Intel® oneAPI
A Unified X-Architecture Programming Model

• The oneAPI specification extends existing developer programming models to enable a diverse set of hardware through language, a set of library APIs, and a low level hardware interface to support cross-architecture programming. To promote compatibility and enable developer productivity and innovation, the oneAPI specification builds upon industry standards and provides an open, cross-platform developer stack.

Realize All of the Hardware Value

• Expose and exploit the latest hardware's cutting-edge features to unleash application performance across CPUS, GPUS, FPGAs, and other accelerators.

Develop Performant Code Quickly and Correctly

• Achieve fast and efficient development with a complete set of cross-architecture libraries and tools that use familiar languages and standards to make heterogeneous development easier.

Intel® oneAPI Toolkits

• Toolkits contain optimized compilers, libraries, frameworks, and analysis tools purpose-built for developers who perform similar tasks. They include implementations of the oneAPI specification along with complementary tools to develop and deploy applications and solutions across Intel® CPU and XPU architectures.

  • Intel oneAPI Base Toolkit - Develop performant, date-centric applications across Intel CPUs, GPUs, and FPGAs with this foundational toolset.​

    • Collective Communications Library​

    • Data Analytics Library

    • Deep Neural Networks Library

    • DPC++/C++ Compiler

    • DPC++ Library

    • Math Kernel Library

    • Threading Library

    • Threading Building Blocks

    • VIdeo Processing Library

    • Advisor

    • Distribution for GDB

    • Distribution for Python

    • DPC++ Compatibility Tool

    • FPGA Add-on for oneAPI Base Toolkit

    • Integrated Performance Primitives

    • Vtune Profiler

  • Intel oneAPI HPC Toolkit - Build, analyze, and scale applications across shared- and distributed-memory computing systems.

    • DPC++/C++ Compiler​

    • C++ Compiler Classic

    • Cluster Check

    • Fortran Compiler (Beta)

    • Fortran Compiler Classic

    • Inspector

    • MPI Library

    • Trace Analyzer and Collector

  • Intel oneAPI Analytics Toolkit - Accelerate end-to-end data science and machine learning pipelines using Python tools and frameworks.

    • Distribution for Python including highly-optimized scikit-learn and XGBoost libraries​

    • Optimization for PyTorch

    • Optimization for Tensorflow

    • Optimization of Modin

    • Low Precision Optimization

    • Model Zoo for Intel Architecture

  • Intel Distribution of OpenVINO toolkit (Powered by oneAPI) - Deploy high-performance inference applications from edge to cloud.​​

  • Intel oneAPI Rendering Toolkit - Create high-fidelity, photorealistic experiences that push the boundaries of visualization.

  • Intel oneAPI IoT Toolkit - Fast-track development of applications and solutions that run at the network's edge.

    • DPC++/C++ Compiler​

    • C++ Compiler Classic

    • Inspector

    • IDE Plugins

    • IoT Connection Tools

    • Linux Kernel Build Tools

  • Intel System Bring-up Toolkit - Strengthen system reliability with hardware and software insight, and optimize power and performance.

The Language

• At the core of the oneAPI specification is DPC++, an open, cross-architecture language built upon the ISO C++ and Khronos SYCL standards. DPC++ extends these standards and provides explicit parallel constructs and offload interfaces to support a broad range of computing architectures and processors, including CPUs and accelerator architectures. Other languages and programming models can be supported on the oneAPI platform via the Accelerator Interface.

The Libraries

• oneAPI provides libraries for compute and data intensive domains. They include deep learning, scientific computing, video analytics, and media processing.

The Hardware Abstraction Layers

• The low-level hardware interface defines a set of capabilities and services that allow a language runtime to utilize a hardware accelerator.

Advanced Ray Tracing

• Advanced ray tracing defines a set of ray tracing and high-fidelity rendering and computation routines for use in a wide variety of 3D graphics uses including, film and television photorealistic visual effects and animation rendering, scientific visualization, high-performance computing computations, gaming, and more. 

• Advanced ray tracing is designed to allow cooperative execution on a wide variety of computational devices: CPUs, GPUs, FPGAs, and other accelerators, termed “XPU” computation.

• The functionality is subdivided into several domains: geometric ray tracing computations, volumetric computation and rendering, path guided ray tracing, image denoising, and an integrated rendering infrastructure and API utilizing all the individual kernel capabilities integrated into a highly capable, easy to use rendering engine.

oneDNN Graph API

• oneDNN Graph API extends oneDNN with a unified high-level graph API for multiple AI hardware classes (CPU, GPU, accelerators). With a flexible graph interface, it maximizes the optimization opportunity for generating efficient code across a variety of Intel and non-Intel HW, and can be closely integrated with ecosystem framework and inference engines.

• oneDNN Graph API accepts a deep learning computation graph as input and performs graph partitioning, where nodes that are candidates for fusion are grouped together. oneDNN Graph compiles and executes a group of deep learning operations in a graph partition as a fused operation.

• With the graph as input, oneDNN Graph implementation can perform target-specific optimization and code generation on a larger scope, which allows it to map the operation to hardware resources and improve execution efficiency and data locality with a global view of the computation graph. With the rapid introduction of hardware support for dense compute, the deep learning workload characteristic changed significantly from a few hot spots on compute-intensive operations to a broad number of operations scattering across the applications.

• Accelerating a few compute-intensive operations using primitive API has diminishing returns and limits the performance potential. It is critical to have a graph API to better exploit hardware compute capacity.