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- Welcome to Azure Essentials.
In the next few minutes, we'll explore
the services in Azure to run high performance
computing applications and solutions.
Azure can deliver all sizes of computation.
From enabling powerful visual simulations
and engineering scenarios, to dynamic rendering
for GPU intensive work, to scientific and
data scenarios using analytics languages like R.
When it comes to high performance compute,
we know that some jobs may require high CPU performance,
while others may need a high memory to core ratio,
dedicated GPUs or even dedicated super computers.
You can mix and match your infrastructure,
and change it as your business needs change.
For larger scale solutions, Azure also offers
low latency InfiniBand interconnects
for running massive HPC jobs across multiple VMs.
And you can use dedicated infrastructure
with Cray supercomputers for extremely demanding workloads.
So, let's walk you through your options in more detail,
and along the way, we'll point out a few
common scenarios that can take advantage
of high performance computing in Azure.
Starting with your infrastructure options,
Azure now offers virtual chains
with fast CPUs for simulations or analytics,
large memory for passing databases
within memory capabilities, as well as GPU support.
Of course all options support both Windows,
and Linux-based workloads, as well as
a variety of open-source development,
automation, management platforms.
GPUs can of course be used for improved visualization,
but the speed of the GPU also enables faster computation.
Now with graphics-intensive scenarios,
Azure's N-series virtual machines are optimized
specifically for native-like access to GPU resources,
offering near bare-metal performance levels.
And V-series VMs are great for
running computer-aided design,
resource-intensive visual simulations,
allowing engineers to access powerful
Azure-hosted compute from just about any work station,
and without needing to move terabytes
of modeling data between end points and servers.
Microsoft has partnered with Nvidia,
to offer their Tesla V100 PCI Express-based GPUs,
and V100 SXM GPUs, so scenarios like
game development or video production
provide dynamic and photo realistic rendering,
as you can see here in this simulator.
You can also see the high frame
rates, and graphics performance.
Beyond graphical scenarios, Azure provides
ND-series virtual machines for
artificial intelligence workloads,
like model-training and inference.
For high performance computing workloads
like computational chemistry and fluid dynamics simulations,
you can use NC-series, and H-series VMs.
Now because Azure's the only public cloud
to provide InfiniBand and RDMA
to connect thousands of instances
across multiple GPUs and compute nodes,
these services provide extremely
fast compute times, at low latency.
Especially compared to CPU only architectures.
And if your jobs require the massive
scale of a Cray supercomputer,
we've got an exclusive partnership with Cray
that allows you to get a dedicated supercomputer
running within your Azure virtual network.
Now you've seen how Azure provides infrastructure
for all of your big compute workloads.
Now let's look at how Azure makes
using those resources easier.
Firstly, Azure Batch can run complex batch processes,
which can reason over large amounts
of data and compute resources, as a service,
without the need to manage individual VMs,
so you can just focus on running the workload,
versus provisioning the computer to run it.
Within Azure Batch, we also have
accelerators for AI, and rendering.
Now to provision, monitor, auto-scale,
and manage the lifecycle of HPC compute clusters,
we also have Azure Cycle Cloud.
This allows IT groups to provide managed clusters in Azure.
Of course, you can use VM scale sets,
or Azure resource manager templates to help you deploy,
manage, and scale your solutions as well.
Once you're up and running, Azure offers
flexible consumption, you can change your infrastructure as
your workload or business needs change.
Here's an example using Cycle Cloud,
to quickly change the virtual machine type you're using.
Azure also provides granular monitoring
of usage and associated costs.
In addition, low priority VMs are allocated from
our surplus compute capacity, this means you can run
batch workloads requiring a large amount
of compute power at up to an 80% discount.
So that was a quick run-through of your options
for high performance computing in Azure.
Azure has high performance computing options
to fit your specific needs across all scales.
If you're interested in the comprehensive set of
compute options in Azure, there are dedicated topics
for IaaS and modern compute in Azure Essentials.
Of course, we're constantly adding new topics on Essentials,
so please keep checking back for more.
You can also learn more at our hands-on learning series,
at the link shown, thanks for watching.
(metallic swooshing)