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- Nejat, Mehrzad, 1989, et al.
(författare)
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Coordinated management of DVFS and cache partitioning under QoS constraints to save energy in multi-core systems
- 2020
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier BV. - 1096-0848 .- 0743-7315. ; 144, s. 246-259
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Tidskriftsartikel (refereegranskat)abstract
- Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage–frequency settings to save energy while respecting QoS requirements of every application in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and Dynamic Voltage–Frequency Scaling (DVFS) settings at runtime at negligible overhead. We show that the energy of 4-core and 8-core systems can be reduced by up to 18% and 14%, respectively, compared to a baseline with even distribution of cache resources and a fixed mid-range core voltage–frequency setting. The energy savings can potentially reach 29% if the QoS targets are relaxed to 40% longer execution time.
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| 2. |
- Nejat, Mehrzad, 1989, et al.
(författare)
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Coordinated Management of Processor Configuration and Cache Partitioning to Optimize Energy under QoS Constraints
- 2020
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Ingår i: Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium, IPDPS 2020. ; , s. 590-601
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Konferensbidrag (refereegranskat)abstract
- An effective way to improve energy efficiency is to throttle hardware resources to meet a certain QoS target, specified as a performance constraint, associated with all applications running on a multicore system. Prior art has proposed resource management (RM) frameworks in which the share of the last-level cache (LLC) assigned to each processor core and the voltage-frequency (VF) setting for each core is managed in a coordinated fashion to reduce energy. A drawback of such a scheme is that, while one core gives up LLC resources for another core, the performance drop must be compensated by a higher VF setting which leads to a quadratic increase in energy consumption. By allowing each core to be adapted to exploit instruction and memory-level parallelism (ILP/MLP), substantially higher energy savings are enabled.This paper proposes a coordinated RM for LLC partitioning, processor adaptation, and per-core VF scaling. A first contribution is a systematic study of the resource trade-offs enabled when trading between the three classes of resources in a coordinated fashion. A second contribution is a new RM framework that utilizes these trade-offs to save more energy. Finally, a challenge to accurately model the impact of resource throttling on performance is to predict the amount of MLP with high accuracy. To this end, the paper contributes with a mechanism that estimates the effect of MLP over different processor configurations and LLC allocations. Overall, we show that up to 18% of energy, and on average 10%, can be saved using the proposed scheme.
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