Runtime energy management of concurrent applications for multi-core platforms

Abstract

Multi-core platforms are employing a greater number of heterogeneous cores and resource configurations to achieve energy-efficiency and high performance. These platforms often execute applications with different performance constraints concurrently, which contend for resources simultaneously, thereby generating varying workload and resources demands over time. There is a little reported work on runtime energy management of concurrent execution, focusing mostly on homogeneous multi-cores and limited application scenarios. This thesis considers both homogeneous and heterogeneous multi-cores and broadens application scenarios. The following contributions are made in this thesis.

Firstly, this thesis presents online Dynamic Voltage and Frequency Scaling (DVFS) techniques for concurrent execution of single-threaded and multi-threaded applications on homogeneous multi-cores. This includes an experimental analysis and deriving metrics for efficient online workload classification. The DVFS level is proactively set through predicted workload, measured through Memory Reads Per Instruction. The analysis also considers thread synchronisation overheads, and underlying memory and DVFS architectures. Average energy savings of up to 60% are observed when evaluated on three different hardware platforms (Odroid-XU3, Intel Xeon E5-2630, and Xeon Phi 7620P).

Next, an energy efficient static mapping and DVFS approach is proposed for heterogeneous multi-core CPUs. This approach simultaneously exploits different types of cores for each application in a concurrent execution scenario. It first selects performance meeting mapping (no. of cores and type) for each application having minimum energy consumption using offline results. Then online DVFS is applied to adapt to workload and performance variations. Compared to recent techniques, the proposed approach has an average of 33% lower energy consumption when validated on the Odroid-XU3.

To eliminate dependency on the offline application profiling and to adapt to dynamic application arrival/completion, an adaptive mapping approach coupled with DVFS is presented. This is achieved through an accurate performance model, and an energy efficient resource selection technique and a resource manager. Experimental evaluation on the Odroid-XU3 shows an improvement of up to 28% in energy efficiency and 7.9% better prediction accuracy by performance models.

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Identifiers

ORCID for Karunakar Reddy Basireddy: orcid.org/0000-0001-9755-1041

ORCID for Geoff Merrett: orcid.org/0000-0003-4980-3894

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