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/