For decades, computer architectures have treated memory and storage as separate entities. Nowadays, we watch the emergence of new memory technologies that promise to significantly change the landscape of memory systems. Byte-addressable non-volatile memory (NVM) technologies are expected to offer access latency close to that of dynamic random access memory and capacity suited for storage, resulting in storage-class memory. However, they also present some limitations, such as limited endurance and asymmetric read and write latency. Furthermore, adjusting the current hardware and software architectures to embrace these new memories in all their potential is proving to be a challenge in itself. In this paper, recent studies are analyzed to map the state-of-the-art of NVM file systems research. To achieve this goal, over 100 studies related to NVM systems were selected, analyzed, and categorized according to their topics and contributions. From the information extracted from these papers, we derive the main concerns and challenges currently being studied and discussed in the academia and industry, as well as the trends and solutions being proposed to address them.

For almost 30 years, computer memory systems have been essentially the same: volatile, high speed memory technologies like SRAM and DRAM used for cache and main memory; magnetic disks for high-end data storage; and persistent, low speed flash memory for storage with low capacity/low energy consumption requirements such as embedded/mobile devices. Today we watch the emergence of new non-volatile memory (NVM) technologies that promise to radically change the landscape of memory systems. This work presents system-level latency and energy impacts of a computer architecture with persistent main memory using PCRAM and Memristor. Our experimental results support the feasibility of employing emerging non-volatile memory technologies as persistent main memory, indicating that performance penalties should be mild, and energy improvements should be significant, up to 45.5% less when using PCRAM and 72.4% less when using Memristor.

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.

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