The provided sources offer a comprehensive look at memory management for GPU-accelerated computing, focusing heavily on **Heterogeneous Memory Management (HMM)** and **NVIDIA Unified Virtual Memory (UVM)**. One source details the release of the **CUDA Toolkit 12.2**, highlighting new features like HMM support, **NVIDIA Hopper (H100) GPU** compatibility, and **Confidential Computing** for secure data environments. Another source focuses exclusively on HMM, explaining how this feature **simplifies GPU programming** by allowing direct access to system-allocated memory, thereby eliminating the need for explicit memory calls like `cudaMallocManaged`. The third source, a technical paper, performs an **in-depth performance analysis of UVM**, examining the **overhead associated with transparent paging** and migration, identifying key performance bottlenecks like **Host OS interactions** (e.g., page unmapping) and the efficiency of **fault batching** and **prefetching** mechanisms.
Source:
https://tallendev.github.io/assets/papers/sc21.pdf
https://developer.nvidia.com/blog/nvidia-cuda-toolkit-12-2-unleashes-powerful-features-for-boosting-applications/
https://developer.nvidia.com/blog/simplifying-gpu-application-development-with-heterogeneous-memory-management/
