4 posts tagged with "gpu"

Hello HYAK Community,

Thank you for your patience this month while we had more scheduled downtime than usual to allow for electrical reconfiguration work in the UW data center. We appreciate how disruptive this work has been in recent weeks, we wanted to assure the community that the benefit is to create additional power capacity that will allow HYAK to grow and broaden its support of the UW research community.

In addition to our regular maintenance, we wanted to make you aware for the following changes initiated during scheduled downtime:

• We implemented an increase in checkpoint (--partition=ckpt) runtime for GPU jobs from 4-5 hours to 8-9 hours (pre-emption requeuing will still occur without notice). Please see our updated documentation page for more details about using idle resources.

Our next scheduled maintenance will be Tuesday May, 14, 2024.

Training Opportunities#

Follow NSF ACCESS Training and Events posting HERE to find online webinars about containers, parallel computing, using GPUs, and more from HPC providers around the USA.

Questions? If you have any questions for us, please reach out to the team by emailing help@uw.edu with Hyak in the subject line.

History#

In fall 2019 (almost two years ago to the day) the HYAK team received our first Turing generation GPU node. HYAK has had a modest GPU footprint in the past as far back as a decade ago with the first generation cluster (called "IKT") and its pre-Pascal generation cards. In 2015 we acquired a smaller test bed of Pascal generation GPUs for the second generation cluster (called "MOX"). There were never more than a dozen GPUs in either the IKT or MOX clusters, but the introduction of Turing GPUs marked a resurgence of interest in these accelerators among the UW research community. In the last two years, we've substantially expanded our capabilities to over 300 GPUs.

Background#

HYAK clusters work on a "condo" model: labs are able to utilize their contributed hardware on-demand as well as take advantage of idle capacity from other groups' hardware via the checkpoint (ckpt) partition. Your checkpoint priority — or "fairshare" in SLURM scheduler parlance — is weighted such that your fairshare is directly proportional to your lab’s contribution to the cluster. In the MOX days, GPU users tended to stay within their contributed hardware partitions and rarely made use of checkpoint. We attributed this to a mental shift: students were used to using a single resource, like a desktop computer, rather than a shared cluster of computing resources. However, with the migration to the third generation HYAK cluster (called "KLONE") and its new QoS scheduling system and the increasing comfort of students using a shared platform, GPU utilization in the checkpoint partition has increased as well. This is a good thing: we want groups to benefit from their HYAK membership in the cluster and take advantage of idle cluster resources beyond their initial hardware contributions. This is a primary tenet of our social contract with the HYAK community: as a node contributor to the cluster, you have access to idle resources of the whole cluster.

Problem#

Fairshare was simpler to calculate in the pre-GPU days because our infrastructure was homogenous: one node contributed to the cluster equaled one fairshare unit. During the last two years of exponential GPU adoption on HYAK, the fairshare calculation has not evolved: 1 HPC node was the same as 1 GPU node at 1 fairshare unit. This didn’t hold because a GPU node can cost between 4 to 8 times (or more) than a traditional HPC node. The result was that labs with GPU or other speciality (e.g., high-memory) nodes tended to have smaller fairshares compared to groups with the same dollar investment but only in traditional CPU nodes. In practice, this meant these GPU users often directly competed for resources with non-GPU jobs in the checkpoint partition on a non-level playing field.

Solution#

Taking into consideration all of this information, as well as the fact that you can request as little as 1 GPU or 1 CPU from the scheduler, we have adjusted the fairshare calculations as follows:

• Financially: 1 GPU card is roughly equivalent to 40 CPU cores (on a dollar basis), therefore the cost normalization is 40:1 in favor of GPUs.
• Scarcity: 1 server typically holds 8 GPU cards or 40 CPU cores, therefore the scarcity normalization is 5:1 in favor of GPUs.
• Combining the financial and scarcity considerations in the points above, the final weighting is 200:1 in favor of GPUs. In other words, 1 GPU card is worth 200 times more than a single CPU core in the eyes of the scheduler and factored into your checkpoint fairshare. Please note that this example only applies to the higher GPU memory cards (i.e., gpu-rtx6k) while less expensive GPUs have commensurately less weight.

Summary#

With the October monthly maintenance today we have introduced a new fairshare weighting system on the KLONE cluster's checkpoint (ckpt) partition that commensurately acknowledges GPU labs for their contributions to the HYAK community. This has no impact on jobs submitted to non-ckpt partitions.

The single biggest research use for GPUs on HYAK is for machine learning and artificial intelligence and the community has been clammoring for CUDA11 support for some time. Unfortunately, it's not easy to separate the GPU driver from the node images so it had to wait until the next maintenance window and some testing for non-ML GPU workflows on HYAK like our gromacs users in the molecular dynamics community.

tl;dr your existing Pytorch codes should work but if you wanted to use the new features in Pytorch that required CUDA11 you can upgrade Pytorch and it will work.

Installing Pytorch with CUDA11#

Since this is now the latest and greatest on HYAK I've taken the opportunity to update the Python documentation on how to install Pytorch with CUDA11 support within a miniconda3 environment, check out the step-by-step here.

Reverse compatibility with CUDA10#

Before the January 12, 2021 cluster maintenance every GPU on HYAK had a driver with CUDA10 and all of your codes were previously compiled against it. To test that the GPU driver update to CUDA11 wouldn't impact the most popular machine learning libraries we are compiling Pytorch against our pre-maintenance CUDA10 and testing it against a GPU with the newer CUDA11 installed.

Activate your new pytorch-cuda10 environment:

The Pytorch website [www] has a nice getting started matrix that generates the requisite install commands against CUDA10.

The command shown above to copy-and-paste below:

Now we can load the Python interpreter and confirm Pytorch is installed and the CUDA10 compiled library recognizes this GPU with CUDA11 [www].

Success!

Previously compiled libraries against CUDA10 from pre-January 12, 2021 maintenance times should still work on the GPUs now with CUDA11. However, if you want to use the full features of libraries that take advantage of newer capabilities in CUDA11 then you should definitely upgrade your libraries.

The second most widely used GPU-enabled workflow on HYAK (besides machine learning) is molecular dynamics (MD) so we wanted to test one of the most popular MD codes, gromacs [source], and ensure this driver upgrade wouldn't negatively impact our researchers. I couldn't find gromacs compiled with GPU support currently in our module collection so I used it as an opportunity to create one for you all, read on!

Using gromacs#

I'll start with the end result for those of you who just want to use it but following that I'll dive into the nuts and bolts of how we created the module so you can perform additional optimizations.

This is a GPU-enabled version of gromacs so we need a GPU first (can verify with nvidia-smi).

gromacs-2020.4 module#

Once we have a GPU we use modules to load gromacs-2020.4 and all its required dependencies (e.g., CUDA11).

All packages are sub-commands of the gmx binary so you can verify the module.

Test simulation of Lysozyme#

I used a tutorial from the gromacs website here to show it runs processes on GPU(s). The tutorial runs an MD simulation on a lysozyme but that's the extent of my study there. The commands below are a summary of the tutorial with a note that the genbox subcommand is now replaced by solvate.

The final gromacs command below starts the fun, the documentation suggests it will automatically identify the GPUs available to send work to them. However, there are more explicit GPU arguments we encourage you to explore.

You can ssh into the node you're using in a separate window to have a parallel nvidia-smi command run so we can monitor the load on the GPU(s).

We can see a process occuping each GPU so it works! At least, gromacs uses GPUs...the GPUs themselves weren't stressed heavily and that requires the user to increase the number of rank processes and match that with available GPUs. You can do this by adding arguments to the gmx mdrun command but by default it did 2 ranks per GPU it detected, which is not a lot.

(Optional) Compile Notes#

You need CUDA11, GNU Compiler, and OpenBLAS library for the version I put together but I was focused on a proof-of-concept and not squeezing out every last drop of performance. There's a lot of further optimization to be done and that's left as an exercise for the reader:

1. Try the Intel compiler and see if it provides further optimization for non-GPU parts of the workflow.
2. Try other math libraries (e.g., MKL) and see if it speeds things up.
3. Add in MPI support if you want to use multiple GPUs across multiple nodes.
4. Add in modules (e.g., PLUMED).
5. Other stuff I can't think of with compile flags [here].

Download Source#

From the login node I staged a folder in the modules directory.

Grab regression tests.

Download gromacs-2020.4 [source].

Get a GPU and Code#

I used the shared build-gpu node for an interactive session but if you are affiliated with a group that has their own you can use that instead.

Once you get a session with GPU (you can run nvidia-smi to confirm you see one). Extract regression tests.

Do the same for the gromacs code and enter the directory.

Pre-requisite Modules#

Modules loaded individually for readability but you could load all modules in one command. Get a refresher on modules here.

Compile#

I created a subdirectory within the source to compile.

Use cmake to create the Makefile. Note: if you copy-and-paste the cmake command below you will have to modify the paths referenced for your environment.

With the Makefile ready you can run make -j 4 and replace 4 with however many cores you have in your session then make install. I created the module file separately so you can load it with module load gromacs/2020.4-cuda11.1 and run the single gmx binary.