Memory and Parallelism Analysis Using a Platform-Independent Approach
作者: Stefano CordaGagandeep SinghAhsan Javed AwanRoel JordansHenk Corporaal
作者单位: Eindhoven University of Technology and IBM Research - Zurich;;Eindhoven University of Technology and IBM Research - Zurich;;Ericsson Research;;Eindhoven University of Technology;;Eindhoven University of Technology
论文集英文名称: Software and Compilers for Embedded Systems
来源数据库: Association for Computing Machinery
DOI: 10.1145/3323439.3323988
关键词: Computation In MemoryMemristorNon-Volatile MemoryNon-Von Neumann ArchitectureSimulator
原始语种摘要: Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.
全文获取路径: ACM 

  • applications 应用程序
  • platform 台地
  • memory 记忆
  • Analysis 分析
  • software 软件
  • entropy 平均信息量
  • metrics 规格
  • detecting 检测
  • computing 计算
  • parallelism 平行性