Mojo

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

OpenCL (Open Computing Language) is an open, royalty-free standard for cross-platform parallel programming of heterogeneous computing systems that lets developers accelerate computing tasks by leveraging diverse processors such as CPUs, GPUs, DSPs, and FPGAs across supercomputers, cloud servers, personal computers, mobile devices, and embedded platforms. It defines a programming framework including a C-based language for writing compute kernels and a runtime API to control devices, manage memory, and execute parallel code, giving portable and efficient access to heterogeneous hardware. OpenCL improves speed and responsiveness for a wide range of applications including creative tools, scientific and medical software, vision processing, and neural network training and inferencing by offloading compute-intensive work to accelerator processors.