The Intel® Technologies and Platforms program provides courseware related to the growing portfolio of Intel products and technologies delivering solutions to help the networking industry, including Communication Service Providers and the ecosystem, to bring advanced performance and intelligence from the core of the data center to the network edge.
In this course, Srihari Makineni, Senior Principal Engineer at Intel Corporation, discusses the 2nd Generation Intel® Xeon® Scalable processors and the three CPU SKUs that are optimized for NFV workloads. He reviews the motivation for creating these SKUs, their benefits and how to enable the SKUs and use their features. He details how to enable Intel® Speed Select Technology - Base Frequency (Intel® SST-BF), its impact on the platform and how the platform thermals change as a result of using the P1 frequency and the Intel SST-BF feature. He then walks through the steps required to enable Intel SST-BF as well as how to validate the configuration.
This video is part of the Network Transformation Experience Kit video series. In this video M Jay, DPDK/NFV Platform Application Engineer at Intel introduces Intel® Speed Select Technology – Base Frequency (Intel® SST-BF). He examines what Intel® SST-BF does, what processors it is available on and what the benefits are.
In this course, Sergey Kiselev, Software Technical Consulting Engineer at Intel, provides an introduction to Intel® System Studio which is an embedded software development tool suite. This course explains several of the tools available in the suite and how Intel System Studio can simplify system and IoT application development.
This course provides a deep-dive demonstration on how Intel® VTune™ Amplifier can be used to profile and optimize a network based workload. There are a number of demonstrations and activities related to a real world scenario where a software issue in the network is identified and requires troubleshooting.
This course provides an introduction to vectorization including why it is important, basic terminology and the role of single instruction, multiple data (SIMD). The lab activity demonstrates how to use using Intel® C++ Compiler and Intel® Math Kernel Library (Intel® MKL) for vectorization.
This course will introduce Intel® Speed Select Technology – Base Frequency (Intel® SST-BF) and Intel® VTune™ Amplifier - Platform Profiler. The lab activity will use Intel VTune Amplifier – Platform Profiler to analyze the impact of Intel SST-BF. The lab will demonstrate a performance test of a VNF workload, analyze and interpret those results and discuss and evaluate the findings.
This video is part of the Network Transformation Experience Kit video series. In this video M Jay, DPDK/NFV Platform Application Engineer at Intel introduces Dynamic Device Personalization (DDP). To keep up with growing network demands, Intel® Ethernet with DDP provides the ability to reconfigure the packet processing pipeline to support a broader range of traffic types. Learn what problems DDP addresses how it works, and the benefits to users.
This course provides a demonstration on how platform telemetry can be utilized to achieve power savings by interactively changing CPU core frequencies.
Course 9: Closed Loop Automation - Telemetry Aware Scheduler for Service Healing and Platform Resilience Demo
This is a closed loop automation, platform resiliency demonstration that minimizes service outage time and therefore maximizes service availability. The course shows that by using Intel® Architecture platform specific metrics and events, we can monitor the health of the platform and take remediation actions when issues arise.
In this demonstration, Dr.-Ing. David I. González-Aguirre, Robotic Research Scientists at Intel Labs, showcases real-time human imitation by a robot in a high-performance, low-latency process featuring Wi-Fi 6 and the 2nd Generation Intel® Xeon® Scalable Processor.
In this course, Alberto Villarreal introduces a new feature in the third generation Intel® Xeon® Scalable processors designed to accelerate Deep Learning training: The Bfloat16, which improves deep learning training workloads.