ecorunnerblog2

Eco-Runner Team Delft is a student team at the TU Delft.  Since 2005 our mission has been to design and build the most efficient vehicle powered by hydrogen.  There are two main aspects to this vehicle- a fuel efficient propulsion system and a vehicle with very little resistance.  The aerodynamic resistance is a big part of the latter.  Rescale’s cloud simulation platform allows us to design a vehicle with a minimal drag resistance.

Our team has been building fuel efficient cars for a decade.  This means that a lot of aerodynamic computations have already been done.  In order to further improve upon the previous vehicle, Ecorunner V, a comparison of different concepts was needed.  To gather reliable results for the different concepts takes a lot of time when limited to internal computation resources.  This is when Rescale entered the picture.

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Vorticity and pressure distribution over Ecorunner body

 

The concepts had to be compared based on a boundary layer analysis and a flow separation model.  After careful consideration, XFlow, from Next Limit Technologies, was chosen to perform the boundary layer analysis.  XFlow features the highest fidelity Wall-Modeled Large Eddy Simulation (WMLES) approach to the turbulence modeling.  The software was used to perform a 3D analysis of the pressure distribution over the vehicle.

Side wind has a significant influence on the drag when driving around a track, so to investigate how much the drag changes the concepts were investigated under different sideslip angles.  By using the results of a low turbulence wind tunnel test, the XFlow model could be analyzed even further.  The insights that we gained by using Rescale were very valuable when planning the wind tunnel test.

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XFlow Velocity simulation over Ecorunner body

 

To get the results, the Nickel hardware configuration was used.  This core type with 128 cores was the most cost-effective configuration for our simulations.  This resulted in a reliable boundary layer analysis.  It proved to be a very good tool to research iterations and compare concepts.  The next step for us is to implement rotating wheels into the model.

The use of Rescale saved us valuable time and money.  It also made a boundary layer analysis possible.  This has never been done in our design process.  By doing the analysis we were able to investigate the different concepts in detail before making a choice and further iterate the model, leading to an even more efficient hydrogen powered car.

About Eco-Runner Team Delft:
Eco-Runner Team Delft is a multi-disciplinary student team at Delft University of Technology. The goal of the team is to build an super efficient vehicle that runs on hydrogen. The team participates in the Shell Eco-Marathon, in May 2015 the team won the race with an efficiency of 3653 km per energy equivalent of a liter of gasoline. For more information on the team and its achievements please visit www.ecorunner.nl

This article was written by Eco-Runner Team Delft.

ryanblog2-2

Microsoft’s announcement of Azure Linux RDMA support last year was great news for those looking to run tightly coupled HPC workloads in the cloud.  Unfortunately, there still isn’t a lot of documentation out there describing how to set it up.  This tutorial appears to be the main source of information for configuring Azure Linux RDMA.  However, there are a couple of omissions in there that can trip you up when setting up your cluster for the first time.  In this post, we’ll cover a few gotchas that you might encounter and some workarounds.

First, the tutorial uses the older ASM model for deploying virtual machines.  Microsoft recommends that new projects use ARM for deployment.  One big reason for switching is that ARM deployments will provision virtual machines in parallel whereas ASM will deployment them serially.  For larger clusters, this can make a big difference in startup time.  This is a simple ARM template that can be used as a starting point that will launch a standalone MPI cluster with the recommended vanilla SLES 12 HPC VHD.

After the cluster launches, you will likely want to install some common packages like, say, git.

However:

# zypper install git
Loading repository data…
Reading installed packages…
‘git’ not found in package names. Trying capabilities.
No provider of ‘git’ found.
Resolving package dependencies…

Nothing to do.

The reason for this is that the vanilla SLES VHD is missing a bunch of repos out of the box.  You can re-add them by running the following:

# cd /etc/zypp/repos.d
# mv sldp-msft.repo sldp-msft.repo.bak
# rm -f *.repo
# systemctl restart guestregister.service
# mv sldp-msft.repo.bak sldp-msft.repo
# zypper addrepo sldp-msft.repo
# zypper refresh

Now, you should have access to a much wider range of packages to install.  As described in the tutorial guide, after you’ve installed any custom packages and also setup Intel MPI, you can capture your custom VHD and use that as the starting point for your MPI clusters instead.

Once you’ve launched a cluster with the custom VHD, you may need to install a VM extension that will update the RDMA drivers.  The tutorial states that you should not update the RDMA driver in the US West, West Europe, and Japan East regions.  However, this appears to be an out-of-date notice, because when we tried running the Intel MPI pingpong test in those regions, we ran into the same DAPL errors that are described here.  After updating the drivers, the pingpong test started working without error.

As far as installing the OSTC Extension goes, there is one small wrinkle that you will need to be aware of- if you ssh into the VM immediately after the installing the extension, you will notice that your connection is dropped shortly after logging in.

azureadmin@n1:~> Connection to 13.93.144.56 closed by remote host.
Connection to 13.93.144.56 closed.

The reason for this is that the VM is rebooted about 2-3 minutes after the extension deployment completes.  It would be nicer if the VM was ready for use when the extension installation finishes, but unfortunately that doesn’t seem to be the case here.  This is something that you’ll need to take into account if you are trying to automate the cluster deployment.

Hopefully, once Azure Linux RDMA support is added to the Azure Batch service you won’t have to deal with any of the above.  Of course, launching the cluster is just the starting point.  You still need to install and tune your simulation software, setup a connection to your license server, and securely transfer your input and output files to and from the cluster.  Rescale’s support team is ready to work with you to accomplish this on Azure using our web, API, or CLI tools.

This article was written by Ryan Kaneshiro.