Hey everyone, welcome to the November
episode of oneAPI Dev News,
I’m your host Jerry Makare.
We’ve got a couple
great things to go through,
but before we do that,
I just want to remind you
that you can learn
more about the topics
we cover by following the links
in the description.
First up, lets take a quick
look at how you can design
And optimize high-Performance
application code with Intel Advisor.
When designing a software application,
there are a variety
of heterogeneous system
configurations available,
making it
a challenging task
to efficiently leverage
the computational capabilities
of multiple devices,
especially when targeting
multiple such configurations.
The same is true when optimizing
existing code to yield
a high-performance application
that takes full advantage
of your hardware setup.
Sometimes, designing
performant code
and further optimizing
it requires you to understand
the underlying hardware
so that your application
can achieve its full potential.
You want to balance
the potential overhead
of managing workload dispatch
on a multi-architecture platform
against software portability
and performance potential.
To make designing and optimizing
highly performant application code
easier, the oneAPI programming
model provides
a flexible tool called Intel Advisor.
This design and analysis
tool helps you: Design
and optimize code
for scalable parallel execution
through task and thread management.
Identify the best ways
to have efficient vectorization,
GPU offloading,
memory usage, and thread-parallelism.
It Analyzes
your code and presents you
with the dependency flow graphs,
a great way to explore the dependencies
and understand the execution flow
of your application.
Code across diverse
architectures with support
for C, C++, SYCL, Python, Fortran,
OpenMP and OpenCL.
Read the rest of the blog
that focuses on important
aspects of performant
application design and optimization
that Intel Advisor can help you with.
You’ll get more information
about and insights into:
Roofline analysis.
Vectorization insights.
Estimate effects of GPU offloading.
AND you’ll Explore threading design.
Next up, watch the video
featuring our colleagues
Jack and Sri, as the share
a demo of text-to-image generative
AI running on a CPU and a GPU
with the same TensorFlow code.
AI workloads require
high performance
from development to deployment,
often across a variety
of compute environments.
Intel AI software, powered by oneAPI,
gets the most performance
out of your Intel CPUs and GPUs.
And it’s
supported across frameworks
such as PyTorch and TensorFlow.
This short demo shows Stable Diffusion,
a compute-intensive text-to-image
generative AI model,
running on a 4th Gen Intel
Xeon Scalable processor
and an Intel Flex Series GPU
with no code changes required
to switch devices.
This example runs in TensorFlow
using Intel Extension for TensorFlow.
oneAPI libraries
enable this example
to also run in PyTorch
with similar performance.
Watch the video and follow the links
to see how you can unlock generative
AI with software powered by oneAPI.
AND our final topic is about the Intel
Developer Cloud.
Accelerate your development
using Intel-optimized software
on the latest Intel
Xeon processors and GPUs.
Sign up to get hand-on experience
with the latest Intel products,
evaluate prerelease Intel
platforms and associated
Intel-optimized software stacks,
and speed up
AI deployments with the latest machine
learning toolkits!
Follow the resource links to see
the get started guide,
see what’s
new, and Access free
digital courses to improve your skills.
Don’t let hardware availability
block your next code,
sign up and get started
using the Intel Developer Cloud today!
That’s everything for this month!
As always thank you for joining me,
and I look forward to seeing you
next month.
Remember to like and share this video
AND subscribe
to the Intel Software YouTube channel
to keep up to date with all
your oneAPI Dev News!