Efficient Logging in Non-Volatile Memory by Exploiting Coherency Protocols

Non-volatile memory (NVM) technologies such as PCM, ReRAM and STT-RAM allow processors to directly write values to persistent storage at speeds that are significantly faster than previous durable media such as hard drives or SSDs. Many applications of NVM are constructed on a logging subsystem, which enables operations to appear to execute atomically and facilitates recovery from failures. Writes to NVM, however, pass through a processor's memory system, which can delay and reorder them and can impair the correctness and cost of logging algorithms. Reordering arises because of out-of-order execution in a CPU and the inter-processor cache coherence protocol. By carefully considering the properties of these reorderings, this paper develops a logging protocol that requires only one round trip to non-volatile memory while avoiding expensive computations. We show how to extend the logging protocol to building a persistent set (hash map) that also requires only a single round trip to non-volatile memory for insertion, updating, or deletion

Procedures and tools for acquisition and analysis of volatile memory on android smartphones

Mobile phone forensics have become more prominent since mobile phones have become ubiquitous both for personal and business practice. Android smartphones show tremendous growth in the global market share. Many researchers and works show the procedures and techniques for the acquisition and analysis the non-volatile memory inmobile phones. On the other hand, the physical memory (RAM) on the smartphone might retain incriminating evidence that could be acquired and analysed by the examiner. This study reveals the proper procedure for acquiring the volatile memory inthe Android smartphone and discusses the use of Linux Memory Extraction (LiME) for dumping the volatile memory. The study also discusses the analysis process of the memory image with Volatility 2.3, especially how the application shows its capability analysis. Despite its advancement there are two major concerns for both applications. First, the examiners have to gain root privileges before executing LiME. Second, both applications have no generic solution or approach. On the other hand, currently there is no other tool or option that might give the same result as LiME and Volatility 2.3

Volatile and Non-Volatile Single Electron Memory

Multi Tunnel Junctions (MTJs) have attracted much attention recently in the fields of Single-Electron Devices (SED) in particular Single-Electron Memory (SEM). In this paper, we have design and study a nano-device structure using a two dimensional array MTJs for Volatile and Non-Volatile-SEM, in order to analyze the impact of physical parameters on the performances. We investigate the single-electron circuit characteristics in our devices qualitatively, using single-electron Monte Carlo simulator SIMON. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3192

Disaggregating non-volatile memory for throughput-oriented genomics workloads

Massive exploitation of next-generation sequencing technologies requires dealing with both: huge amounts of data and complex bioinformatics pipelines. Computing architectures have evolved to deal with these problems, enabling approaches that were unfeasible years ago: accelerators and Non-Volatile Memories (NVM) are becoming widely used to enhance the most demanding workloads. However, bioinformatics workloads are usually part of bigger pipelines with different and dynamic needs in terms of resources. The introduction of Software Defined Infrastructures (SDI) for data centers provides roots to dramatically increase the efficiency in the management of infrastructures. SDI enables new ways to structure hardware resources through disaggregation, and provides new hardware composability and sharing mechanisms to deploy workloads in more flexible ways. In this paper we study a state-of-the-art genomics application, SMUFIN, aiming to address the challenges of future HPC facilities.This work is partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitivity (TIN2015-65316-P) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Energy-Efficient Streaming Using Non-volatile Memory

The disk and the DRAM in a typical mobile system consume a significant fraction (up to 30%) of the total system energy. To save on storage energy, the DRAM should be small and the disk should be spun down for long periods of time. We show that this can be achieved for predominantly streaming workloads by connecting the disk to the DRAM via a large non-volatile memory (NVM). We refer to this as the NVM-based architecture (NVMBA); the conventional architecture with only a DRAM and a disk is referred to as DRAMBA. The NVM in the NVMBA acts as a traffic reshaper from the disk to the DRAM. The total system costs are balanced, since the cost increase due to adding the NVM is compensated by the decrease in DRAM cost. We analyze the energy saving of NVMBA, with NAND flash memory serving as NVM, relative to DRAMBA with respect to (1) the streaming demand, (2) the disk form factor, (3) the best-effort provision, and (4) the stream location on the disk. We present a worst-case analysis of the reliability of the disk drive and the flash memory, and show that a small flash capacity is sufficient to operate the system over a year at negligible cost. Disk lifetime is superior to flash, so that is of no concern