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- 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
(författare)
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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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