SaC: Exploiting execution-time slack to save energy in heterogeneous multicore systems
Paper in proceeding, 2019

Reducing the energy to carry out computational tasks is key to almost any computing application. We focus in this paper on iterative applications that have explicit computational deadlines per iteration. Our objective is to meet the computational deadlines while minimizing energy. We leverage the vast configuration space offered by heterogeneous multicore platforms which typically expose three dimensions for energy saving configurability: Voltage/frequency levels, thread count and core type (e.g. ARM big/LITTLE). We note that when choosing the most energy-efficient configuration that meets the computational deadline, an iteration will typically finish before the deadline and execution-time slack will build up across iterations. Our proposed slack management policy - SaC (Slack as a Currency) - proactively explores the configuration space to select configurations that can save substantial amounts of energy. To avoid the overheads of an exhaustive search of the configuration space, our proposal also comprises a low-overhead, on-line method by which one can assess each point in the configuration space by linearly interpolating between the endpoints in each configuration-space dimension. Overall, we show that our proposed slack management policy and linear-interpolation configuration assessment method can yield 62% energy savings on top of race-to-idle without missing any deadlines.

Soft Real-Time Systems

Energy Efficiency

Run-Time Systems

Quality of Service

Heterogeneous Multicore Systems

Resource Management


Muhammad Waqar Azhar

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Miquel Pericas

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Per Stenström

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

ACM International Conference Proceeding Series

978-145036295-5 (ISBN)

48th International Conference on Parallel Processing, ICPP 2019
Kyoto, Japan,

Subject Categories

Computational Mathematics

Energy Systems

Computer Systems



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