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Online concurrent workload classification for multi-core energy management

Online concurrent workload classification for multi-core energy management
Online concurrent workload classification for multi-core energy management
Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (V-f ). Such processors often need to execute applications concurrently, exhibiting varying and mixed workloads (e.g. compute- and memory-intensive) depending on the instruction mix and resource sharing. Runtime adaptation is key to achieving energy savings without trading-off application performance with such workload variabilities. In this paper, we propose an online energy management technique that performs concurrent workload classification using the metric Memory Reads Per Instruction (MRPI) and pro-actively selects an appropriate V-f setting through workload prediction. Subsequently, it monitors the workload prediction error and performance loss, quantified by Instructions Per Second (IPS) at runtime and adjusts the chosen V-f to compensate. We validate the proposed technique on an Odroid-XU3 with various combinations of benchmark applications. Results show an improvement in energy efficiency of up to 69% compared to existing approaches.
621-624
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Singh, Amit
bb67d43e-34d9-4b58-9295-8b5458270408
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Singh, Amit
bb67d43e-34d9-4b58-9295-8b5458270408
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Basireddy, Karunakar Reddy, Singh, Amit, Merrett, Geoff and Al-Hashimi, Bashir (2018) Online concurrent workload classification for multi-core energy management. IEEE Design, Automation & Test in Europe, Dresden, Germany. 19 - 23 Mar 2018. 4 pp, pp. 621-624.

Record type: Conference or Workshop Item (Paper)

Abstract

Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (V-f ). Such processors often need to execute applications concurrently, exhibiting varying and mixed workloads (e.g. compute- and memory-intensive) depending on the instruction mix and resource sharing. Runtime adaptation is key to achieving energy savings without trading-off application performance with such workload variabilities. In this paper, we propose an online energy management technique that performs concurrent workload classification using the metric Memory Reads Per Instruction (MRPI) and pro-actively selects an appropriate V-f setting through workload prediction. Subsequently, it monitors the workload prediction error and performance loss, quantified by Instructions Per Second (IPS) at runtime and adjusts the chosen V-f to compensate. We validate the proposed technique on an Odroid-XU3 with various combinations of benchmark applications. Results show an improvement in energy efficiency of up to 69% compared to existing approaches.

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Published date: 23 March 2018
Venue - Dates: IEEE Design, Automation & Test in Europe, Dresden, Germany, 2018-03-19 - 2018-03-23

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Local EPrints ID: 415727
URI: https://eprints.soton.ac.uk/id/eprint/415727
PURE UUID: 3b0271f9-9374-4e5d-a1f1-b61660d4f840
ORCID for Karunakar Reddy Basireddy: ORCID iD orcid.org/0000-0001-9755-1041
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 21 Nov 2017 17:30
Last modified: 06 Jun 2018 12:42

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Contributors

Author: Karunakar Reddy Basireddy ORCID iD
Author: Amit Singh
Author: Geoff Merrett ORCID iD

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