deps = ["Printf"] @@ -78,15 +78,15 @@ version = "0.10.2" [[deps.HTTP]] deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "995f762e0182ebc50548c434c171a5bb6635f8e4" +git-tree-sha1 = "d1d712be3164d61d1fb98e7ce9bcbc6cc06b45ed" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.10.4" +version = "1.10.8" [[deps.IOCapture]] deps = ["Logging", "Random"] -git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c" +git-tree-sha1 = "b6d6bfdd7ce25b0f9b2f6b3dd56b2673a66c8770" uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" -version = "0.2.4" +version = "0.2.5" [[deps.InteractiveUtils]] deps = ["Markdown"] @@ -128,15 +128,15 @@ uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" [[deps.Literate]] deps = ["Base64", "IOCapture", "JSON", "REPL"] -git-tree-sha1 = "bad26f1ccd99c553886ec0725e99a509589dcd11" +git-tree-sha1 = "eef2e1fc1dc38af90a18eb16e519e06d1fd10c2a" uuid = "98b081ad-f1c9-55d3-8b20-4c87d4299306" -version = "2.16.1" +version = "2.19.0" [[deps.LiveServer]] deps = ["HTTP", "LoggingExtras", "MIMEs", "Pkg", "Sockets", "Test"] -git-tree-sha1 = "24d05efe53436b22a42bf2ae459f47c48b0c2603" +git-tree-sha1 = "1e46b873b8ef176e23ee43f96e72cd45c20bafb4" uuid = "16fef848-5104-11e9-1b77-fb7a48bbb589" -version = "1.2.7" +version = "1.3.1" [[deps.Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" @@ -186,15 +186,15 @@ version = "2.0.0" [[deps.OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] -git-tree-sha1 = "af81a32750ebc831ee28bdaaba6e1067decef51e" +git-tree-sha1 = "38cb508d080d21dc1128f7fb04f20387ed4c0af4" uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" -version = "1.4.2" +version = "1.4.3" [[deps.OpenSSL_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "60e3045590bd104a16fefb12836c00c0ef8c7f8c" +git-tree-sha1 = "a028ee3cb5641cccc4c24e90c36b0a4f7707bdf5" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "3.0.13+0" +version = "3.0.14+0" [[deps.OrderedCollections]] git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5" @@ -267,9 +267,9 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.TranscodingStreams]] deps = ["Random", "Test"] -git-tree-sha1 = "a09c933bebed12501890d8e92946bbab6a1690f1" +git-tree-sha1 = "60df3f8126263c0d6b357b9a1017bb94f53e3582" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.10.5" +version = "0.11.0" [[deps.URIs]] git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b" diff --git a/index.html b/index.html index ba3031e..b806485 100644 --- a/index.html +++ b/index.html @@ -1 +1 @@ - Julia On HPC Clusters

Julia On HPC Clusters

by the Julia HPC community



Julia on HPC clusters

πŸ‘‹ Welcome to the notes about the Julia programming language on HPC clusters.

Objective

The goal of these notes is to offer good practices and tips and tricks about how to use and set up Julia on HPC clusters. The target audiences are therefore Julia users on clusters as well as HPC system administrators.

Acknowledgements

These notes don't come out of nowhere but stem from endless conversations with many people (see below for a certainly incomplete list) on the Julia discourse, over Slack and Discord, as well as in-person discussions at Julia HPC minisymposia, BoFs, and workshops.

They also build upon previous efforts to document best practices, most notably https://github.com/hlrs-tasc/julia-on-hpc-systems/, from which we took (among other things) the HPC systems with Julia support information.

People

Maintainers

Contributors

The following people have provided valuable contributions, either in the form of PRs or via public or private communication:

(You think we forgot your name here? Please make a PR!)

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\ No newline at end of file + Julia On HPC Clusters

Julia On HPC Clusters

by the Julia HPC community



Julia on HPC clusters

πŸ‘‹ Welcome to the notes about the Julia programming language on HPC clusters.

Objective

The goal of these notes is to offer good practices and tips and tricks about how to use and set up Julia on HPC clusters. The target audiences are therefore Julia users on clusters as well as HPC system administrators.

Acknowledgements

These notes don't come out of nowhere but stem from endless conversations with many people (see below for a certainly incomplete list) on the Julia discourse, over Slack and Discord, as well as in-person discussions at Julia HPC minisymposia, BoFs, and workshops.

They also build upon previous efforts to document best practices, most notably https://github.com/hlrs-tasc/julia-on-hpc-systems/, from which we took (among other things) the HPC systems with Julia support information.

People

Maintainers

Contributors

The following people have provided valuable contributions, either in the form of PRs or via public or private communication:

(You think we forgot your name here? Please make a PR!)

Edit this page on
Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file diff --git a/package-lock.json b/package-lock.json index d1e011d..16ef736 100644 --- a/package-lock.json +++ b/package-lock.json @@ -6,7 +6,7 @@ "": { "dependencies": { "cheerio": "^1.0.0-rc.12", - "highlight.js": "^11.9.0", + "highlight.js": "^11.10.0", "lunr": "^2.3.9" } }, @@ -140,9 +140,9 @@ } }, "node_modules/highlight.js": { - "version": "11.9.0", - "resolved": "https://registry.npmjs.org/highlight.js/-/highlight.js-11.9.0.tgz", - "integrity": "sha512-fJ7cW7fQGCYAkgv4CPfwFHrfd/cLS4Hau96JuJ+ZTOWhjnhoeN1ub1tFmALm/+lW5z4WCAuAV9bm05AP0mS6Gw==", + "version": "11.10.0", + "resolved": "https://registry.npmjs.org/highlight.js/-/highlight.js-11.10.0.tgz", + "integrity": "sha512-SYVnVFswQER+zu1laSya563s+F8VDGt7o35d4utbamowvUNLLMovFqwCLSocpZTz3MgaSRA1IbqRWZv97dtErQ==", "engines": { "node": ">=12.0.0" } diff --git a/package.json b/package.json index 160bd69..4ed8b57 100644 --- a/package.json +++ b/package.json @@ -1,7 +1,7 @@ { "dependencies": { "cheerio": "^1.0.0-rc.12", - "highlight.js": "^11.9.0", + "highlight.js": "^11.10.0", "lunr": "^2.3.9" } } diff --git a/search/index.html b/search/index.html index 5b1d7ad..950eb74 100644 --- a/search/index.html +++ b/search/index.html @@ -1 +1 @@ - Search β‹… YourWebsite

Julia On HPC Clusters

by the Julia HPC community



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Julia On HPC Clusters

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\ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index 7ad3fbe..f335c5b 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,56 +2,62 @@ - https://juliahpc.github.io/user_jlls/index.html - 2024-03-20 + https://juliahpc.github.io/sysadmin_julia/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/user_hpcprofiling/index.html - 2024-03-20 + https://juliahpc.github.io/search/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/index.html - 2024-03-20 + https://juliahpc.github.io/user_hpcprofiling/intel_vtune/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/user_gettingstarted/index.html - 2024-03-20 + https://juliahpc.github.io/user_jlls/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/sysadmin_julia/index.html - 2024-03-20 + https://juliahpc.github.io/user_vscode/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/search/index.html - 2024-03-20 + https://juliahpc.github.io/user_hpcprofiling/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/user_hpcsystems/index.html - 2024-03-20 + https://juliahpc.github.io/user_faq/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/user_hpcprofiling/intel_vtune/index.html - 2024-03-20 + https://juliahpc.github.io/index.html + 2024-07-12 monthly 0.5 - https://juliahpc.github.io/user_faq/index.html - 2024-03-20 + https://juliahpc.github.io/user_gettingstarted/index.html + 2024-07-12 + monthly + 0.5 + + + https://juliahpc.github.io/user_hpcsystems/index.html + 2024-07-12 monthly 0.5 diff --git a/sysadmin_julia/index.html b/sysadmin_julia/index.html index a24d455..b069e6c 100644 --- a/sysadmin_julia/index.html +++ b/sysadmin_julia/index.html @@ -1,4 +1,4 @@ - Admin: How to provide Julia to users?

Julia On HPC Clusters

by the Julia HPC community



Admin: How to provide Julia to users?

Content

  1. Providing Julia itself
  2. Don't provide packages
  3. Providing global package preferences
    1. Example: MPI.jl and CUDA.jl
    2. Providing everything in form of a Lmod module
    3. Checking that things work es expected
  4. Supporting Visual Studio Code
  5. Potentially useful resources

Providing Julia itself

It is generally best to just provide the regular, pre-built Julia binaries from https://julialang.org/downloads/. They work and are efficient.

You might be tempted to build Julia from source but this is almost never necessary or advantageous (and can be cumbersome). The reason is pretty simple: Julia is a compiler in itself and user code is therefore barely influenced by how you compiled Julia. Hence, unless you know exactly what you're doing and have a good reason in mind, the pre-built binaries are the way to go.

‴ back to Content

Don't provide packages

We strongly recommend against trying to provide Julia packages (e.g. MPI.jl or CUDA.jl) in a central fashion. There are a few important technical reasons but the two most important reasons to not do it are conceptual

  1. It conflicts with the idea of local, self-contained Julia environments.

  2. Packages evolve too quickly (i.e. your central depot is out of date pretty quickly).

Short explanation: To have people use your centrally provided version of a package you would have to instruct them to not add that package to their (local) Julia environment as otherwise they'll very(!) likely get a different version of the package and will end up not using your central version to begin with. To make using PackageX still work, you would then have to adjust JULIA_LOAD_PATH appropriately. However, not only doesn't the user not get the version of PackageX that they potentially want/need, the local Project.toml/Manifest.toml would not even contain PackageX and therefore severely undermine self-containedness and reproducibility - one of the nicest properties of Julia projects.

Generally speaking, there is also little reason to provide regular Julia packages in a central fashion in the first place. After all, most of them don't take up that much disk space and a little bit of redundancy is acceptable on modern HPC clusters. In this regard, note that all packages only get installed once per user - and not per project as is the case for python - since Julia installs packages to a user-central depot and reuses them across projects.

‴ back to Content

Providing global package preferences

What you might want to do is to globally override binary dependencies of Julia packages, so-called Julia artifacts (JLLs), such that users automatically get redirected to system binaries under the hood. This is especially relevant if vendor specific binaries (e.g. a Cray MPI library) are the only ones that function properly on the HPC cluster. Below we describe how to realize this by overriding package preferences. However, note that JLLs have the big advantage that they are convenient and work nicely together. You should hence only override preferences if there is a good reason for it.

Example: MPI.jl and CUDA.jl

In this example we show how to make MPI.jl automatically use a system OpenMPI installation as well as make CUDA.jl use a system CUDA installation. The two steps are pretty straightforward:

  1. Create a global Project.toml that sets the preferences of the target packages.

  2. Append the path to the global Project.toml to JULIA_LOAD_PATH (for all users).

According to the documentation of MPI.jl (relevant section) and CUDA.jl (relevant section) the preferences that we need to modify are actually not in MPI.jl or CUDA.jl but rather in MPIPreferences.jl and CUDA_Runtime_jll.jl. We thus create a Project.toml like below:

πŸ’‘ Note
Tip: To obtain the correct preferences blocks you can use MPIPreferences.use_system_binary() and CUDA.set_runtime_version!(v"11.7"; local_toolkit=true) respectively. This will create the relevant blocks in a LocalPreferences.toml file. The only thing you need to adjust is the block header, e.g. MPIPreferences -> preferences.MPIPreferences.
[extras]
+             Admin: How to provide Julia to users?  
 
 
 
 
Julia On HPC Clusters
 
by the Julia HPC community
 
 
   
   
 
 
 
 
 
 
 
Admin: How to provide Julia to users?
 
 
 
 Content
 
  1. Providing Julia itself
  2. Don't provide packages
  3. Providing global package preferences
    1. Example: MPI.jl and CUDA.jl
    2. Providing everything in form of a Lmod module
    3. Checking that things work es expected
  4. Supporting Visual Studio Code
  5. Potentially useful resources
 
 
Providing Julia itself
 
It is generally best to just provide the regular, pre-built Julia binaries from https://julialang.org/downloads/. They work and are efficient.
 
You might be tempted to build Julia from source but this is almost never necessary or advantageous (and can be cumbersome). The reason is pretty simple: Julia is a compiler in itself and user code is therefore barely influenced by how you compiled Julia. Hence, unless you know exactly what you're doing and have a good reason in mind, the pre-built binaries are the way to go.
 
‴ back to Content
 
Don't provide packages
 
We strongly recommend against trying to provide Julia packages (e.g. MPI.jl or CUDA.jl) in a central fashion. There are a few important technical reasons but the two most important reasons to not do it are conceptual
 
     
  1. It conflicts with the idea of local, self-contained Julia environments.
     
  2. Packages evolve too quickly (i.e. your central depot is out of date pretty quickly).
     
 
Short explanation: To have people use your centrally provided version of a package you would have to instruct them to not add that package to their (local) Julia environment as otherwise they'll very(!) likely get a different version of the package and will end up not using your central version to begin with. To make using PackageX still work, you would then have to adjust JULIA_LOAD_PATH appropriately. However, not only doesn't the user not get the version of PackageX that they potentially want/need, the local Project.toml/Manifest.toml would not even contain PackageX and therefore severely undermine self-containedness and reproducibility - one of the nicest properties of Julia projects.
 
Generally speaking, there is also little reason to provide regular Julia packages in a central fashion in the first place. After all, most of them don't take up that much disk space and a little bit of redundancy is acceptable on modern HPC clusters. In this regard, note that all packages only get installed once per user - and not per project as is the case for python - since Julia installs packages to a user-central depot and reuses them across projects.
 
‴ back to Content
 
Providing global package preferences
 
What you might want to do is to globally override binary dependencies of Julia packages, so-called Julia artifacts (JLLs), such that users automatically get redirected to system binaries under the hood. This is especially relevant if vendor specific binaries (e.g. a Cray MPI library) are the only ones that function properly on the HPC cluster. Below we describe how to realize this by overriding package preferences. However, note that JLLs have the big advantage that they are convenient and work nicely together. You should hence only override preferences if there is a good reason for it.
 
Example: MPI.jl and CUDA.jl
 
In this example we show how to make MPI.jl automatically use a system OpenMPI installation as well as make CUDA.jl use a system CUDA installation. The two steps are pretty straightforward:
 
     
  1. Create a global Project.toml that sets the preferences of the target packages.
     
  2. Append the path to the global Project.toml to JULIA_LOAD_PATH (for all users).
     
 
According to the documentation of MPI.jl (relevant section) and CUDA.jl (relevant section) the preferences that we need to modify are actually not in MPI.jl or CUDA.jl but rather in MPIPreferences.jl and CUDA_Runtime_jll.jl. We thus create a Project.toml like below:
 
πŸ’‘ Note
 
Tip: To obtain the correct preferences blocks you can use MPIPreferences.use_system_binary() and CUDA.set_runtime_version!(v"11.7"; local_toolkit=true) respectively. This will create the relevant blocks in a LocalPreferences.toml file. The only thing you need to adjust is the block header, e.g. MPIPreferences -> preferences.MPIPreferences.
 
[extras]
 MPIPreferences = "3da0fdf6-3ccc-4f1b-acd9-58baa6c99267"
 CUDA_Runtime_jll = "76a88914-d11a-5bdc-97e0-2f5a05c973a2"
 
@@ -50,4 +50,4 @@
 module load lang/JuliaHPC/1.9.1-foss-2022a-CUDA-11.7.0
 
 # Start Julia and pass on all command-line arguments
-exec julia "${@}"
 
At PC2 such a script is provided for every Julia version / Julia module (see JuliaHPC_Installer) as well as a generic one that only contains module load lang/JuliaHPC (without a version number) and thus always loads the latest Julia module.
 
‴ back to Content
 
Potentially useful resources
  
‴ back to Content
 
 
 Edit this page on 
 Last modified: March 20, 2024. Website built with Franklin.jl and the Julia programming language. 
 
 
 

\ No newline at end of file
+exec julia "${@}"

At PC2 such a script is provided for every Julia version / Julia module (see JuliaHPC_Installer) as well as a generic one that only contains module load lang/JuliaHPC (without a version number) and thus always loads the latest Julia module.

‴ back to Content

Potentially useful resources

‴ back to Content

Edit this page on
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\ No newline at end of file diff --git a/user_faq/index.html b/user_faq/index.html index 600c5a7..604b313 100644 --- a/user_faq/index.html +++ b/user_faq/index.html @@ -1,4 +1,4 @@ - FAQ

Julia On HPC Clusters

by the Julia HPC community



FAQ

Content

  1. Should I compile Julia from source?
  2. Where should I put the Julia depot?
  3. How should I start Julia within a job script?
  4. How can I force Julia to compile code for a heterogeneous cluster?
  5. Should I use Distributed.jl or MPI.jl for large-scale parallelism?
  6. Should I use Julia artifacts (JLLs) or system software?
  7. How to cope with a large number of MPI processes accessing the same Julia depot?
  8. How to avoid LD_LIBRARY_PATH issues?
  9. I get memory-related issues when using CUDA.jl on a HPC cluster

Should I compile Julia from source?

No, you should not. Use the pre-built binaries from the website or, preferably, a corresponding Lmod module provided by the cluster admins. Compiling Julia from source will generally not improve the performance of your code (Julia is a compiler in itself) but may very well be cumbersome. Unless you know what you're doing and have a very good reason (e.g. creating a debug build with flags like USE_INTEL_JITEVENTS=1) you should not compile Julia yourself.

‴ back to Content

Where should I put the Julia depot?

Ideally, you should set JULIA_DEPOT_PATH to point to a place with the following properties:

  • good (parallel) I/O

  • no tight quotas

  • read and write access

  • no automatic deletion of unused files (or otherwise you have to find a workaround)

On some systems, the best place might be on the parallel file system. On others, it might simply be in $HOME.

‴ back to Content

How should I start Julia within a job script?

Assuming that SLURM is used on your HPC cluster, you should generally start Julia under srun, e.g. srun julia --project mycode.jl. This is especially important if your code is MPI-parallel, in which case srun is a replacement for mpirun/mpiexec, but also recommended for serial code (there are at least a few reasons).

‴ back to Content

How can I force Julia to compile code for a heterogeneous cluster?

Set JULIA_CPU_TARGET to a value that is generic enough to cover all the types of CPUs that you're targeting.

Example: JULIA_CPU_TARGET="generic;skylake-avx512,clone_all;znver2,clone_all".

‴ back to Content

Should I use Distributed.jl or MPI.jl for large-scale parallelism?

While the Distributed standard library provides some convenient tools and has its use cases you should generally use MPI.jl to scale your code up (e.g. to thousands of compute nodes). Not only is MPI the established standard for distributed computing in any programming language, it also makes use of fast interconnects in HPC clusters (which the Distributed standard library currently doesn't).

‴ back to Content

Should I use Julia artifacts (JLLs) or system software?

If JLLs work fine for you then use them. JLLs have the big advantage that they are convenient and, in many cases, "just work" out of the box. System software can (but doesn't necessarily) give better performance but overriding the relevant bits of JLLs can be cumbersome. Generally speaking, we recommend to only manually replace JLL libraries by system software if JLLs don't work (e.g. if a vendor specific MPI is required). However, in such a case it would be even better to nudge the HPC admins and make this setup permanent and generally available in form of a Julia Lmod module.

‴ back to Content

How to cope with a large number of MPI processes accessing the same Julia depot?

In a distributed computing scenario with, e.g., multiple thousands of Julia (MPI) processes, accessing the same Julia depot on a shared file system - when loading packages and precompiled cache files on using PackageX - can become (very) time consuming. A workaround is to bundle up the Julia depot (e.g. as a .tar.gz), distribute it to the local node storage (if available) or local memory (often times mounted as /tmp) of all assigned compute nodes, and then set the JULIA_DEPOT_PATH accordingly.

‴ back to Content

How to avoid LD_LIBRARY_PATH issues?

When using Julia on a system that uses an environment-variable based module system (such as modules or Lmod), the LD_LIBRARY_PATH variable might be filled with entries pointing to different packages and libraries. This might (or might not) lead to issues stemming from Julia loading libraries other than the ones packaged with it. If you encounter such problems, make sure that Julia's lib directory is always the first directory in LD_LIBRARY_PATH.

One possibility to achieve this is to create a wrapper shell script that modifies LD_LIBRARY_PATH before calling the Julia executable. Inspired by a script from UCL's Owain Kenway:

#!/usr/bin/env bash
+             FAQ  
 
 
 
 
Julia On HPC Clusters
 
by the Julia HPC community
 
 
   
   
 
 
 
 
 
 
 
FAQ
 
 Content
 
  1. Should I compile Julia from source?
  2. Where should I put the Julia depot?
  3. How should I start Julia within a job script?
  4. How can I force Julia to compile code for a heterogeneous cluster?
  5. Should I use Distributed.jl or MPI.jl for large-scale parallelism?
  6. Should I use Julia artifacts (JLLs) or system software?
  7. How to cope with a large number of MPI processes accessing the same Julia depot?
  8. How to avoid LD_LIBRARY_PATH issues?
  9. I get memory-related issues when using CUDA.jl on a HPC cluster
 
 
Should I compile Julia from source?
 
No, you should not. Use the pre-built binaries from the website or, preferably, a corresponding Lmod module provided by the cluster admins. Compiling Julia from source will generally not improve the performance of your code (Julia is a compiler in itself) but may very well be cumbersome. Unless you know what you're doing and have a very good reason (e.g. creating a debug build with flags like USE_INTEL_JITEVENTS=1) you should not compile Julia yourself.
 
‴ back to Content
 
Where should I put the Julia depot?
 
Ideally, you should set JULIA_DEPOT_PATH to point to a place with the following properties:
 
     
  • good (parallel) I/O
     
  • no tight quotas
     
  • read and write access
     
  • no automatic deletion of unused files (or otherwise you have to find a workaround)
     
 
As a rule of thumb: Put the Julia depot on the parallel file system (typically $SCRATCH).
 
‴ back to Content
 
How should I start Julia within a job script?
 
Assuming that SLURM is used on your HPC cluster, you should generally start Julia under srun, e.g. srun julia --project mycode.jl. This is especially important if your code is MPI-parallel, in which case srun is a replacement for mpirun/mpiexec, but also recommended for serial code (there are at least a few reasons).
 
Note that you can use the slim mpiexecjl wrapper from MPI.jl to use the "correct" MPI driver automatically.
 
‴ back to Content
 
How can I force Julia to compile code for a heterogeneous cluster?
 
Set JULIA_CPU_TARGET to a value that is generic enough to cover all the types of CPUs that you're targeting.
 
Example: JULIA_CPU_TARGET="generic;skylake-avx512,clone_all;znver2,clone_all".
 
This compiles all functions (clone_all) for Intel Skylake (skylake-avx512), AMD Zen 2 (znver2), and a generic fallback (generic).
 
‴ back to Content
 
Should I use Distributed.jl or MPI.jl for large-scale parallelism?
 
While the Distributed standard library provides some convenient tools and has its use cases you should generally use MPI.jl to scale your code up (e.g. to thousands of compute nodes). Not only is MPI the established standard for distributed computing in any programming language, it also makes use of fast interconnects in HPC clusters (which the Distributed standard library currently doesn't).
 
‴ back to Content
 
Should I use Julia artifacts (JLLs) or system software?
 
If JLLs work fine for you then use them. JLLs have the big advantage that they are convenient and, in many cases, "just work" out of the box. System software can (but doesn't necessarily) give better performance but overriding the relevant bits of JLLs can be cumbersome. Generally speaking, we recommend to only manually replace JLL libraries by system software if JLLs don't work (e.g. if a vendor specific MPI is required). However, in such a case it would be even better to nudge the HPC admins and make this setup permanent and generally available in form of a Julia Lmod module.
 
‴ back to Content
 
How to cope with a large number of MPI processes accessing the same Julia depot?
 
In a distributed computing scenario with, e.g., multiple thousands of Julia (MPI) processes, accessing the same Julia depot on a shared file system - when loading packages and precompiled cache files on using PackageX - can become (very) time consuming. A workaround is to bundle up the Julia depot (e.g. as a .tar.gz), distribute it to the local node storage (if available) or local memory (often times mounted as /tmp) of all assigned compute nodes, and then set the JULIA_DEPOT_PATH accordingly.
 
‴ back to Content
 
How to avoid LD_LIBRARY_PATH issues?
 
When using Julia on a system that uses an environment-variable based module system (such as modules or Lmod), the LD_LIBRARY_PATH variable might be filled with entries pointing to different packages and libraries. This might (or might not) lead to issues stemming from Julia loading libraries other than the ones packaged with it. If you encounter such problems, make sure that Julia's lib directory is always the first directory in LD_LIBRARY_PATH.
 
One possibility to achieve this is to create a wrapper shell script that modifies LD_LIBRARY_PATH before calling the Julia executable. Inspired by a script from UCL's Owain Kenway:
 
#!/usr/bin/env bash
 
 # This wrapper makes sure the julia binary distributions picks up the GCC
 # libraries provided with it correctly meaning that it does not rely on
@@ -11,4 +11,4 @@
 directory=$(readlink -f $(dirname ${location})/..)
 
 export LD_LIBRARY_PATH=${directory}/lib/julia:${LD_LIBRARY_PATH}
-exec ${directory}/bin/julia "$@"
 
Note that using readlink might not be optimal from a performance perspective if used in a massively parallel environment. Alternatively, hard-code the Julia path or set an environment variable accordingly.
 
‴ back to Content
  
Try setting JULIA_CUDA_MEMORY_POOL=none (see the CUDA.jl documentation for more information).
 
‴ back to Content
 
 
 Edit this page on 
 Last modified: March 20, 2024. Website built with Franklin.jl and the Julia programming language. 
 
 
 

\ No newline at end of file
+exec ${directory}/bin/julia "$@"

Note that using readlink might not be optimal from a performance perspective if used in a massively parallel environment. Alternatively, hard-code the Julia path or set an environment variable accordingly.

‴ back to Content

Try setting JULIA_CUDA_MEMORY_POOL=none (see the CUDA.jl documentation for more information).

‴ back to Content

Edit this page on
Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file diff --git a/user_gettingstarted/index.html b/user_gettingstarted/index.html index a4bee3f..0ac0ad5 100644 --- a/user_gettingstarted/index.html +++ b/user_gettingstarted/index.html @@ -1 +1 @@ - Getting started

Julia On HPC Clusters

by the Julia HPC community



Getting started

Getting started with Julia on a new cluster can sometimes be a challenge. Below we provide some common tips that will hopefully help you with your onboarding process.

Content

  1. Julia binaries or Julia module
  2. Placing the Julia depot

Julia binaries or Julia module

When starting on a new HPC cluster the first thing you should do is figure out if there is a pre-configured Julia module (Lmod module) available on the cluster. To that end, module key julia or module spider julia might be helpful commands.

If there isn't a Julia module available that you can load, you should use the regular, pre-built Julia binaries. You can either download (e.g. wget or curl) them directly from the website or use juliaup. In any case, you should not build Julia from source (unless you have a very good reason and know that you're doing).

‴ back to Content

Placing the Julia depot

One you have julia, you must make sure that the Julia depot is placed on an appropriate file system. By default, it will be stored in $HOME/.julia. This may or may not be a good choice. What you want is a place on a file system with the following properties

  • no tight quotas (at least >= 20 GB)

  • read and write access (ideally also from compute nodes)

  • good (parallel) I/O

  • no automatic deletion of unused files (or otherwise you have to find a workaround)

Often times these criterion are best fit on a parallel file system (which $HOME rarely is). For this reason it might be necessary to set JULIA_DEPOT_PATH=/path/to/fast/fs/.julia.

Note that if the last point (automatic deletion of unused files) is an issue for you, a pragmatic workaround could be a cronjob that touches all files in the Julia depot every once in a while.

‴ back to Content

Edit this page on
Last modified: March 20, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file + Getting started

Julia On HPC Clusters

by the Julia HPC community



Getting started

Getting started with Julia on a new cluster can sometimes be a challenge. Below we provide some common tips that will hopefully help you with your onboarding process.

Content

  1. Julia binaries or Julia module
  2. Placing the Julia depot

Julia binaries or Julia module

When starting on a new HPC cluster the first thing you should do is figure out if there is a pre-configured Julia module (Lmod module) available on the cluster. To that end, module key julia or module spider julia might be helpful commands.

If there isn't a Julia module available that you can load, you should use the regular, pre-built Julia binaries. You can either download (e.g. wget or curl) them directly from the website or use juliaup. In any case, you should not build Julia from source (unless you have a very good reason and know that you're doing).

‴ back to Content

Placing the Julia depot

One you have julia, you must make sure that the Julia depot is placed on an appropriate file system. By default, it will be stored in $HOME/.julia. This may or may not be a good choice. What you want is a place on a file system with the following properties

  • no tight quotas (at least >= 20 GB)

  • read and write access (ideally also from compute nodes)

  • good (parallel) I/O

  • no automatic deletion of unused files (or otherwise you have to find a workaround)

Often times these criterion are best fit on a parallel file system (often $SCRATCH). For this reason it might be necessary to set JULIA_DEPOT_PATH=$SCRATCH/.julia.

Note that if the last point (automatic deletion of unused files) is an issue for you, a pragmatic workaround could be a cronjob that touches all files in the Julia depot every once in a while.

‴ back to Content

Edit this page on
Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file diff --git a/user_hpcprofiling/index.html b/user_hpcprofiling/index.html index 3064be1..66fad7e 100644 --- a/user_hpcprofiling/index.html +++ b/user_hpcprofiling/index.html @@ -1 +1 @@ - HPC Profiling Tools

Julia On HPC Clusters

by the Julia HPC community



HPC Profiling Tools

There are plenty of HPC tools for profiling out there. Figuring out which one is good in what scenario and, more importantly, which one actually works with Julia is non-trivial. This page will try to provide helpful orientation.

Content

  1. NVIDIA Nsight Systems
  2. Score-P
  3. LIKWID
  4. Intel VTune Profiler
  5. Other tools

NVIDIA Nsight Systems

Good for: GPU, MPI

NVIDIA Nsight Systems is a powerful profiling tool for analyzing (multi-)GPU and/or MPI-parallel applications (the latter might be somewhat surprising). Especially useful when combined with NVTX.jl for manual instrumentation (you can name and even color sections of your code).

Examples:

Impressions:

Score-P

Good for: MPI

The Julia package ScoreP.jl allows you to use Score-P for analyzing MPI-parallel Julia applications. Output files are of type .cubex (profiling), which can be opened with, e.g., Cube or ParaProf, and .otf2 (tracing), which can be opened with, e.g., Vampir or Intel Trace Analyzer.

Noteworthy limitations:

  • The package isn't battle-tested and currently experimental.

  • While manual instrumentation works, automatic tracing of Julia functions isn't (yet) supported.

Examples:

  • You're best chance is to check out the README.md.

  • Only works on Linux.

Impressions (of Cube and Vampir):

LIKWID

Good for: intra-node hardware-level profiling

LIKWID.jl, named after the underlying eponymous benchmarking suite LIKWID, enables (interactive) monitoring of the performance of arbitrary Julia functions on a hardware level by examining hardware performance counters inside of CPUs (and NVIDIA GPUs).

Noteworthy limitations:

  • Manual installation of LIKWID necessary (no JLL).

  • Some features marked as experimental (but basic core is solid).

  • Only works on Linux.

Examples:

Impressions:

Intel VTune Profiler

Good for: serial, multithreading, GC

The Intel VTune Profiler is a nice tool, e.g., for finding hot spots in your code. It supports local and remote performance profiling. To make it work with Julia check out IntelITT.jl and our dedicated Intel VTune + Julia page.

Noteworthy limitations:

  • Works best (only?) on systems with Intel CPUs.

  • Can't profile on macOS (only remotely on Linux machine).

  • May require compiling Julia from source (if you want more details, e.g., about GC).

Examples:

Impressions:

Other tools

If you know something about the following tools, in particular if and how it supports Julia, please make a PR!

  • Extrae (instrumentation / measurement framework, comparable to Score-P)

  • TAU

  • MUST (MPI runtime correctness analysis)

  • PAPI / PAPI.jl (to be compared to LIKWID)

  • HPCToolkit

‴ back to Content

Edit this page on
Last modified: March 20, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file + HPC Profiling Tools

Julia On HPC Clusters

by the Julia HPC community



HPC Profiling Tools

There are plenty of HPC tools for profiling out there. Figuring out which one is good in what scenario and, more importantly, which one actually works with Julia is non-trivial. This page will try to provide helpful orientation.

Content

  1. NVIDIA Nsight Systems
  2. Extrae
  3. Score-P
  4. LIKWID
  5. Intel VTune Profiler
  6. Other tools

NVIDIA Nsight Systems

Good for: GPU, MPI

NVIDIA Nsight Systems is a powerful profiling tool for analyzing (multi-)GPU and/or MPI-parallel applications (the latter might be somewhat surprising). Especially useful when combined with NVTX.jl for manual instrumentation (you can name and even color sections of your code).

Examples:

Impressions:

Extrae

Good for: MPI, GPU, Threads

The Julia package Extrae.jl allows you to use Extrae for analyzing parallel Julia applications. It will produce trace files that can be visualized and analyzed with Paraver.

Noteworthy limitations:

  • The package isn't battle-tested.

  • The Paraver GUI might be overwhelming and takes some getting used to.

Examples:

  • Only works on Linux.

Impressions (of Paraver):

Score-P

Good for: MPI

The Julia package ScoreP.jl allows you to use Score-P for analyzing MPI-parallel Julia applications. Output files are of type .cubex (profiling), which can be opened with, e.g., Cube or ParaProf, and .otf2 (tracing), which can be opened with, e.g., Vampir or Intel Trace Analyzer.

Noteworthy limitations:

  • The package isn't battle-tested and currently experimental.

  • While manual instrumentation works, automatic tracing of Julia functions isn't (yet) supported.

Examples:

  • You're best chance is to check out the README.md.

  • Only works on Linux.

Impressions (of Cube and Vampir):

LIKWID

Good for: intra-node hardware-level profiling

LIKWID.jl, named after the underlying eponymous benchmarking suite LIKWID, enables (interactive) monitoring of the performance of arbitrary Julia functions on a hardware level by examining hardware performance counters inside of CPUs (and NVIDIA GPUs).

Noteworthy limitations:

  • Manual installation of LIKWID necessary (no JLL).

  • Some features marked as experimental (but basic core is solid).

  • Only works on Linux.

Examples:

Impressions:

Intel VTune Profiler

Good for: serial, multithreading, GC

The Intel VTune Profiler is a nice tool, e.g., for finding hot spots in your code. It supports local and remote performance profiling. To make it work with Julia check out IntelITT.jl and our dedicated Intel VTune + Julia page.

Noteworthy limitations:

  • Works best (only?) on systems with Intel CPUs.

  • Can't profile on macOS (only remotely on Linux machine).

  • May require compiling Julia from source (if you want more details, e.g., about GC).

Examples:

Impressions:

Other tools

If you know something about the following tools, in particular if and how it supports Julia, please make a PR!

‴ back to Content

Edit this page on
Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
\ No newline at end of file diff --git a/user_hpcprofiling/intel_vtune/index.html b/user_hpcprofiling/intel_vtune/index.html index 249bb03..38b56c1 100644 --- a/user_hpcprofiling/intel_vtune/index.html +++ b/user_hpcprofiling/intel_vtune/index.html @@ -1,4 +1,4 @@ - Intel VTune Profiler + Julia

Julia On HPC Clusters

by the Julia HPC community



Intel VTune Profiler + Julia

Content

  1. General notes
  2. Remote usage - GUI
    1. Example code
    2. Profiling via the GUI
    3. Results
  3. Command-line interface (CLI)

General notes

  • You need to set ENABLE_JITPROFILING=1.

  • For Julia < 1.9, you need to compile Julia from source with USE_INTEL_JITEVENTS=1.

Remote usage - GUI

Example code

using ThreadPinning
+             Intel VTune Profiler + Julia  
 
 
 
 
Julia On HPC Clusters
 
by the Julia HPC community
 
 
   
   
 
 
 
 
 
 
 
Intel VTune Profiler + Julia
 
 
 Content
 
  1. General notes
  2. Remote usage - GUI
    1. Example code
    2. Profiling via the GUI
    3. Results
  3. Command-line interface (CLI)
 
 
General notes
 
     
  • You need to set ENABLE_JITPROFILING=1.
     
  • For Julia < 1.9, you need to compile Julia from source with USE_INTEL_JITEVENTS=1.
     
 
Remote usage - GUI
 
Example code
 
using ThreadPinning
 using IntelITT
 
 # Multithreaded SAXPY kernel: Y[i] = a * X[i] + Y[i]
@@ -62,4 +62,4 @@
 export ENABLE_JITPROFILING=1
 exec julia "${@}"
 
  • Wheter you use a julia wrapper or not, you must set ENABLE_JITPROFILING=1.
     
  • In the screenshot above, I've selected "Hotpots" as the analysis type (right pane). It's likely what you want to use.
      
  • Once everything is configured, press either on "Start" (play button) or on "Start Paused" (play + pause button). I recommend to use "Start Paused" in conjuction with using IntelITT.jl, i.e. IntelITT.resume() and IntelITT.pause(), in your code. This way, you can already pre-select the code region that you actually care about.
      
    Results
     
       
    • Summary
       
     
     
       
    • Top-down Tree
       
     
     
       
    • Comments
       
         
      • Due to how Julia works internally, your Julia functions appear with the prefix julia_ and a suffix _XX, where XX is a unique number, in the call stack. For example, the Julia function mysquare(x) = x^2 might appear as julia_mysquare_89.
         
      • In the SAXPY example above, we use @threads, which, under the hood, creates a function threadsfor_fun that will then get run (see the top-down tree above). Unfortunately, this can also cause problems like "cutting off" the call stack information as well as hindering source code resolution.
         
      • Source code resolution (i.e. "View Source") didn't work for me remotely. However, it gives you the option to point to the source file locally. This worked fine.
         
       
     
    Command-line interface (CLI)
     
    Useful commands:
     
       
    • Profiling: ENABLE_JITPROFILING=1 vtune -collect hotspots -start-paused -- julia --project mycode.jl
       
    • Open the results in the GUI: vtune-gui r000hs
       
    • Generating text reports:
       
     
    vtune -report hotspots -r r000hs -group-by source-line > reports/hotspots.report
 vtune -report top-down -r r000hs > reports/top_down.report
-vtune -report callstacks -r r000hs > reports/callstacks.report
     
    Check out the julia-intelvtune repository by Carsten Bauer which contains a basic demonstration.
     
     
     Edit this page on 
     Last modified: March 20, 2024. Website built with Franklin.jl and the Julia programming language. 
     
     
    •  
    
\ No newline at end of file
+vtune -report callstacks -r r000hs > reports/callstacks.report

    Check out the julia-intelvtune repository by Carsten Bauer which contains a basic demonstration.

    Edit this page on
    Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
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    Julia On HPC Clusters

    by the Julia HPC community



    HPC systems with Julia support

    πŸ’‘ Note
    It is difficult to keep the table below up-to-date. It is also almost certainly incomplete. We rely on people like you, so please update the table if you think something can be improved!

    Content

    1. Description
    2. HPC systems
      1. Australasia
      2. Europe
      3. North America
      4. Other HPC systems

    Description

    We try to maintain an (incomplete) list of HPC systems that provide a Julia installation and/or support for using Julia to its users. For this, we use the following nomenclature:

    • Center: The HPC center's name

    • System: The compute system's "marketing" name

    • Installation: Is a pre-installed Julia module available?

    • Support: Can users expect "official" Julia support on the system (e.g. by HPC staff)?

    • Interactive: Is interactive computing with Julia supported, i.e., can you run parallel jobs on the system interactively via, e.g., Jupyter notebooks?

    • Architecture: The main CPU used in the system

    • Accelerators: The main accelerator (if anything) in the system

    • Documentation: Links to documentation for Julia users

    HPC systems

    (Sorted in alphabetical order)

    Australasia

    CenterSystemInstallationSupportInteractiveArchitectureAcceleratorsDocumentation
    NeSIMahuika, Māuiβœ…βœ…βœ…Intel Xeon Broadwell/Cascade Lake + AMD EPYC MilanNvidia Tesla P100, A1001

    Europe

    CenterSystemInstallationSupportInteractiveArchitectureAcceleratorsDocumentation
    ARC, UCLMyriad, Kathleen, Michael, Youngβœ…βœ…?various Intel Xeonvarious GPUs1
    CSC (EuroHPC)LUMIβœ…βœ…βœ…AMD EPYC MilanAMD Radeon Instinct MI250X1, 2
    CSCPuhtiβœ…βœ…βœ…Intel Xeon Gold 6230Nvidia Tesla V1001, 2
    CSCMahtiβœ…βœ…βœ…AMD EPYC 7H12Nvidia Ampere A1001, 2
    CSCSPiz Daintβœ…βœ…βœ…Intel Xeon Broadwell + HaswellNvidia Tesla P1001
    DESY ITMaxwellβœ…?βœ…various AMD EPYC/Intel Xeonvarious GPUs1
    HLRSHawkβœ…βœ…βœ…AMD EPYC RomeNvidia Tesla A1001
    HPC2N, UmeΓ₯ UKebnekaiseβœ…βœ…?Intel Xeon Broadwell + SkylakeNvidia Tesla K80, Nvidia Tesla V1001
    IT4I (EuroHPC)Karolinaβœ…βœ…βœ…AMD EPYC RomeNvidia Ampere A1001
    IZUM (EuroHPC)Vegaβœ…βœ…βœ…AMD EPYC RomeNvidia Ampere A1001
    LuxProvide (EuroHPC)MeluXinaβœ…?βœ…AMD EPYC RomeNvidia Ampere A100-401, 2
    PC2, U PaderbornNoctua 1βœ…βœ…βœ…Intel Xeon SkylakeIntel Stratix 10 + consumer GPUs1
    PC2, U PaderbornNoctua 2βœ…βœ…βœ…AMD EPYC MilanNvidia Ampere A100, Xilinx Alveo U2801
    ULHPC, U LuxembourgAion, Irisβœ…?βœ…AMD EPYC Rome + Intel Xeon Broadwell/SkylakeNvidia Tesla V1001
    ZDV, U MainzMOGON IIβœ…??Intel Xeon Broadwell + Skylakeno1
    ZIBHLRN-IVβœ…βœ…?Intel Cascade Lake APcoming soon: Nvidia A100, Intel PVC1

    North America

    CenterSystemInstallationSupportInteractiveArchitectureAcceleratorsDocumentation
    Carnegie Mellon College of EngineeringArjuna, Herculesβœ…βœ…βœ…Intel Xeon+AMD EPYC MilanNvidia A100, Nvidia K801
    Dartmouth CollegeDiscoveryβœ…?βœ…Intel Xeon (various) + AMD EPYC 7532Nvidia V1001
    FASRC, Harvard UCannonβœ…?βœ…Intel Xeon Cascade LakeNvidia V100, A1001
    HPC @ LLNLvarious systemsβœ…?βœ…various processorsvarious GPUs1
    NERSCCoriβœ…??Intel Xeon HaswellIntel Xeon Phi1
    NERSCPerlmutterβœ…βœ…?AMD EPYC MilanNvidia Ampere A1001, 2
    Open Science GridN/AβŒβœ…?VariousVarious1
    Perimeter Institute for Theoretical PhysicsSymmetryβœ…βœ…βœ…AMD EPYC, Intel XeonNvidia V100-
    Pittsburgh Supercomputing CenterBridges-2βœ…βœ…βœ…AMD EPYC, Intel XeonNvidia V1001
    Princeton UniversitySeveral including Tigerβœ…βœ…βœ…Intel Xeon (Skylake + Broadwell)Nvidia P1001

    Other HPC systems

    There are a number of other HPC systems that have been reported to provide a Julia installation and/or Julia support, but lack enough details to be put on the list above:

    • Various clusters at ANL

    Edit this page on
    Last modified: March 20, 2024. Website built with Franklin.jl and the Julia programming language.
    \ No newline at end of file + HPC systems with Julia support

    Julia On HPC Clusters

    by the Julia HPC community



    HPC systems with Julia support

    πŸ’‘ Note
    It is difficult to keep the table below up-to-date. It is also almost certainly incomplete. We rely on people like you, so please update the table if you think something can be improved!

    Content

    1. Description
    2. HPC systems
      1. Australasia
      2. Europe
      3. North America
      4. Other HPC systems

    Description

    We try to maintain an (incomplete) list of HPC systems that provide a Julia installation and/or support for using Julia to its users. For this, we use the following nomenclature:

    • Center: The HPC center's name

    • System: The compute system's "marketing" name

    • Installation: Is a pre-installed Julia module available?

    • Support: Can users expect "official" Julia support on the system (e.g. by HPC staff)?

    • Interactive: Is interactive computing with Julia supported, i.e., can you run parallel jobs on the system interactively via, e.g., Jupyter notebooks?

    • Architecture: The main CPU used in the system

    • Accelerators: The main accelerator (if anything) in the system

    • Documentation: Links to documentation for Julia users

    HPC systems

    (Sorted in alphabetical order)

    Australasia

    CenterSystemInstallationSupportInteractiveArchitectureAcceleratorsDocumentation
    NeSIMahuika, Māuiβœ…βœ…βœ…Intel Xeon Broadwell/Cascade Lake + AMD EPYC MilanNvidia Tesla P100, A1001

    Europe

    CenterSystemInstallationSupportInteractiveArchitectureAcceleratorsDocumentation
    ARC, UCLMyriad, Kathleen, Michael, Youngβœ…βœ…?various Intel Xeonvarious GPUs1
    CSC (EuroHPC)LUMIβœ…βœ…βœ…AMD EPYC MilanAMD Radeon Instinct MI250X1, 2
    CSCPuhtiβœ…βœ…βœ…Intel Xeon Gold 6230Nvidia Tesla V1001, 2
    CSCMahtiβœ…βœ…βœ…AMD EPYC 7H12Nvidia Ampere A1001, 2
    CSCSPiz Daintβœ…βœ…βœ…Intel Xeon Broadwell + HaswellNvidia Tesla P1001
    DESY ITMaxwellβœ…?βœ…various AMD EPYC/Intel Xeonvarious GPUs1
    HLRSHawkβœ…βœ…βœ…AMD EPYC RomeNvidia Tesla A1001
    HPC2N, UmeΓ₯ UKebnekaiseβœ…βœ…?Intel Xeon Broadwell + SkylakeNvidia Tesla K80, Nvidia Tesla V1001
    IT4I (EuroHPC)Karolinaβœ…βœ…βœ…AMD EPYC RomeNvidia Ampere A1001
    IZUM (EuroHPC)Vegaβœ…βœ…βœ…AMD EPYC RomeNvidia Ampere A1001
    LuxProvide (EuroHPC)MeluXinaβœ…?βœ…AMD EPYC RomeNvidia Ampere A100-401, 2
    PC2, U PaderbornNoctua 1βœ…βœ…βœ…Intel Xeon SkylakeIntel Stratix 10 + consumer GPUs1
    PC2, U PaderbornNoctua 2βœ…βœ…βœ…AMD EPYC MilanNvidia Ampere A100, Xilinx Alveo U2801
    ULHPC, U LuxembourgAion, Irisβœ…?βœ…AMD EPYC Rome + Intel Xeon Broadwell/SkylakeNvidia Tesla V1001
    ZDV, U MainzMOGON IIβœ…??Intel Xeon Broadwell + Skylakeno1
    ZIBHLRN-IVβœ…βœ…?Intel Cascade Lake APcoming soon: Nvidia A100, Intel PVC1

    North America

    CenterSystemInstallationSupportInteractiveArchitectureAcceleratorsDocumentation
    Carnegie Mellon College of EngineeringArjuna, Herculesβœ…βœ…βœ…Intel Xeon+AMD EPYC MilanNvidia A100, Nvidia K801
    Dartmouth CollegeDiscoveryβœ…?βœ…Intel Xeon (various) + AMD EPYC 7532Nvidia V1001
    FASRC, Harvard UCannonβœ…?βœ…Intel Xeon Cascade LakeNvidia V100, A1001
    HPC @ LLNLvarious systemsβœ…?βœ…various processorsvarious GPUs1
    NERSCCoriβœ…??Intel Xeon HaswellIntel Xeon Phi1
    NERSCPerlmutterβœ…βœ…?AMD EPYC MilanNvidia Ampere A1001, 2
    Open Science GridN/AβŒβœ…?VariousVarious1
    Perimeter Institute for Theoretical PhysicsSymmetryβœ…βœ…βœ…AMD EPYC, Intel XeonNvidia V100-
    Pittsburgh Supercomputing CenterBridges-2βœ…βœ…βœ…AMD EPYC, Intel XeonNvidia V1001
    Princeton UniversitySeveral including Tigerβœ…βœ…βœ…Intel Xeon (Skylake + Broadwell)Nvidia P1001

    Other HPC systems

    There are a number of other HPC systems that have been reported to provide a Julia installation and/or Julia support, but lack enough details to be put on the list above:

    • Various clusters at ANL

    Edit this page on
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    \ No newline at end of file diff --git a/user_jlls/index.html b/user_jlls/index.html index c413a75..565ce5c 100644 --- a/user_jlls/index.html +++ b/user_jlls/index.html @@ -1 +1 @@ - JLLs vs system software

    Julia On HPC Clusters

    by the Julia HPC community



    JLLs vs system software

    Content

    TBD

    ‴ back to Content

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    \ No newline at end of file + JLLs vs system software

    Julia On HPC Clusters

    by the Julia HPC community



    JLLs vs system software

    Content

    TBD

    ‴ back to Content

    Edit this page on
    Last modified: July 12, 2024. Website built with Franklin.jl and the Julia programming language.
    \ No newline at end of file diff --git a/user_vscode/index.html b/user_vscode/index.html new file mode 100644 index 0000000..2d9e059 --- /dev/null +++ b/user_vscode/index.html @@ -0,0 +1,20 @@ + Visual Studio Code on HPC Clusters

    Julia On HPC Clusters

    by the Julia HPC community



    Visual Studio Code on HPC Clusters

    VS Code is a very popular IDE with a great Julia extension (website). Running it on a cluster typically isn't straightforward and we want to highlight the most important steps here.

    Content

    1. What does "Running VS Code on a cluster" actually mean?
    2. Running VS Code on a login node (SSH)
    3. Installing the Julia extension
    4. Making the Julia extension work
    5. Can I run VS Code on a compute node? (SSH)
    6. Running VS Code remotely via "remote tunnels"

    What does "Running VS Code on a cluster" actually mean?

    To be clear: What it does not mean is to have VS Code running locally and using its terminal (at the bottom) the ssh to the cluster and running julia on it.

    Instead, we want to connect VS Code to a little server component that will run on the cluster. Hence, only the GUI will run locally and everything else will run on the cluster. This includes VS Code extensions, like the Julia extension and will eventually give us an integrated REPL, a debugger, a plot pane, etc., all working remotely on the cluster.

    Running VS Code on a login node (SSH)

    Note: We assume that the hostname of the cluster is mycluster.

    1. Make sure that you have password-less SSH access to the cluster (ssh mycluster should bring you to the cluster).

    2. In VS Code, press F1 and run the Remote-SSH: Open SSH Host... command.

    3. Enter mycluster and press enter.

    After a second or two, you should have VS Code running on a login node! πŸŽ‰

    ‴ back to Content

    Installing the Julia extension

    1. Open the extensions view (press CTRL/CMD + SHIFT + X).

    2. Search for julia.

    3. Click on install.

    Making the Julia extension work

    Typically, the julia command is not directly available on a cluster but one must load a Julia module first (e.g. module load julia). Unfortunately, the Julia extension doesn't know about this, so we need to teach it.

    1. Prepare a Julia wrapper script (julia_wrapper.sh) like the following:

    #!/bin/bash
+
+# Making module command available
+# ------------------------------------------------------------
+export MODULEPATH=/etc/modulefiles:/usr/share/modulefiles
+export LMOD_SYSTEM_DEFAULT_MODULES="DefaultModules"
+source /usr/share/lmod/lmod/init/profile
+module --initial_load restore
+# ------------------------------------------------------------
+
+# Load julia
+module load julia
+
+# Pass on all arguments to julia
+exec julia "${@}"

    Note the module name as well as (at least) the first two lines of the block that makes the module command available are system specific and must be adjusted. To figure our the latter, you may run the following in a shell on the cluster: echo $MODULEPATH and echo $LMOD_SYSTEM_DEFAULT_MODULES.

    1. Make the wrapper script executable: chmod +x julia_wrapper.sh

    2. Check that it works: ./julia_wrapper.sh should open the Julia REPL.

    Once the wrapper is ready and functional, we need to point the Julia extension to it.

    1. Open the VS Code Settings (press CTRL/CMD + ,).

    2. Click on the tab Remote [SSH: mycluster].

    3. Search for Julia executable.

    4. Insert /path/to/julia_wrapper.sh into the text field under Julia: Executable Path.

    If ALT/OPTION + J followed by ALT/OPTION + O (or pressing F1 and executing the Julia: Start REPL command) successfully spins up the integrated Julia REPL, you know that the setup is working! πŸŽ‰

    Important note: If you want all the nice features of the Julia extension - like inline evaluation, plotting, debugging, etc. - you need to use the integrated REPL started by the shortcuts mentioned above. If you just type julia in a terminal (at the bottom), the resulting REPL will not be connected to the extension!

    ‴ back to Content

    Can I run VS Code on a compute node? (SSH)

    Maybe. The first thing you need to check is whether it is at all possible for you to connect to a compute node via SSH, as not all HPC centers allow this. (Note that typically you can only ssh to compute nodes that are part of a job that you have currently running.)

    Given that you can ssh to compute nodes, what you want to do next is add a generic (wildcard) entry, that covers all compute nodes, to your .ssh/config. Assuming that the hostnames of the compute nodes start with cn (e.g. cn1201 and cn2134 etc.) and assuming that you already have a ssh config entry for mycluster, this could look like this:

    Host cn*
+    HostName %h
+    ProxyJump mycluster
+    User [USERNAME]
+    IdentityFile [PATH TO PRIVATE KEY]
+    IdentitiesOnly yes

    Afterwards, you can use the following steps to run VS Code on a compute node:

    1. Open a terminal (outside of VS Code)

    2. Login to the cluster (ssh mycluster)

    3. Get an interactive session on a compute node (and write down the name of the node that has been assigend to you, say, cn1201).

    4. Follow the steps under Running VS Code on a login node (SSH) above but replace the hostname mycluster by the name of the compute node (e.g. cn1201).

    ‴ back to Content

    Running VS Code remotely via "remote tunnels"

    Microsoft provides "Remote tunnels" as an alternative to SSH to connect VS Code to remote systems.

    Pro

    • Allows you to connect to any machine with internet access (including cluster compute nodes that you can't ssh to!).

    Con

    • Requires a GitHub / Microsoft account.

    • Microsoft serves as the tunnel provider (man in the middle).

    1. Download the VS Code command line interface from https://code.visualstudio.com/Download (search for CLI).

    2. Copy the single code binary to the cluster (and put the directory on PATH).

    3. Run code tunnel --verbose and follow the steps (to connect to your GitHub account).

    4. Afterwards, open VS Code and install the Remote - Tunnels extension.

    5. Run the command Remote-Tunnels: Connect to Tunnel... and connect to the remote node.

    ‴ back to Content

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