Persistent memory hash indexes

2021 ◽  
Vol 14 (5) ◽  
pp. 785-798
Author(s):  
Daokun Hu ◽  
Zhiwen Chen ◽  
Jianbing Wu ◽  
Jianhua Sun ◽  
Hao Chen
Keyword(s):  
Future Development ◽  
High Performance ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Hash Tables ◽  
Trade Offs ◽  
Depth Analysis ◽  
Persistent Memory ◽  
Memory Modules

Persistent memory (PM) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery, especially with the recent release of Intel Optane DC Persistent Memory Modules. However, most of them are evaluated on DRAM-based emulators with unreal assumptions, or focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how well the proposed hash indexes perform on real PM and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of six state-of-the-art hash tables including Level hashing, CCEH, Dash, PCLHT, Clevel, and SOFT, with real PM hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PM bandwidth, CPU instructions, and NUMA) affect the performance of PM-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts, and suggest desirable optimizations and directions for the future development of PM-based hash tables.

Octopus + : An RDMA-Enabled Distributed Persistent Memory File System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-25
Author(s):  
Bohong Zhu ◽  
Youmin Chen ◽  
Qing Wang ◽  
Youyou Lu ◽  
Jiwu Shu
Keyword(s):  
High Speed ◽  
High Performance ◽  
File System ◽  
Direct Memory Access ◽  
File Systems ◽  
Distributed File Systems ◽  
Persistent Memory ◽  
Memory Modules ◽  
Non Volatile Memory ◽  
Volatile Memory

Non-volatile memory and remote direct memory access (RDMA) provide extremely high performance in storage and network hardware. However, existing distributed file systems strictly isolate file system and network layers, and the heavy layered software designs leave high-speed hardware under-exploited. In this article, we propose an RDMA-enabled distributed persistent memory file system, Octopus + , to redesign file system internal mechanisms by closely coupling non-volatile memory and RDMA features. For data operations, Octopus + directly accesses a shared persistent memory pool to reduce memory copying overhead, and actively fetches and pushes data all in clients to rebalance the load between the server and network. For metadata operations, Octopus + introduces self-identified remote procedure calls for immediate notification between file systems and networking, and an efficient distributed transaction mechanism for consistency. Octopus + is enabled with replication feature to provide better availability. Evaluations on Intel Optane DC Persistent Memory Modules show that Octopus + achieves nearly the raw bandwidth for large I/Os and orders of magnitude better performance than existing distributed file systems.

scholarly journals An overview of touchless 2D fingerprint recognition

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jannis Priesnitz ◽  
Christian Rathgeb ◽  
Nicolas Buchmann ◽  
Christoph Busch ◽  
Marian Margraf
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Attack Detection ◽  
Fingerprint Recognition ◽  
Research Groups ◽  
Trade Offs ◽  
Reliable Detection ◽  
Recognition Systems ◽  
Open Issues ◽  
Research Resources

AbstractTouchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work.

Elastic Parallel Systems for High Performance Cloud Computing: State-of-the-Art and Future Directions

2019 ◽  
Vol 29 (02) ◽  
pp. 1950006 ◽  
Author(s):  
Stefan Kehrer ◽  
Wolfgang Blochinger
Keyword(s):  
Conceptual Framework ◽  
High Performance ◽  
State Of The Art ◽  
Specific Property ◽  
Parallel Systems ◽  
Future Research ◽  
Future Directions ◽  
Ex Post ◽  
Depth Analysis ◽  
Performance Computing

With on-demand access to compute resources, pay-per-use, and elasticity, the cloud evolved into an attractive execution environment for High Performance Computing (HPC). Whereas elasticity, which is often referred to as the most beneficial cloud-specific property, has been heavily used in the context of interactive (multi-tier) applications, elasticity-related research in the HPC domain is still in its infancy. Existing parallel computing theory as well as traditional metrics to analytically evaluate parallel systems do not comprehensively consider elasticity, i.e., the ability to control the number of processing units at runtime. To address these issues, we introduce a conceptual framework to understand elasticity in the context of parallel systems, define the term elastic parallel system, and discuss novel metrics for both elasticity control at runtime as well as the ex-post performance evaluation of elastic parallel systems. Based on the conceptual framework, we provide an in-depth analysis of existing research in the field to describe the state-of-the-art and compile our findings into a research agenda for future research on elastic parallel systems.

Scaling Dynamic Hash Tables on Real Persistent Memory

2021 ◽  
Vol 50 (1) ◽  
pp. 87-94
Author(s):  
Baotong Lu ◽  
Xiangpeng Hao ◽  
Tianzheng Wang ◽  
Eric Lo
Keyword(s):  
Hash Table ◽  
High Load ◽  
Low Latency ◽  
Load Factor ◽  
Hash Tables ◽  
Persistent Memory ◽  
Memory Modules

Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piecewise and partial solutions that side-stepped many important properties, in particular good scalability, high load factor and instant recovery.

scholarly journals Recent Developments in Tandem White Organic Light-Emitting Diodes

Molecules ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 151 ◽  
Author(s):  
Peng Xiao ◽  
Junhua Huang ◽  
Yicong Yu ◽  
Baiquan Liu
Keyword(s):  
Future Development ◽  
Single Unit ◽  
High Performance ◽  
Light Emitting Diodes ◽  
State Of The Art ◽  
Organic Light Emitting Diodes ◽  
Light Emitting ◽  
Thermally Activated ◽  
Organic Light ◽  
Recent Developments

Tandem white organic light-emitting diodes (WOLEDs) are promising for the lighting and displays field since their current efficiency, external quantum efficiency and lifetime can be strikingly enhanced compared with single-unit devices. In this invited review, we have firstly described fundamental concepts of tandem device architectures and their use in WOLEDs. Then, we have summarized the state-of-the-art strategies to achieve high-performance tandem WOLEDs in recent years. Specifically, we have highlighted the developments in the four types of tandem WOLEDs (i.e., tandem fluorescent WOLEDs, tandem phosphorescent WOLEDs, tandem thermally activated delayed fluorescent WOLEDs, and tandem hybrid WOLEDs). Furthermore, we have introduced doping-free tandem WOLEDs. In the end, we have given an outlook for the future development of tandem WOLEDs.

Content and composition of phospholipids, fatty acids and sterols in commercial natural phospholipid excipients

2020 ◽  
Vol 16 ◽  
Author(s):  
Luxia Zheng ◽  
Xiong Shen ◽  
Yingchun Wang ◽  
Jian Liang ◽  
Mingming Xu ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Cluster Analysis ◽  
Fatty Acid ◽  
High Performance ◽  
Comprehensive Evaluation ◽  
Phospholipid Fatty Acid ◽  
Egg Yolk ◽  
Systematic Research ◽  
Manufacturing Enterprises ◽  
The Many

Background: Phospholipids are widely used in food and pharmaceutical industry as functional excipients. In spite of the many analytical methods reported, there are very limited reports concerning systematic research and comparison of phospholipid excipients. Objective: To present a comprehensive evaluation of commercial natural phospholipid excipients (CNPEs). Methods: Seventeen batches of CNPEs from five manufacturing enterprises, isolated either from soybean or egg yolk, were investigated. The content and composition of phospholipids, fatty acids and sterols as a whole were considered as the evaluative index of CNPEs. Eight kinds of phospholipids were determined by supercritical fluid chromatography (SFC), twenty-one kinds of fatty acids were determined by gas chromatography (GC) after boron trifluoride-methanol derivatization, and nine kinds of sterols were determined by high performance liquid chromatography (HPLC) after separation and derivatization of the unsaponifiable matter. Cluster analysis was employed for classification and identification of the CNPEs. Results: The results showed that each kind of CNPEs had its characteristic content and composition of phospholipids, fatty acids and sterols. Seventeen batches of samples were divided into eight groups in cluster analysis. CNPEs of the same type from different source (soybean or egg yolk) or enterprises presented different content and composition of phospholipids, fatty acids and sterols. Conclusion: Each type of CNPEs had its characteristic content and composition of phospholipid, fatty acid and sterol. The compositions of phospholipid, fatty acid and sterol as a whole can be applied as an indicator of the quality and characteristics for CNPEs.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

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