--- title: Imagination GPU Driver Update Adds Support for Additional Vulkan and OpenCL Extensions description: Imagination updates its GPU driver, adding Vulkan and OpenCL extensions to enhance performance, memory management, and virtualisation for Linux and Android platforms. image: https://blog.imaginationtech.com/hubfs/ddk-blog-banner-oct-25.jpg --- [![Imagination](https://blog.imaginationtech.com/hubfs/Imagination_December2019%20Theme/Images/Header%20Logo%20White.svg) ![Imagination](https://blog.imaginationtech.com/hubfs/Imagination_December2019%20Theme/Images/Header%20Logo%20Black.svg)](https://blog.imaginationtech.com/) - [Products & Solutions](https://www.imaginationtech.com/products/) - [GPU](https://www.imaginationtech.com/products/gpu/) - [CPU](https://www.imaginationtech.com/products/cpu/) - [AI](https://www.imaginationtech.com/products/ai/) - [Ethernet](https://www.imaginationtech.com/products/ethernet/) - [Ray Tracing](https://www.imaginationtech.com/products/ray-tracing/) - 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08 October 2025 - [Patrik Masson](https://blog.imaginationtech.com/author/patrik-masson) In our latest updated, our customers can now grab the latest version of the Linux and Android Driver Development Kits from the Partner Portal. Here are some of the highlights of what has been updated:  ### Leveraging Cooperative Matrix in Vulkan To help accelerate graphics post-processing, neural shaders, physics simulations, and machine learning inference on the GPU, DDK 25.2 implements support for **VK\_KHR\_cooperative\_matrix**. This extension provides Vulkan developers with a standardised and widely supported way to handle cooperative matrix operations, which are particularly useful for high-performance matmul in compute shaders. Cooperative matrix types are medium-sized matrices that are primarily supported in compute shaders, where the storage for the matrix is spread across all invocations in a given scope. These invocations cooperate to run matrix multiplies in a parallelised and optimised manner. They are an ideal fit for the deeply integrated AI acceleration inside the new [Imagination E-Series GPUs](https://www.imaginationtech.com/products/e-series/). ### Vulkan Pipeline Compilation Rearchitecture As Vulkan has evolved, developer needs have shifted, making the original model of compiling complete pipeline-shader objects upfront increasingly impractical. Many new extensions reflect this shift, introducing more flexible, dynamic pipeline behaviour. However, these changes challenge traditional architectures that rely on static pipeline-state assumptions. To address this, we’re extensively re-architecting our Vulkan driver’s shader and pipeline compilation process. This overhaul introduces a modular compilation framework, streamlined development workflows, and improved management of pipeline variants. Enhancements in caching and hashing reduce runtime hitching, while new capabilities for late-stage shader linking and re-compilation provide a foundation for future innovations to support modern Vulkan development and evolving application demands. ### OpenCL Memory Management Improvements Developers can now use pinned memory via **CL\_USE\_HOST\_PTR** and **CL\_ALLOC\_HOST\_PTR**. This is an effective tool for bandwidth-intensive applications, repetitive transfers and memory transfer limited segments of applications. We’ve also introduced the ability to import externally allocated memory, such as buffers shared via DMA-BUF file descriptors, into OpenCL as Unified Shared Virtual Memory (SVM). This enables seamless, zero-copy data sharing between OpenCL and other components (e.g., video decoders, camera drivers, or other compute APIs) that support DMA-BUF. By mapping external memory into the OpenCL SVM space, applications can: - Avoid redundant memory copies between subsystems. - Achieve lower latency and higher throughput in data-intensive pipelines. - Maintain unified memory access semantics across host and device. ### Better Power Management In Virtualised Systems Virtualisation is a popular feature, particularly in the automotive market where the GPU in a multi-domain controller is supporting the operating systems for the in-vehicle infotainment, cockpit and ADAS all at once. Customers that employ GPU virtualisation can now take advantage of GPU dynamic voltage and frequency scaling (DVFS) on platforms. --- *Imagination maintains its Android, Windows, and open-source and proprietary Linux drivers with regular updates that give customers access to the latest operating systems and APIs (and their popular extensions), as well as performance optimisations.* *This release, 25.2 RTM1, will be maintained for at least one year for bug fixes and security vulnerabilities.* *To find out more,* [***request a meeting with one of our team.***](https://www.imaginationtech.com/contact-us/) [GPU](https://blog.imaginationtech.com/tag/gpu) #### Share this post - - [mailto:?subject=https://blog.imaginationtech.com/driver-update-adds-support-for-vulkan-and-opencl-extensions](mailto:?subject=https://blog.imaginationtech.com/driver-update-adds-support-for-vulkan-and-opencl-extensions) ##### About the Author ![Picture of Patrik Masson](https://blog.imaginationtech.com/hs-fs/hubfs/patrik-masson.jpeg?width=60&name=patrik-masson.jpeg) [Patrik Masson](https://blog.imaginationtech.com/author/patrik-masson) Patrik is Director of Product Management at Imagination Technologies, where he leads the strategy and development of the company's software and developer tools. With over a decade of experience in industrial automation, managing extensive product portfolios through their full lifecycles, he focuses on delivering value for Imagination's customers and developer ecosystem across the entire software stack. [More from Patrik Masson](https://blog.imaginationtech.com/author/patrik-masson) ### Subscribe to our blog ### Introducing E-Series GPU Imagination E-Series GPU IP breaks through the barriers of throughput, memory, and power at the edge.Built for next-gen AI, E-Series delivers high performance and adaptability across mobile, automotive, desktop, and consumer markets, avoiding the rigidity of fixed-function accelerators while retaining the flexibility of GPU programmability. 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