Memory energy management for an enterprise decision support system

Kumar, Vishwajeet; Doshi,; Dimitrov,; Lu, Chi-Jie

doi:10.1109/islped.2011.5993649

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…However, if the process is memory bound, increasing the CPU performance will not significantly speed up execution [28] while still using more power. Thus, other techniques must be used such as memory frequency scaling [29], offlining memory pages [30], reducing refresh rates [31], and the use of heterogeneous memory [32,33]. Mixed workloads are more of a challenge because they may benefit from DVFS periodically as the program changes execution regions.…”

Section: Dynamic Voltage and Frequency Scalingmentioning

confidence: 99%

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

Giardino

Schwyn

Ferri

et al. 2022

JLPEA

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With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiveness of a reinforcement-learning based dynamic power manager placed in a software framework. This combination of Q-learning for determining policy and the software abstractions provide many of the benefits of co-design, namely, good performance, responsiveness and application guidance, with the flexibility of easily changing policies or platforms. The Q-learning based Quality of Service Manager (2QoSM) is implemented on an autonomous robot built on a complex, powerful embedded single-board computer (SBC) and a high-resolution path-planning algorithm. We find that the 2QoSM reduces power consumption up to 42% compared to the Linux on-demand governor and 10.2% over a state-of-the-art situation aware governor. Moreover, the performance as measured by path error is improved by up to 6.1%, all while saving power.

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Section: Dynamic Voltage and Frequency Scalingmentioning

confidence: 99%

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

Giardino

Schwyn

Ferri

et al. 2022

JLPEA

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“…Cache-centric optimizations (either cacheconscious [32] or cache-oblivious [33], [34]) reduce memory access and create more opportunities for energy saving. Besides optimizations targeting at general DRAM systems, some researchers have also proposed energy saving techniques for specific applications such as databases [8], [35] and video processing [35].…”

Section: Related Workmentioning

confidence: 99%

Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management

et al. 2016

IEEE Trans. Comput.

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Modern DRAM architectures allow a number of low-power states on individual memory ranks for advanced power management. Many previous studies have taken advantage of demotions on low-power states for energy saving. However, most of the demotion schemes are statically performed on a limited number of pre-selected low-power states, and are suboptimal for different workloads and memory architectures. Even worse, the idle periods are often too short for effective power state transitions, especially for memory intensive applications. Wrong decisions on power state transition incur significant energy and delay penalties. In this paper, we propose a novel memory system design named RAMZzz with rank-aware energy saving optimizations including dynamic page migrations and adaptive demotions. Specifically, we group the pages with similar access locality into the same rank with dynamic page migrations. Ranks have their hotness: hot ranks are kept busy for high utilization and cold ranks can have more lengthy idle periods for power state transitions. We further develop adaptive state demotions by considering all low-power states for each rank and a prediction model to estimate the power-down timeout among states. We experimentally compare our algorithm with other energy saving policies with cycle-accurate simulation. Experiments with benchmark workloads show that RAMZzz achieves significant improvement on energy-delay 2 and energy consumption over other energy saving techniques.

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“…While the power consumption of processors has not been largely increased with decreasing CPU frequency, that of DRAM continues to grow. Since it is not rare that DRAM consumes higher power than processors on modern servers [2], a reduction in the energy consumption of DRAM is a critical challenge to reduce that of an entire system.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Energy-Efficiency of DRAM Channel Interleaving Schemes for Multithreaded Programs

Imamura

Yasui

Inoue

et al. 2018

IEICE Trans. Inf. & Syst.

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The power consumption of server platforms has been increasing as the amount of hardware resources equipped on them is increased. Especially, the capacity of DRAM continues to grow, and it is not rare that DRAM consumes higher power than processors on modern servers. Therefore, a reduction in the DRAM energy consumption is a critical challenge to reduce the system-level energy consumption. Although it is well known that improving row buffer locality (RBL) and bank-level parallelism (BLP) is effective to reduce the DRAM energy consumption, our preliminary evaluation on a real server demonstrates that RBL is generally low across 15 multithreaded benchmarks. In this paper, we investigate the memory access patterns of these benchmarks using a simulator and observe that cache line-grained channel interleaving schemes, which are widely applied to modern servers including multiple memory channels, hurt the RBL each of the benchmarks potentially possesses. In order to address this problem, we focus on a row-grained channel interleaving scheme and compare it with three cache line-grained schemes. Our evaluation shows that it reduces the DRAM energy consumption by 16.7%, 12.3%, and 5.5% on average (up to 34.7%, 28.2%, and 12.0%) compared to the other schemes, respectively.

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Memory energy management for an enterprise decision support system

Cited by 10 publications

References 7 publications

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management

Evaluating Energy-Efficiency of DRAM Channel Interleaving Schemes for Multithreaded Programs

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