A Survey of Non-Volatile Main Memory Technologies: State-of-the-Arts, Practices, and Future Directions

Liu, Haikun; Chen, Di; Jin, Hai; Liao, Xiaofei; He, Bingsheng; Hu, Kan

doi:10.1007/s11390-020-0780-z

Cited by 23 publications

(11 citation statements)

References 123 publications

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“…The motivation behind this performance metric is as follows. It has been heavily advocated that persistent memories significantly outperform traditional dynamic random access memories (DRAMs) due to low standby power, higher memory density, and much lower cost/bit [30,31]. However, persistent memories suffer from the write endurance problem, i.e., every persistent memory (PM) unit can sustain a very limited number of writes (i.e., stores) before it wears-out.…”

Section: Contributionsmentioning

confidence: 99%

Adaptive Versioning in Transactional Memory Systems

Poudel

Sharma

2021

Algorithms

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Transactional memory has been receiving much attention from both academia and industry. In transactional memory, program code is split into transactions, blocks of code that appear to execute atomically. Transactions are executed speculatively and the speculative execution is supported through data versioning mechanism. Lazy versioning makes aborts fast but penalizes commits, whereas eager versioning makes commits fast but penalizes aborts. However, whether to use eager or lazy versioning to execute those transactions is still a hotly debated topic. Lazy versioning seems appropriate for write-dominated workloads and transactions in high contention scenarios whereas eager versioning seems appropriate for read-dominated workloads and transactions in low contention scenarios. This necessitates a priori knowledge on the workload and contention scenario to select an appropriate versioning method to achieve better performance. In this article, we present an adaptive versioning approach, called Adaptive, that dynamically switches between eager and lazy versioning at runtime, without the need of a priori knowledge on the workload and contention scenario but based on appropriate system parameters, so that the performance of a transactional memory system is always better than that is obtained using either eager or lazy versioning individually. We provide Adaptive for both persistent and non-persistent transactional memory systems using performance parameters appropriate for those systems. We implemented our adaptive versioning approach in the latest software transactional memory distribution TinySTM and extensively evaluated it through 5 micro-benchmarks and 8 complex benchmarks from STAMP and STAMPEDE suites. The results show significant benefits of our approach. Specifically, in persistent TM systems, our approach achieved performance improvements as much as 1.5× for execution time and as much as 240× for number of aborts, whereas our approach achieved performance improvements as much as 6.3× for execution time and as much as 170× for number of aborts in non-persistent transactional memory systems.

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Section: Contributionsmentioning

confidence: 99%

Adaptive Versioning in Transactional Memory Systems

Poudel

Sharma

2021

Algorithms

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“…Optane™ DCPMM [1] is a relatively new hardware, still not fully present in any supercomputer worldwide. Thus, it was subjected so far only to limited examination on scientific workloads.…”

Section: Related Workmentioning

confidence: 99%

“…These standard block devices offer large and relatively cheap non-volatile storage, but their access times are orders-ofmagnitude slower than those of DRAM. Optane™ DataCenter Persistent Memory Module (DCPMM) [1], a new technology introduced by Intel, provides non-volatile memory that can be plugged into standard memory bus slots (DDR DIMMs) and therefore be accessed much faster than standard storage devices. In this work, we present and analyze the results of a comprehensive performance assessment of several ways in which DCPMM can 1) replace standard storage devices, and 2) replace or augment DRAM for improving the performance of HPC scientific computations.…”

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confidence: 99%

Assessing the Use Cases of Persistent Memory in High-Performance Scientific Computing

Yehonatan¹,

Snir²,

Rusanovsky³

et al. 2021

Preprint

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As the High Performance Computing (HPC) world moves towards the Exa-Scale era, huge amounts of data should be analyzed, manipulated and stored. In the traditional storage/memory hierarchy, each compute node retains its data objects in its local volatile DRAM. Whenever the DRAM's capacity becomes insufficient for storing this data, the computation should either be distributed between several compute nodes, or some portion of these data objects must be stored in a non-volatile block device such as a hard disk drive (HDD) or an SSD storage device. These standard block devices offer large and relatively cheap non-volatile storage, but their access times are orders-ofmagnitude slower than those of DRAM. Optane™ DataCenter Persistent Memory Module (DCPMM) [1], a new technology introduced by Intel, provides non-volatile memory that can be plugged into standard memory bus slots (DDR DIMMs) and therefore be accessed much faster than standard storage devices. In this work, we present and analyze the results of a comprehensive performance assessment of several ways in which DCPMM can 1) replace standard storage devices, and 2) replace or augment DRAM for improving the performance of HPC scientific computations. To achieve this goal, we have configured an HPC system such that DCPMM can service I/O operations of scientific applications, replace standard storage devices and file systems (specifically for diagnostics and checkpoint-restarting), and serve for expanding applications' main memory. We focus on keeping the scientific codes with as few changes as possible, while allowing them to access the NVM transparently as if they access persistent storage. Our results show that DCPMM allows scientific applications to fully utilize nodes' locality by providing them with sufficiently-large main memory. Moreover, it can also be used for providing a high-performance replacement for persistent storage. Thus, the usage of DCPMM has the potential of replacing standard HDD and SSD storage devices in HPC architectures and enabling a more efficient platform for modern supercomputing applications. The source code used by this work, as well as the benchmarks and other relevant sources, are available at: https://github.com/ Scientific-Computing-Lab-NRCN/StoringStorage.

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“…Now, every unit of the organization can get all kinds of information needed from the database at any time and process it in its own unit. Simultaneously, the transaction processing system within the organization and the customer support system outside the organization are gradually connected, and a unified EDI standard compatible with various industries has been established, thus forming a supply chain process management system [13]. Each actor becomes the knowledge node in the internal supply chain of the organization, and the competitiveness of each technical link in the organization depends on the innovation power of these knowledge nodes [14].…”

Section: Introductionmentioning

confidence: 99%

Action Mechanism and Model of Cross-Border E-Commerce Green Supply Chain Based on Customer Behavior

Zhang

Su-kun

2021

Mathematical Problems in Engineering

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In recent years, China’s cross-border e-commerce has flourished, and the transaction volume has increased year by year. Cross-border e-commerce has become a favorable breakthrough point for China's foreign trade. This article mainly studies the action mechanism and model of cross-border e-commerce green supply chain based on customer behavior. Green supply chain partners select 24 secondary indicators of the evaluation system as the input vector. The historical data of each index is collected by field investigation as sample data and brought into the neural network for training. The output vector of the only output layer of the network is used as the evaluation result of the supplier. This paper divides the operation mode of green supply chain into four stages and puts forward improvement tools for the functional modules in each stage. Enterprises can use the tools in the modules to improve the operation efficiency of green supply chain. According to the green level evaluation demand of green supply chain, this paper uses the hierarchical method to evaluate it. According to the survey results, this paper uses arithmetic average method to deal with the operation and establishes a secondary index after decomposition. Finally, this paper uses confirmatory factor analysis to test the measurement model and further uses analysis of variance to test the relationship between the two types of social cues and behavioral willingness. The data shows that the orderly fluctuation range of the east, middle, and west cross-border logistics subsystems basically remains at around 0.2 to 0.6. The results show that the establishment of the green supply chain model has a very positive significance for the implementation and development of the green supply chain in China’s manufacturing industry. Through the research of profit and profit distribution in the green supply chain, it provides guidance for the green supply chain to effectively select the supply chain members to cooperate and calculate and distribute the profit reasonably, so that the green supply chain management can be widely used in reality.

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A Survey of Non-Volatile Main Memory Technologies: State-of-the-Arts, Practices, and Future Directions

Cited by 23 publications

References 123 publications

Adaptive Versioning in Transactional Memory Systems

Adaptive Versioning in Transactional Memory Systems

Assessing the Use Cases of Persistent Memory in High-Performance Scientific Computing

Action Mechanism and Model of Cross-Border E-Commerce Green Supply Chain Based on Customer Behavior

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