Hello everyone and welcome to
another episode of oneAPI Dev News from the IDZ Studios in Oregon.
I'm your host, Jerry Makare, and I'm very happy to be here with you.
Additional information and resources for all of the topics that we cover today
can be found by following the links in the description field.
Our first topic
today looks at a guide that will help you explore SYCL with samples from Intel.
SYCL applications are C++ programs for parallelism.
SYCL is designed for parallel programing and heterogeneous computing
and provides a consistent programing language, C++, and APIs
across CPU, GPU, FPGA and AI accelerators.
Each architecture can be programed and used either in isolation or together,
which allows developers to learn once and then program for distinct accelerators.
Each class of accelerator requires an appropriate formulation
and tuning of the algorithms for best performance, but the language
and programing model remains consistent regardless of the target device.
The guide aims to help developers
understand how to program using the oneAPI programing model
and how to target and optimize for the appropriate architecture
to achieve optimal application performance.
Take a look at the guide
and then check out some code walkthroughs to see all of the additional resources.
Next up, learn
how you can achieve performance advantage with Intel oneAPI tools and Fourth
Gen Intel Xeon Scalable processors featuring built in accelerator engines.
The January 2023 launch of the Fourth Gen Intel Xeon and Intel
Max series processors
not only delivered the most built in accelerators of any CPU in the world,
they delivered leadership performance for the most important computing
challenges across AI analytics, networking, security,
storage and high performance computing.
The latest Intel accelerator engines include Intel Advanced Matrix Extensions,
which improves the performance of deep learning, training and inference.
It is ideal for workloads like natural language processing,
recommendation systems and image recognition.
Intel Quick Assist Technology, which helps free up processor cores
by offloading encryption, decryption and compression
so systems can serve a larger number of clients or use less power.
With Intel Quick Assist Technology Fourth Gen Intel
Xeon scalable processors are the highest performance CPU's.
They can compress and encrypt in a single data flow.
Intel Data Streaming Accelerator, which drives high performance for storage,
networking and data intensive workloads by improving streaming data movement
and transformation operations designed to offload the most common
data movement tasks the cores overhead and data center scale deployments.
Intel DSA helps speed up data movement across the CPU,
memory caches, all attached memory storage and network devices.
Intel In-memory Analytics Accelerator which helps run databases
and analytics workloads faster with potentially greater power efficiency.
This built in accelerator increases query throughput and decreases the memory
footprint for in-memory databases and big data analytics workloads.
Intel AI is ideal for in-memory databases, open
source databases and data stores like RockDB and ClickHouse.
Developers can realize the full potential and value of these new built-in
accelerator features and instruction sets with Intel oneAPI and AI tools,
A complete set of advanced compilers, libraries, analysis and debug tools
and optimized frameworks that simplify the development
and deployment of accelerated solutions.
a complete set of advanced compilers, libraries,
analysis and debug tools, and optimized frameworks
that simplify the development and deployment of accelerated solutions.
Dig into this technical article to learn more,
and to see it broken down by workload type.
AND finally, learn
about the essential tools for jumpstarting your AI development projects.
AI projects are compute- and data-intensive.
Many projects start with hardware platforms
chosen and optimized only for the training phase of AI.
However, data manipulation and preprocessing can slow
development productivity.
Often deployed models must run in browsers
or on edge devices, which means a larger memory footprint,
while still providing fast turnaround on a different processing architecture.
As a result, many AI projects must maintain multiple code repositories.
Imagine if you could create just one TensorFlow project and run the same code
for all processors.
Or if you could train and run your PyTorch models and automatically use
the best configurations and optimizations for the hardware it's running on.
Or imagine if you could analyze your entire
end to end machine learning pipelines using just one tool.
This is what Intel's AI tools, libraries and framework optimizations
built on the one API open standard provide for AI developers.
Read the article that examines one API and explore some of Intel's many tools
built on it to see how it makes AI development
more straightforward and manageable.
Thank you for joining me today.
Please check out all of the topics we've mentioned
and check out all the resources that are available to you.
That's all for this month, if you like this video, please share it
and remember to subscribe to the Intel Software YouTube channel.
I'll see you next month for another episode of oneAPI Dev News.