Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (4)
  • Open Access

    ARTICLE

    Characterization of Memory Access in Deep Learning and Its Implications in Memory Management

    Jeongha Lee1, Hyokyung Bahn2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 607-629, 2023, DOI:10.32604/cmc.2023.039236

    Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when comparing read and write accesses,… More >

  • Open Access

    ARTICLE

    An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

    Jaehwan Lee1, Sangoh Park2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 171-186, 2023, DOI:10.32604/csse.2023.038139

    Abstract Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch and address the limitations of… More >

  • Open Access

    ARTICLE

    Key-Value Store Coupled with an Operating System for Storing Large-Scale Values

    Jeonghwan Im1, Hyuk-Yoon Kwon2,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3333-3350, 2022, DOI:10.32604/cmc.2022.029566

    Abstract The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair. Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility. However, the research efforts storing the large-scale values such as multimedia data files (e.g., images or videos) in the key-value store were limited. In this study, we propose a new key-value store, WR-Store++ aiming to store the large-scale values stably. Specifically, it provides… More >

  • Open Access

    ARTICLE

    Mobile Memory Management System Based on User’s Application Usage Patterns

    Jaehwan Lee, Sangoh Park*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4031-4050, 2021, DOI:10.32604/cmc.2021.017872

    Abstract Currently, the number of functions to improve user convenience in smartphone applications is increasing. In addition, more mobile applications are being loaded into mobile operating system memory for faster launches, thus increasing the memory requirements for smartphones. The memory used by applications in mobile operating systems is managed using software; allocated memory is freed up by either considering the usage state of the application or terminating the least recently used (LRU) application. As LRU-based memory management schemes do not consider the application launch frequency in a low memory situation, currently used mobile operating systems can lead to the termination of… More >

Displaying 1-10 on page 1 of 4. Per Page