# GPU virtualization and scheduling methods: A comprehensive survey ## Metadata Presented in [CSUR 2017](https://doi.org/10.1145/3068281). Authors: Cheol-Ho Hong, Ivor Spence, Dimitrios S. Nikolopoulos (*Queen’s University Belfast*) ## Understanding the paper GPU virtualization can be implemented on **three approaches**. ### **API remoting** * **Library-level**. * This approach provides **a GPU wrapper library** to guest OS to intercept GPU calls. * The intercepted calls are forwarded to the host OS or a remote machine with GPUs. * The requests will be processed remotely and the results are returned to the guest OS. * The core difficulty is that **the GPU driver implementation is not open source**.

Architecture of the API remoting approach

### **Para & full virtualization** * **Drivel-level**. * This approach uses **a custom GPU driver** based on **the available documentation** to realize GPU virtualization at the driver level. * NVIDIA-related **reverse engineering** is required.

Architecture of the full or para virtualization approach

### **Hardware-supported virtualization** * **Hardware-level**. * This approach requires that a guest OS is given direct access to GPUs with **hardware extension features**. * For example, NVIDIA GRID allows some GPUs to be multiplexed.