An Empirical Guide to the Behavior and Use of Scalable Persistent Memory

TL;DR

This paper provides a comprehensive analysis of Intel's 3D XPoint NVDIMM, offering performance insights, best practices for programming, and reevaluation of existing software to optimize the use of this new persistent memory technology.

Contribution

It delivers the first detailed performance characterization of 3D XPoint DIMMs and offers practical guidelines for software optimization and programming on these devices.

Findings

3D XPoint DIMMs have unique performance characteristics distinct from DRAM.

Best practices significantly improve application performance on 3D XPoint memory.

Reevaluation of prior software shows performance gains when applying new guidelines.

Abstract

After nearly a decade of anticipation, scalable nonvolatile memory DIMMs are finally commercially available with the release of Intel's 3D XPoint DIMM. This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. Researchers have not idly waited for real nonvolatile DIMMs (NVDIMMs) to arrive. Over the past decade, they have written a slew of papers proposing new programming models, file systems, libraries, and applications built to exploit the performance and flexibility that NVDIMMs promised to deliver. Those papers drew conclusions and made design decisions without detailed knowledge of how real NVDIMMs would behave or how industry would integrate them into computer architectures. Now that 3D XPoint NVDIMMs are actually here, we can provide detailed performance numbers, concrete guidance for programmers on these systems, reevaluate prior art for performance, and reoptimize persistent memory software for the real 3D XPoint DIMM. In this paper, we explore the performance properties and characteristics of Intel's new 3D XPoint DIMM at the micro and macro level. First, we investigate the basic characteristics of the device, taking special note of the particular ways in which its performance is peculiar relative to traditional DRAM or other past methods used to emulate NVM. From these observations, we recommend a set of best practices to maximize the performance of the device. With our improved understanding, we then explore the performance of prior art in application-level software for persistent memory, taking note of where their performance was influenced by our guidelines.