# 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.
Architecture of the hardware-supported approach
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