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Task Allocation on Nonvolatile-Memory-Based Hybrid Main Memory

机译:基于非易失性内存的混合主内存上的任务分配

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In this paper, we consider the task allocation problem on a hybrid main memory composed of nonvolatile memory (NVM) and dynamic random access memory (DRAM). Compared to the conventional memory technology DRAM, the emerging NVM has excellent energy performance since it consumes orders of magnitude less leakage power. On the other hand, most types of NVMs come with the disadvantages of much shorter write endurance and longer write latency as opposed to DRAM. By leveraging the energy efficiency of NVM and long write endurance of DRAM, this paper explores task allocation techniques on hybrid memory for multiple objectives such as minimizing the energy consumption, extending the lifetime, and minimizing the memory size. The contributions of this paper are twofold. First, we design the integer linear programming (ILP) formulations that can solve different objectives optimally. Then, we propose two sets of heuristic algorithms including three polynomial time offline heuristics and three online heuristics. Experiments show that compared to the optimal solutions generated by the ILP formulations, the offline heuristics can produce near-optimal results.
机译:在本文中,我们考虑了由非易失性存储器(NVM)和动态随机存取存储器(DRAM)组成的混合主存储器上的任务分配问题。与传统的内存技术DRAM相比,新兴的NVM具有出色的能源性能,因为它消耗的泄漏功率少几个数量级。另一方面,与DRAM相比,大多数类型的NVM都具有写入寿命短得多和写入等待时间长的缺点。通过利用NVM的能源效率和DRAM的长写入耐久性,本文探索了混合内存上的任务分配技术,以实现多个目标,例如最小化能耗,延长寿命和最小化内存大小。本文的贡献是双重的。首先,我们设计可优化解决不同目标的整数线性规划(ILP)公式。然后,我们提出了两组启发式算法,包括三种多项式时间离线启发式算法和三种在线启发式算法。实验表明,与ILP公式生成的最佳解决方案相比,离线启发式方法可以产生接近最佳的结果。

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