Persistent memory (PM) is expected to augment or replace DRAM as main memory. PM combines byte-addressability with non-volatility, providing an opportunity to host byte-addressable data persistently. There are two main approaches for utilizing PM: either as memory mapped files or as persistent memory objects (PMOs). Memory mapped files require that programmers reconcile two different semantics (file system and virtual memory) for the same underlying data, and require the programmer use complicated transaction semantics to keep data crash consistent.
To solve this problem, the first part of this dissertation designs, implements, and evaluates a new PMO abstraction that addresses these problems by hosting data in pointer-rich data structures without the backing of a filesystem, and introduces a new primitive, psync, that when invoked renders data crash consistent while concealing the implementation details from the programmer via shadowing. This new approach outperforms a state-of-the-art memory mapped design by 3.2 times depending on the workload. It also addresses the security of at-rest PMOs, by providing for encryption and integrity verification of PMOs. To do this, it performs encryption and integrity verification on the entire PMO, which adds an overhead of between 3-46% depending on the level of protection.
The second part of this dissertation demonstrates how crash consistency, security, and integrity verification can be conserved while the overall overhead is reduced by decrypting individual memory pages instead of the entire PMO, yielding performance improvements compared to the original whole PMO design of 2.62 times depending on the workload.
The final part of this dissertation improves the performance of PMOs even further by mapping userspace pages to volatile memory and copying them into PM, rather than directly writing to PM. Bundling this design with a stream buffer predictor to decrypt pages into DRAM ahead of time improves performance by 1.9 times.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1192 |
| Date | 01 January 2024 |
| Creators | Greenspan, Derrick Alex |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
| Rights | In copyright |
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