Orthogonal persistence in nonvolatile memory architectures: A persistent heap design and its implementation for a Java Virtual Machine

Abstract

Summary Current computer systems separate main memory from storage, and programming languages typically reflect this distinction using different representations for data in memory and storage. However, moving data back and forth between these different layers and representations compromise both programming and execution efficiency. To remedy this, the concept of orthogonal persistence (OP) was proposed in the early 1980s advocating that, from a programmer's standpoint, there should be no differences in the way that short‐term and long‐term data are manipulated. However, at that time, the underlying implementations still had to cope with the complexity of moving data across memory and storage. Today, recent nonvolatile memory (NVM) technologies, such as resistive RAM and phase‐change memory, allow main memory and storage to be collapsed into a single layer of persistent memory, opening the way for more efficient programming abstractions for handling persistence. In this work, we revisit OP concepts in the context of NVM architectures and propose a persistent heap design for languages with automatic memory management. We demonstrate how it can significantly increase programmer and execution efficiency, removing the impedance mismatch of crossing semantic boundaries. To validate and demonstrate the presented concepts, we present JaphaVM, an implementation of the proposed design based on JamVM, an open‐source Java Virtual Machine. Our results show that JaphaVM, in most cases, executes the same operations between one and two orders of magnitude faster than regular database‐based and file‐based implementations, while requiring significantly less lines of code.

SPHT: A scalable and high‐performance hashing scheme for persistent memory

“…With near‐DRAM access latency, high capacity, and disk‐like persistence, PM devices are promising to replace DRAM as the next‐generation main memory 2‐4 . The rapid development of PM technologies has significantly affected the design of today's storage systems 5‐10 . It is crucial for large‐scale storage systems to build high‐performance indexing structures for PM to achieve fast queries.…”

“…systems. [5][6][7][8][9][10] It is crucial for large-scale storage systems to build high-performance indexing structures for PM to achieve fast queries.…”