| 1. |
- Nejat, Mehrzad, 1989, et al.
(författare)
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Cooperative Slack Management: Saving Energy of Multicore Processors by Trading Performance Slack between QoS-Constrained Applications
- 2022
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Ingår i: Transactions on Architecture and Code Optimization. - : Association for Computing Machinery (ACM). - 1544-3973 .- 1544-3566. ; 19:2
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Tidskriftsartikel (refereegranskat)abstract
- Processor resources can be adapted at runtime according to the dynamic behavior of applications to reduce the energy consumption of multicore processors without affecting the Quality-of-Service (QoS). To achieve this, an online resource management scheme is needed to control processor configurations such as cache partitioning, dynamic voltage-frequency scaling, and dynamic adaptation of core resources.Prior State-of-the-art has shown the potential for reducing energy without any performance degradation by coordinating the control of different resources. However, in this article, we show that by allowing short-term variations in processing speed (e.g., instructions per second rate), in a controlled fashion, we can enable substantial improvements in energy savings while maintaining QoS. We keep track of such variations in the form of performance slack. Slack can be generated, at some energy cost, by processing faster than the performance target. On the other hand, it can be utilized to save energy by allowing a temporary relaxation in the performance target. Based on this insight, we present Cooperative Slack Management (CSM). During runtime, CSM finds opportunities to generate slack at low energy cost by estimating the performance and energy for different resource configurations using analytical models. This slack is used later when it enables larger energy savings. CSM performs such trade-offs across multiple applications, which means that the slack collected for one application can be used to reduce the energy consumption of another. This cooperative approach significantly increases the opportunities to reduce system energy compared with independent slack management for each application. For example, we show that CSM can potentially save up to 41% of system energy (on average, 25%) in a scenario in which both prior art and an extended version with local slack management for each core are ineffective.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- Nejat, Mehrzad, 1989
(författare)
-
Dynamic Management of Multi-Core Processor Resources to Improve Energy Efficiency under Quality-of-Service Constraints
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the current technology trends, the number of computers and computation demand is increasing dramatically. In addition to different economic and environmental costs at a large scale, the operational time of battery-powered devices is dependent on how efficiently the computer processors consume energy. Computer processors generally consist of several processing cores and a hierarchy of cache memory that includes both private and shared cache capacity among the cores. A resource management algorithm can adjust the configuration of different core and cache resources at regular intervals during run-time, according to the dynamic characteristics of the workload. A typical resource management policy is to maximize performance, in terms of processing speed or throughput, without exceeding the power and thermal limits. However, this can lead to excessive energy expenditure since a higher performance does not necessarily increase the value of the outcome. For example, increasing the frame-rate of multi-media applications beyond a certain target will not improve user experience considerably. Therefore, applications should be associated with Quality-of-Service (QoS) targets. This way, the resource manager can search for configurations with minimum energy that does not violate the performance constraints of any application. To achieve this goal, we propose several resource management schemes as well as hardware and software techniques for performance and energy modeling, in three papers that constitute this thesis. In the first paper, we demonstrate that, in many cases, independent management of resources such as per-core dynamic voltage-frequency scaling (DVFS) and cache partitioning fails to save a considerable energy without causing any performance degradation. Therefore, we present a coordinated resource management algorithm that saves considerable energy by exploring different combinations of resource allocations to all applications, at regular intervals during run-time. This scheme is based on simplified analytical performance and energy models and a multi-level reduction technique for reducing the dimensions of the multi-core configuration space. In the second paper, we extend the coordinated resource management with dynamic adaptation of the core micro-architectural resources. This way, we include instruction- and memory-level parallelism, ILP and MLP, resp., in the resource trade-offs together with per-core DVFS and cache partitioning. This provides a powerful means to further improve energy savings. Additionally, to enable this scheme, we propose a hardware technique that improves the accuracy of performance and energy prediction for different core sizes and cache partitionings. Finally, in the third paper, we demonstrate that substantial improvements in energy savings are possible by allowing short-term deviations from the baseline performance target. We measure these deviations by introducing a parameter called slack. Based on this, we present Cooperative Slack Management (CSM) that finds opportunities to generate slack at low energy cost and utilize it later to save more energy in the same or even other processor cores. This way, we also ensure that the performance consistently remains ahead of the baseline target in every core.
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| 5. |
- Nejat, Mehrzad, 1989, et al.
(författare)
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QoS-driven coordinated management of resources to save energy in multi-core systems
- 2019
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Ingår i: Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019. ; , s. 303-313
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Konferensbidrag (refereegranskat)abstract
- © 2019 IEEE Applications that are run on multicore systems without performance targets can waste significant 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 individual applications in multiprogrammed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and DVFS settings at runtime at negligible overhead. Compared to DVFS and cache partitioning alone, we show that our proposed coordinated RMA is capable of saving, on average, 20% energy as compared to 15% for DVFS alone and 7% for cache partitioning alone, when the performance target is set to 70% of the baseline system performance.
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| 6. |
- Nejat, Mehrzad, 1989
(författare)
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QoS Driven Coordinated Management of Resources to Save Energy in Multi-Core Systems
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Reducing the energy consumption of computing systems is a necessary endeavor. However, saving energy should not come at the expense of degrading user experience. To this end, in this thesis, we assume that applications running on multi-core processors are associated with a quality-of-service (QoS) target in terms of performance constraints. This way, hardware resources can be throttled to minimize energy expenditure without violating the QoS requirements. Typical resource management schemes control different resources such as processor cores and on-chip cache memory independently. These approaches are not effective under performance constraints for all applications. Therefore, this thesis presents multi-core resource management schemes that coordinately control several resources in a unified algorithm. This way, the resource manger can find trade-offs between resource allocations to different applications to reduce system-level energy consumption, while still meeting the QoS targets expressed as performance constraints for every application. Implementing a coordinated resource management scheme that dynamically adapts to varying run time behavior of a multi-programmed workload without any prior knowledge about the applications is a challenging task. Two different schemes are presented in this thesis to address this challenge. Both schemes are invoked at regular intervals during program execution. They employ simple and, yet, sufficiently accurate analytical models and a novel hardware technique to predict the effect of different resource allocations on performance and energy for each application. Using a heuristic method, the multi-dimensional system configuration space is pruned in several levels to find the optimum resource settings, with respect to energy efficiency, in a negligible time. In the first scheme a resource management algorithm is presented that coordinates the control of voltage-frequency (VF) of each processor core with partitioning of the on-chip cache space. In the second scheme, a re-configurable processor is considered in which sections of the core micro-architectural resources can be dynamically deactivated to save energy. The resource manager can reactivate these sections, at the proper time, to increase instruction and memory level parallelism (ILP/MLP). This introduces new trade-offs between processor core size, VF settings, and the allocation of cache space for each application. By exploiting these trade-offs, the second scheme improves the energy savings compared to the first scheme considerably. The proposed schemes are evaluated using a novel simulation framework. This framework estimates the effect of different resource management algorithms on full execution of benchmark applications in a multi-programmed workload. According to the experimental results, the proposed schemes can save up to 18% of system energy while respecting the performance constraints of all applications. The average energy savings are 6% and 10% with the first and second schemes, respectively. Further experiments on the first scheme shows that energy savings can potentially improve up to 29% if the users can tolerate a bounded reduction in performance that leads to 40% longer execution time.
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