PDL Abstract

Page Overlays: An Enhanced Virtual Memory Framework to Enable Fine-grained Memory Management

Proceedings of the 42nd International Symposium on Computer Architecture (ISCA), Portland, OR,
June 2015.

Vivek Seshadri, Gennady Pekhimenko, Olatunji Ruwase*, Onur Mutlu, Phillip B. Gibbons†,
Michael A. Kozuch†, Todd C. Mowry, Trishul Chilimb*

Carnegie Mellon University
†Intel Labs Pittsburgh
*Microsoft Research


Many recent works propose mechanisms demonstrating the potential advantages of managing memory at a fine (e.g., cache line) granularity—e.g., fine-grained deduplication and fine-grained memory protection. Unfortunately, existing virtual memory systems track memory at a larger granularity (e.g., 4 KB pages), inhibiting efficient implementation of such techniques. Simply reducing the page size results in an unacceptable increase in page table overhead and TLB pressure.

We propose a new virtual memory framework that enables efficient implementation of a variety of fine-grained memory management techniques. In our framework, each virtual page can be mapped to a structure called a page overlay, in addition to a regular physical page. An overlay contains a subset of cache lines from the virtual page. Cache lines that are present in the overlay are accessed from there and all other cache lines are accessed from the regular physical page. Our page-overlay framework enables cache-line-granularity memory management without significantly altering the existing virtual memory framework or introducing high overheads.

We show that our framework can enable simple and efficient implementations of seven memory management techniques, each of which has a wide variety of applications. We quantitatively evaluate the potential benefits of two of these techniques: overlay-on-write and sparse-data-structure computation. Our evaluations show that overlay-on-write, when applied to fork, can improve performance by 15% and reduce memory capacity requirements by 53% on average compared to traditional copy-on-write. For sparse data computation, our framework can outperform a state-of-the-art software-based sparse representation on a number of real-world sparse matrices. Our framework is general, powerful, and effective in enabling fine-grained memory management at low cost.