The oneAPI software model provides a comprehensive and unified portfolio of developer tools that can be used across hardware targets, including a range of performance libraries spanning several workload domains. DPC++ is based on industry standards and open specifications to encourage ecosystem collaboration and innovation and is the programming language for oneAPI.
This hands-on training experience will walk you through the process step-by-step using sample code that will teach you how to develop, test, and run your own oneAPI code within the Intel® DevCloud for oneAPI environment.
The Intel® DevCloud for oneAPI is a development sandbox where you can test and optimize your applications and solutions across a range of Intel® CPUs, GPUs, and FPGAs using oneAPI(Beta) software.
Developers who want to learn the basics of Data Parallel C++ for heterogeneous computing (CPU, GPU, FPGA, Accelerators, etc.)
Run DPC++ sample code directly on the Intel® DevCloud for oneAPI using Jupyter notebooks as you read about the essential features of the language.
Access to the Intel® DevCloud for oneAPI, web browser to access and run Jupyter notebooks.
In this series of hands-on lab exercises, the developer is presented with six basic DPC++ programs that illustrate the elements of a DPC++ application. The developer will be presented with the chance to modify the source code in some exercises in order to become familiar with DPC++ program concepts.
These hands-on exercises will show how to implement Unified Shared Memory (USM) in DPC++ code and also demonstrate how to solve for data dependencies for in-order and out-of-order queues.
These hands-on exercises demonstrate the enhanced features of DPC++ with sub-groups. The code samples demonstrate how to implement a query for sub-group info, sub-group collectives, and sub-group shuffle operations.
These two notebooks demonstrate various aspects of Intel® Advisor. The first Jupyter Notebook uses Intel Advisor to show performance offload opportunities of a sample application and then additional command-line options for getting for getting offload advisor results. The second Jupyter Notebook gives an example of roofline analysis and command line options for getting advisor results. Both sets of results are rendered inside of the notebook. These are exploratory and do not require any modification to code.
This module provides a demonstration of device offloading and using Intel® VTune™ Profiler command-line to collect and analyze gpu_hotspots. Using the Jupyter Notebook the developer will learn how to collect performance metrics and be able to explore the results with the HTML output rendering inside of the notebook. This module is exploratory and does not require any code modification.