Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping
Modern embedded computing platforms consist of a high amount of heterogeneous resources, which allows executing multiple applications on a single device. The number of running application on the system varies with time and so does the amount of available resources. This has considerably increased th...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
22.01.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Modern embedded computing platforms consist of a high amount of heterogeneous
resources, which allows executing multiple applications on a single device. The
number of running application on the system varies with time and so does the
amount of available resources. This has considerably increased the complexity
of analysis and optimization algorithms for runtime mapping of firm real-time
applications. To reduce the runtime overhead, researchers have proposed to
pre-compute partial mappings at compile time and have the runtime efficiently
compute the final mapping. However, most existing solutions only compute a
fixed mapping for a given set of running applications, and the mapping is
defined for the entire duration of the workload execution. In this work we
allow applications to adapt to the amount of available resources by using
mapping segments. This way, applications may switch between different
configurations with varied degree of parallelism. We present a runtime manager
for firm real-time applications that generates such mapping segments based on
partial solutions and aims at minimizing the overall energy consumption without
deadline violations. The proposed algorithm outperforms the state-of-the-art
approaches on the overall energy consumption by up to 13% while incurring an
order of magnitude less scheduling overhead. |
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DOI: | 10.48550/arxiv.2001.08094 |