Training for oneAPI

Essentials of SYCL

Learn the fundamentals of programming in a heterogeneous environment where a CPU, GPU, or accelerator can be programmed to work together or in isolation.

Overview

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

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

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.

Intel® DPC++ Compatibility Tool

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

Get hands-on practice with the tool using a 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.

Intel® oneAPI Math Kernel Library (oneMKL) Essentials

Learn how to use oneMKL and its functions to create performant applications and speed up computations with low-level math routines.

Overview

oneMKL 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.

Performance, Portability, and Productivity

This course is designed for developers who are familiar with SYCL and who develop code that is expected to perform well in a heterogeneous environment. For a primer on SYCL, take the Essentials of SYCL course.

Overview

Developers of HPC applications are faced with an increasingly diverse number of computing platforms that feature multiple generations of CPUs, GPUs, 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.

Heterogeneous 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 heterogeneous 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.

Machine Learning Using oneAPI

Learn how to accelerate machine learning workloads with packages like scikit-learn*, XGBoost, NumPy, SciPy, and pandas—all powered by oneAPI. Learn how to get performance gains with zero to a few extra lines of code. Accelerate on the latest Intel hardware, including CPUs and GPUs, using stock versions or use Intel® Extension for Scikit-learn*, which is part of AI Tools.

Overview

AI Tools give data scientists, AI developers, and researchers familiar Python tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architecture. The components are built using oneAPI libraries for low-level compute optimizations. This toolkit maximizes performance from preprocessing through machine learning, and provides interoperability for efficient model development.