Obtaining high compute performance on today’s modern computer architectures requires code that is optimized, power efficient, and scalable. The demand for high performance continues to increase due to needs in AI, video analytics, data analytics, as well as in traditional high performance computing (HPC).

Modern workload diversity has resulted in a need for architectural diversity; no single architecture is best for every workload. A mix of scalar, vector, matrix, and spatial (SVMS) architectures deployed in CPU, GPU, AI, and FPGA accelerators is required to extract the needed performance.
Today, coding for CPUs and accelerators requires different languages, libraries, and tools. That means each hardware platform requires completely separate software investments and provides limited application code reusability across different target architectures.

The oneAPI programming model simplifies the programming of CPUs and accelerators using modern C++ features to express parallelism with an open source programming language called Data Parallel C++ (DPC++). The DPC++ language enables code reuse for the host (such as a CPU) and accelerators (such as a GPU) using a single source language, with execution and memory dependencies clearly communicated.

Mapping within the DPC++ code can be used to transition the application to run on the hardware, or set of hardware, that best accelerates the workload. A host is available to simplify development and debugging of device code, even on platforms that do not have an accelerator available. oneAPI also supports programming on CPUs and accelerators using the OpenMP* offload feature with existing C/C++ or Fortran code.