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NPAM: NVM-Aware Page Allocation for Multi-Core Embedded Systems

IEEE TRANSACTIONS ON COMPUTERS 2017年第10期66卷 1703-1716页

作者： Poursafaei, Farimah R. Bazzaz, Mostafa Ejlali, Alireza Sharif Univ Technol Dept Comp Engn Tehran *** Iran

Energy consumption is one of the prominent design constraints of multi-core embedded systems. Since the memory subsystem is responsible for a considerable portion of energy consumption of embedded systems, Non-Volatile Memories (NVMs) have been proposed as a candidate for replacing conventional memories such as SRAM and DRAM. The advantages of NVMs compared to conventional memories are that they consume less leakage power and provide higher density. However, these memories suffer from increased overhead of write operations and limited lifetime. In order to address these issues, researchers have proposed NVM-aware memory management techniques that consider the characteristics of the memories of the system when deciding on the placement of the application data. In systems equipped with memory management unit (MMU), the application data is partitioned into pages during the compile phase and the data is managed at page level during the runtime phase. In this paper we present an NVM-aware data partitioning and mapping technique for multi-core embedded systems equipped with MMU that specifies the placement of the application data based on access pattern of the data and characteristics of the memories. The experimental results show that the proposed technique improves the energy consumption of the system by 28.10 percent on average.

关键词： Energy-efficient embedded systems emerging technologies hierarchical data placement memory management

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A hierarchical model of data locality 06

A hierarchical model of data locality

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33rd Symposium on Principles of Programming Languages

作者： Zhang, CL Ding, C Ogihara, M Zhong, YT Wu, YF Univ Rochester Dept Comp Sci Rochester NY 14627 USA George Mason Univ Dept Comp Sci Fairfax VA 22030 USA Intel Labs Programming Sys Res Lab Santa Clara CA 95052 USA

ISBN: (纸本)9781595930279

In POPL 2002, Petrank and Rawitz showed a universal result-finding optimal data placement is not only NP-hard but also impossible to approximate within a constant factor if P not equal NP. Here we study a recently published concept called reference affinity, which characterizes a group of data that are always accessed together in computation. On the theoretical side, we give the complexity for finding reference affinity in program traces, using a novel reduction that converts the notion of distance into satisfiability. We also prove that reference affinity automatically captures the hierarchical locality in divide-and-conquer computations including matrix solvers and N-body simulation. The proof establishes formal links between computation patterns in time and locality relations in space. On the practical side, we show that efficient heuristics exist. In particular, we present a sampling method and show that it is more effective than the previously published technique, especially for data that are often but not always accessed together. We show the effect on generated and real traces. These theoretical and empirical results demonstrate that effective data placement is still attainable in general-purpose programs because common (albeit not all) locality patterns can be precisely modeled and efficiently analyzed.

关键词： theory algorithms performance hierarchical data placement program locality reference affinity volume distance NP-complete N-body simulation

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A hierarchical model of data locality

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ACM SIGPLAN NOTICES 2006年第1期41卷 16-29页

关键词： theory algorithms performance hierarchical data placement program locality reference affinity volume distance NP-complete N-body simulation

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