Workload-aware Power Optimization Strategy for Asymmetric Multiprocessors

Sozzo, Emanuele Del; Durelli, Gianluca; Trainiti, Ettore M. G.; Miele, Antonio; Santambrogio, Marco D.; Bolchini, Cristiana

doi:10.3850/9783981537079_0253

Cited by 16 publications

(26 citation statements)

References 8 publications

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“…However, DVFS is not exploited in [25] and [13], which can help to achieve further energy savings. In contrast, the approaches of [6], [12], [26] exploit DVFS, but they have several drawbacks. For example, in [26], design space is explored for a single application, which increases exponentially if concurrent applications have to be considered.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the approaches of [6], [12], [26] exploit DVFS, but they have several drawbacks. For example, in [26], design space is explored for a single application, which increases exponentially if concurrent applications have to be considered. In [6], each application is executed as single threaded and use only one type of core for it at a time.…”

Section: Related Workmentioning

confidence: 99%

“…Further, they are agnostic of concurrent workload variations and thus fail to adapt for concurrently executing multiple applications. Recently, there has been focus on online optimization facilitated by offline analysis results [6], [10]- [13], [25], [26]. Such approaches lead to better performance results than only online optimizations as they take advantage from both offline and online computations.…”

Section: Related Workmentioning

confidence: 99%

“…For heterogeneous multi-cores, recently some works have been reported that consider multi-threaded applications [6], [12], [13], [25], [26]. However, most of these approaches map application threads completely on one type of core situated within a cluster [25] [13] [26] [6].…”

Section: Related Workmentioning

confidence: 99%

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Inter-Cluster Thread-to-Core Mapping and DVFS on Heterogeneous Multi-Cores

Basireddy

Singh

Biswas

et al. 2018

IEEE Trans. Multi-Scale Comp. Syst.

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Abstract-Heterogeneous multi-core platforms that contain different types of cores, organized as clusters, are emerging, e.g. ARM's big.LITTLE architecture. These platforms often need to deal with multiple applications, having different performance requirements, executing concurrently. This leads to generation of varying and mixed workloads (e.g. compute and memory intensive) due to resource sharing. Run-time management is required for adapting to such performance requirements and workload variabilities and to achieve energy efficiency. Moreover, the management becomes challenging when the applications are multi-threaded and the heterogeneity needs to be exploited. The existing run-time management approaches do not efficiently exploit cores situated in different clusters simultaneously (referred to as inter-cluster exploitation) and DVFS potential of cores, which is the aim of this paper. Such exploitation might help to satisfy the performance requirement while achieving energy savings at the same time. Therefore, in this paper, we propose a run-time management approach that first selects thread-to-core mapping based on the performance requirements and resource availability. Then, it applies online adaptation by adjusting the voltage-frequency (V-f) levels to achieve energy optimization, without trading-off application performance. For thread-to-core mapping, offline profiled results are used, which contain performance and energy characteristics of applications when executed on the heterogeneous platform by using different types of cores in various possible combinations. For an application, thread-to-core mapping process defines the number of used cores and their type, which are situated in different clusters. The online adaptation process classifies the inherent workload characteristics of concurrently executing applications, incurring a lower overhead than existing learning-based approaches as demonstrated in this paper. The classification of workload is performed using the metric Memory Reads Per Instruction (MRPI). The adaptation process pro-actively selects an appropriate V-f pair for a predicted workload. Subsequently, it monitors the workload prediction error and performance loss, quantified by instructions per second (IPS), and adjusts the chosen V-f to compensate. We validate the proposed run-time management approach on a hardware platform, the Odroid-XU3, with various combinations of multi-threaded applications from PARSEC and SPLASH benchmarks. Results show an average improvement in energy efficiency up to 33% compared to existing approaches while meeting the performance requirements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Inter-Cluster Thread-to-Core Mapping and DVFS on Heterogeneous Multi-Cores

Basireddy

Singh

Biswas

et al. 2018

IEEE Trans. Multi-Scale Comp. Syst.

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“…Mapping of multi-threaded applications on single-ISA heterogeneous MPSoCs has been a hot topic [4,5,8,10,18,25,26]. Most of these approaches consider Samsung Exynos 5422 SoC and utilize 4 big and/or 4 LITTLE cores that have the same ISA [4,8,25].…”

Section: State-of-the-artmentioning

confidence: 99%

Reliable mapping and partitioning of performance-constrained openCL applications on CPU-GPU MPSoCs

Wächter

Merrett

Al-Hashimi

et al. 2017

Proceedings of the 15th IEEE/ACM Symposium on Embedded Systems for Real-Time Multimedia

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Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. Existing approaches exploit applications executing in CPU and GPU cores at the same time taking into account performance and energy consumption for mapping and partitioning. This paper presents a proposal for mapping and partitioning of applications in CPU-GPU MPSoCs taking into account the temperature behavior of the system. We evaluate the temperature profiling to partition the applications between CPU and GPU. The profiling is done by measuring the temperature of the CPU and GPU cores while executing different applications at different partitions. Results shown up to 13% savings of average temperature of the chip while maintaining performance requirements. A lower thermal behavior represents a better long-term reliability (lifetime) of the SoC.

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Proactive Scenario Characteristic-Aware Online Power Management on Mobile Systems

et al. 2020

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Modern mobile systems are requested to execute diverse user scenarios. Depending on the types of user scenarios, mobile systems utilize hardware resources differently. Thus, power management policies of mobile systems must adapt to various user scenarios. In this paper, we propose a dynamic voltage/frequency scaling (DVFS) policy to increase the energy efficiency of multicore mobile systems by adapting to user scenarios. The proposed policy provides effective power management regardless of user scenarios by using operation characteristics that can represent the execution behavior of various user scenarios. Furthermore, the proposed policy is suitable for modern mobile systems in which online power management is essential, because it does not require preliminary knowledge of target scenarios. To balance the trade-off between energy consumption and quality-of-service (QoS), the proposed scenarioaware policy provides 'just enough' processing speed to process the requested amount of work at the given parallelism level. To demonstrate the practicality of the proposed policy, we evaluated the effectiveness of the proposed scenario-aware policy for real-world user scenarios. Compared to the conventional DVFS policies, the proposed scenario-aware policy achieved a maximum of 25.5 % energy saving on the mobile system that uses asymmetric multicore CPU, and a maximum of 30.7 % energy saving on the mobile system that uses symmetric multicore CPU, without any QoS violation that degrades user experiences. INDEX TERMS Dynamic voltage/frequency scaling (DVFS), energy efficiency, quality-of-service (QoS), workload-aware power management, application-aware power management.

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Workload-aware Power Optimization Strategy for Asymmetric Multiprocessors

Cited by 16 publications

References 8 publications

Inter-Cluster Thread-to-Core Mapping and DVFS on Heterogeneous Multi-Cores

Inter-Cluster Thread-to-Core Mapping and DVFS on Heterogeneous Multi-Cores

Reliable mapping and partitioning of performance-constrained openCL applications on CPU-GPU MPSoCs

Proactive Scenario Characteristic-Aware Online Power Management on Mobile Systems

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