Virtual Memory Stitching (VMS)

• Virtual Memory Stitching (VMS) is a technique that aggregates non-contiguous physical memory regions into a single virtual address space, enhancing flexibility and reducing fragmentation.
• It employs methods like segmentation-based allocation, granular page sharing, and GPU memory management APIs to streamline resource use in cloud computing and deep learning environments.
• Practical implementations have demonstrated doubled VM densities and significant GPU memory savings, underscoring its role in improving system performance and scalability.

Virtual Memory Stitching (VMS) refers to a collection of techniques and system architectures that enable the fusion, aggregation, or dynamic composition of physically non-contiguous memory regions into a coherent contiguous virtual address space. The stitched virtual memory can span disparate blocks within one system or across multiple devices and hosts. VMS is used to minimize memory fragmentation, maximize resource utilization, enable rapid scaling of virtualized workloads, and facilitate seamless access to large datasets or model parameters, especially in environments with complex memory allocation patterns such as clouds or GPU-accelerated deep learning systems.

1. Conceptual Foundations of Virtual Memory Stitching

VMS generalizes the principle of decoupling the virtual address space from the underlying physical memory layout. Instead of enforcing direct contiguity at the physical level, VMS mechanisms rely on advanced virtual memory mapping primitives or segmentation strategies to aggregate arbitrary-sized memory fragments into one logical region. Key aspects include support for:

• Memory oversubscription via thin provisioning and granular page sharing (Moniruzzaman et al., 2014)
• Dynamic creation of stitched blocks at allocation time using low-level memory management APIs (Guo et al., 16 Jan 2024)
• Segmentation at the hypervisor layer to create large virtual memory segments composed from several physical blocks (Teabe et al., 2020)
• GPU-driven virtual memory management, allowing device-resident page tables and direct manipulation of stitched memory mappings without CPU/OS intervention (Nazaraliyev et al., 8 Nov 2024)

In cloud, OS, and accelerator contexts, VMS addresses the constraints of traditional paging, boot-time allocation, and physical contiguity by leveraging virtualization, copy-on-write, live migration, advanced page mapping APIs, and cross-device data transfer protocols.

2. Core Techniques in VMS Architectures

VMS employs a variety of architectural, system, and hardware primitives to enable efficient stitching:

• Granular Page Sharing and Copy-on-Write (CoW): By maintaining sharable pages among clones and utilizing policy-based memory managers, identical data among multiple VMs is deduplicated and memory is allocated dynamically (Moniruzzaman et al., 2014).
• Segmentation-based Allocation: Systems such as Compromis replace page-based allocation with direct segment mapping—defined by (host base + guest offset)—to provision VM memory and reduce translation latency (Teabe et al., 2020). The DS-n formulation for n segments:

$hpa = HBRreg_i + (gpa - GBRreg_i)$

supports composing non-contiguous blocks into virtually contiguous segments.

• GPU Virtual Memory Management APIs: CUDA primitives such as cuMemAddressReserve, cuMemCreate, and cuMemMap enable the mapping of multiple physically separate blocks into a single reserved virtual address range. In GMLake, sBlocks are stitched from primitive pBlocks using these APIs (Guo et al., 16 Jan 2024):

$$\text{VA}_s \leftarrow \text{cuMemAddressReserve}(S)$$

$$\text{cuMemMap}(\text{VA}_s^{(i)}, \text{PA}_i, \text{size}(p_i))$$

• RDMA-driven Memory Migration: GPUVM demonstrates GPU thread-initiated on-demand paging and migration between host and device using an RDMA-capable NIC, bypassing CPU/OS overhead. This is essential for stitched memory pools spanning multiple hosts or accelerators (Nazaraliyev et al., 8 Nov 2024).
• Advanced Simulation and Verification Frameworks: Modal abstractions using separation logic and virtual points-to relationships, mechanized in frameworks such as Iris, allow formal verification of stitched virtual address spaces and multi-rooted page table transitions (Kuru et al., 2023). Simulation platforms like Virtuoso facilitate rapid prototyping and validation of VMS schemes under realistic system models (Kanellopoulos et al., 7 Mar 2024).

3. Use Cases and Implementations

Cloud and Virtual Machine Scaling

VMS is instrumental for rapid provisioning and live migration in cloud computing. By instantiating VMs from pre-booted live images and streaming memory on demand, VMS-based systems achieve:

• VM startup times up to 2x–10x faster than traditional boot processes
• Memory oversubscription, effectively doubling the number of VMs per physical machine (Moniruzzaman et al., 2014)

OpenStack-based architectures integrate extensions to their Nova API and KVM backends to enable VMS operations, including “live-image-create” and automated on-the-fly memory streaming. Stitched memory pools are dynamically managed so that VM clones launch nearly instantaneously and share common memory elements.

GPU Deep Learning Training

LLMs and DNNs benefit from VMS by reducing GPU memory fragmentation and enabling larger batch sizes:

• GMLake fuses multiple non-contiguous GPU memory blocks using CUDA VMM APIs, reducing GPU memory usage by 9.2 GB (on average) and up to 25 GB in extreme cases, with a reduction of fragmentation up to 33% on A100 80GB GPUs (Guo et al., 16 Jan 2024).
• The allocation subsystem employs two-tier pools (primitive and stitched), executing “BestFit” allocation routines to minimize fragmentation and optimize memory utilization.

System-wide Unified Memory and Data Migration

GPUVM demonstrates a GPU-driven, RDMA-enabled memory system for high-throughput page migration and physical-virtual mapping:

• Enables on-demand paged memory with latency up to 4x lower than classical unified virtual memory (UVM) systems
• Fine-grained page management (4–8KB) and reference-counted eviction logic allow stitched memory pools to be transparently migrated without CPU/OS bottlenecks (Nazaraliyev et al., 8 Nov 2024)

Simulation and Formal Verification

Virtuoso provides simulation capabilities for evaluating VMS schemes at high fidelity:

• Modular component models for TLBs, page tables, and contiguous memory allocation facilitate direct experimentation with VMS algorithms and page mapping structures (Kanellopoulos et al., 7 Mar 2024).

The modal abstraction and separation logic techniques presented by (Kuru et al., 2023) allow formal verification of VMS routines and correct composition of address spaces, critical for operating system security and correctness in multi-domain environments.

4. Technical Challenges and Solutions

Some intrinsic challenges arise in VMS design and deployment:

• Fragmentation & Allocation Overhead: VMS enables aggregation of fragmented physical memory at the cost of more complex mapping logic. Solutions involve best-fit search routines, stitching only when exact-fit blocks are unavailable, and amortizing allocation overhead via pool reuse (Guo et al., 16 Jan 2024).
• Translation Latency: Multi-segment address translation requires additional register manipulations or page table walks. DS-n schemes minimize this by limiting the number of segments and relying on arithmetic translation rather than multi-level lookups (Teabe et al., 2020).
• Concurrency and Coordination: Handling thousands of concurrent memory requests (e.g., page faults on GPU) requires warp-level synchronization primitives and leader election protocols for fault coalescing (Nazaraliyev et al., 8 Nov 2024).
• Compatibility and Portability: Transitioning legacy systems expecting paged memory to segmentation or stitched mapping models requires hardware and software adaptation. Modal abstraction frameworks and simulation platforms can prototype compatibility approaches (Kuru et al., 2023, Kanellopoulos et al., 7 Mar 2024).

5. Quantitative Impact and Evaluation

The effectiveness of VMS is measured through multiple metrics:

System	Metric	Reported Impact
Cloud VM launch	Startup times	2x–10x faster than baseline
Cloud VM density	Memory oversubscription	2x increase in VMs per host
GPU DNN training	GPU memory usage	Avg. 9.2 GB (up to 25 GB) saved
GPU DNN training	Memory fragmentation	Avg. 15% (up to 33%) reduction
GPU Unified Mem	Latency-bound app throughput	Up to 4x compared to UVM
VM migration	Migration time	Up to 10.18% faster with PML (Bitchebe et al., 2020)

Benchmarks such as HPL Linpack validate that VMS-enabled working set estimation and allocation maintain system stability and prevent crashes due to underestimation (Bitchebe et al., 2020).

6. Extensions and Applications Beyond Classical VM

VMS principles extend to domains beyond traditional system memory:

• Medical Image Stitching: SX-Stitch adapts a segmentation-and-stitching pipeline to rapidly and robustly fuse medical X-ray image regions, harnessing context-aware neural architectures (VMS-UNet) and optimized energy functions for image alignment (Li et al., 9 Sep 2024). Hybrid energy functions integrating color, geometric, and semantic costs (§7) enable seamless medical image fusion, outperforming SOTA methods.
• Formal Verification: The modal abstraction techniques of (Kuru et al., 2023) and Iris-based mechanized logic provide a sound foundation for formally reasoning about stitched address spaces, page table updates, and correctness under context switches or dynamic stitching operations.

7. Future Directions and Open Questions

Emerging directions in VMS include:

• Extending stitching to cross-hardware, geographically distributed, or heterogeneous device pools (potentially enabling multi-tenant, multi-cloud resource composability).
• Investigating dynamic resizing and real-time segment updates, requiring fast, non-intrusive register and mapping updates.
• Formal modular verification of stitched address spaces using advanced separation logic and modal abstractions in large-scale, security-sensitive kernels.

Potential open challenges involve mitigating fragmentation in highly variable allocation environments, integrating segmentation-based VMS into legacy OS and hypervisor implementations, and enabling transparent stitching across rapidly evolving hardware memory hierarchies.

8. Summary

Virtual Memory Stitching synthesizes a spectrum of system techniques—memory allocation algorithms, hardware primitives, simulator platforms, and formal verification methods—to allow efficient, flexible fusion of disparate physical memory resources. VMS directly addresses limits in scalability, resource utilization, and speed endemic to legacy virtual memory systems, realizing substantial gains in cloud VM density, GPU deep learning scalability, and application throughput. Rigorous evaluations and formal modeling frameworks demonstrate VMS’s technical soundness while leaving open significant avenues for continued research in multi-domain memory management, cross-device stitching, and correctness verification.