Highlights include improved performance and standardization with the DPC++ compiler, VTune support of the latest Intel GPUs and GPU pinning with the Intel MPI Library.
* Introduced Model Zoo in the Intel® AI Analytics Toolkit including pretrained models and sample scripts for many popular open source deep learning topologies optimized for Intel architectures.
* Intel® AI Analytics Toolkit adds GPU support for DBSCAN and SVM algorithms and many CPU optimizations for scikit-learn algorithms. Added new scikit-ipp 1.0.0, a drop-in replacement for scikit-image package to accelerate image processing functions. Includes XGBoost 1.1 release with the latest histogram tree grow method optimized for Intel CPUs for faster training.
* Improved DPC++ compiler performance for CPU platforms
* Simplified and modernized DPC++ language definition by leveraging newer standard C++ language features.
* Intel® VTune™ Profiler now supports the latest Intel GPUs: Gen9 and Gen11 integrated graphics, and pre-released Gen12 integrated and discrete GPUs and adds an improved GPU Memory Hierarchy diagram annotated with GPU hardware performance metrics.
* Intel® MPI Library introduced initial GPU pinning support for Intel Xe architecture devices and expanded support for Mellanox ConnectX.
* Improved migration of CUDA math, texture, and parallelism library calls in the Intel DPC++ Compatibility Tool
* Intel® System Debugger provides now a new auto-detection mechanism in the target connection assistant that helps quickly establish a system debug connection to a target platform. The system debugger also enhanced system TraceCLI configuration support that allows developers to easily set-up this interface in both interactive and scripting modes, and added a system debug sample for developers to easily explore and learn the system debug capabilities.
* DevCloud FPGA Full Access will be available June 23, 2020.