Training for oneAPI

Essentials of Data Parallel C++

Learn the fundamentals of Data Parallel C++ (DPC++) programming in a heterogeneous environment where a CPU, GPU, FPGA, or accelerator can be programmed to work together or in isolation.

Overview

The language and API extensions in DPC++ enable different development use cases.

• Develop new offload acceleration or heterogeneous computer applications
• Convert existing C or C++ code to SYCL and DPC++
• Migrate your code from other accelerator languages or frameworks

Use this learning path to get hands-on practice with the essentials of DPC++ using Jupyter* Notebooks on Intel® DevCloud.

In addition to this training, the book, Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems Using C++ and SYCL, is now available.

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OpenMP* Offload Basics

Learn the fundamentals of using OpenMP* offload directives to target GPUs through hands-on practice in this guided learning path.

Overview

OpenMP* offload constructs are a set of directives for C++ and Fortran that allow you to offload data and execution to target accelerators such as GPUs. The constructs are supported in the Intel® oneAPI HPC Toolkit with the Intel® C++ Compiler and the Intel® Fortran Compiler.

Use this learning path to get hands-on practice with the essentials of DPC++ using Jupyter Notebooks on Intel DevCloud.

Intel® DPC++ Compatibility Tool

Learn how the tool assists you in the migration of your CUDA* program to DPC++ via this self-guided, step-by-step tutorial.

Overview

The Intel® DPC++  Compatibility Tool assists you in migrating a CUDA* program to Data Parallel C++ (DPC++), which is based on modern C++ and incorporates portable industry standards such as SYCL*. 

Get hands-on practice with the tool using this guided Jupyter Notebook on your local machine.

Intel® OSPRay Essentials

Learn how to use the Intel® OSPRay renderer and its API to create high-fidelity photorealistic images and scenes using simple geometries.

Overview

Intel® OSPRay is an open-source, scalable, and portable ray tracing engine for high-performance and scientific visualization applications. Effects such as ambient occlusion, shadows, and transparency can be rendered to enable new insight into huge data. 

Use this learning path to get hands-on practice with the essentials of Intel® OSPRay using Jupyter Notebooks on Intel DevCloud.

Intel® oneAPI Math Kernel Library (oneMKL) Essentials

Learn how to use the Intel® oneAPI Math Kernel Library (oneMKL) and its functions to create performant applications and speed up computations with low-level math routines.

Overview

The Intel® oneAPI Math Kernel Library enhances math routines such as vector and matrix operations from Basic Linear Algebra Subprograms (BLAS) and the Linear Algebra Package (LAPACK), fast Fourier transforms (FFT), and random number generator (RNG) functions.

Use this learning path to get hands-on practice with oneMKL using Jupyter Notebooks on Intel DevCloud.

FPGA Development Flow Using Intel® oneAPI Base Toolkit

Learn how to emulate FPGA hardware on the host, generate report files and optimize your design for the FPGA architecture, and compile and run your application on the FPGA fabric.

Overview

The Intel® FPGA Add-on for oneAPI Base Toolkit enables you to compile and run custom designs on FPGAs by generating bitstreams and configuring the hardware accelerator to meet the application's needs. 

Use this learning path to get hands-on practice with the Intel® oneAPI Base Toolkit and Intel® FPGA Add-on for oneAPI Base Toolkit using Jupyter Notebooks on Intel DevCloud.

Performance, Portability, and Productivity

This course is designed for developers who are familiar with DPC++ and who develop code that is expected to perform well in a heterogenous environment. For a primer on DPC++, take the Essentials of Data Parallel C++ course.

Overview

Developers of high-performance computing applications are faced with an increasingly diverse number of computing platforms that feature multiple generations of CPUs, GPUs, FPGAs, and other accelerators. Developing code that is performant and portable across a diverse set of platforms can be expensive and time-consuming to achieve the best result.

Heterogenous Programming Using Numba-Data Parallel Python* for AI & HPC

This data parallel Python* course demonstrates high-performing code targeting Intel® XPUs using Python. Developers learn how to take advantage of heterogenous architectures and speed up applications without using low-level proprietary programming APIs. 

Overview

Python has become a useful tool in advancing scientific research and computation with very rich ecosystem of open-source packages for mathematics, science, and engineering, anchored on the performant numerical computation on arrays and matrices, data analysis and visualization capabilities.

Preinstalled with the latest Intel hardware, frameworks, tools, and libraries, the Intel® DevCloud is a free service that enables you to get started without installing software locally. Sign up to gain access.

• Jupyter Notebook tutorials
• Basic shell commands
• Basic vi command
• Queue management and job submission
• Advanced queue management

What is the Intel DevCloud

Just Minutes to Your oneAPI Project on Intel® DevCloud

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