5 key benefits of Intel oneAPI 2024 Toolkits

What is Intel oneAPI?

Intel’s oneAPI is an open, cross-industry, standards-based, unified programming model that delivers a common developer experience across accelerator architectures. It is intended to be used across different computing accelerator architectures, including GPUs, AI accelerators, and field-programmable gate arrays.

oneAPI is designed to enable developers to program across CPUs, GPUs, FPGAs, and other accelerators. It offers performance advantages, regardless of the hardware architectures (CPU, GPU, FGPA, or accelerators), libraries, languages, or frameworks you use.

The Intel oneAPI 2024 Toolkits are a suite of developer tools that accelerate AI, HPC, and rendering applications on various platforms, including Intel CPUs, GPUs, and AI accelerators.

Intel oneAPI 2024 benefits

• Future-Ready Programming – Accelerates performance on the latest Intel GPUs including added support for Python, Modin, XGBoost, and rendering; supports upcoming 5th Gen Intel^® Xeon^® Scalable and Intel^® Core™ Ultra CPUs; and expands AI and HPC capabilities via broadened standards coverage across multiple tools.
• AI Acceleration – Speeds up AI and machine learning on Intel CPUs and GPUs with native support through Intel-optimised PyTorch and TensorFlow frameworks; delivers faster performance and deployments using standard Python for numeric workloads in Intel^® Distribution of Python.
• Vector Math Optimisations – oneMKL integrates RNG offload on target devices for HPC simulations, statistical sampling, and more on x86 CPUs and Intel GPUs, and supports FP16 datatype on Intel GPUs.
• Improved CUDA-to-SYCL Migration – Intel^® DPC++ Compatibility Tool (based on open source SYCLomatic) adds CUDA library APIs and 20 popular applications in AI, deep learning, cryptography, scientific simulation, and imaging.
• Advanced Preview Features – These evaluation previews include C++ parallel STL for easy GPU offload, dynamic device selection to optimise compute node resource usage, SYCL graph for reduced GPU offload overhead, thread composability to prevent thread oversubscription in OpenMP, and profile offloaded code to NPUs.

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