Bandits meet Computer Architecture: Designing a Smartly-allocated Cache
This work addresses performance optimization in embedded systems with repetitive thread execution, though it appears incremental as it applies existing bandit frameworks to a specific domain.
The paper tackles the problem of online resource allocation in embedded systems, such as cache allocation to threads, by developing algorithms based on multi-armed bandits, and shows improved system performance through experiments with synthetic and real-world data.
In many embedded systems, such as imaging sys- tems, the system has a single designated purpose, and same threads are executed repeatedly. Profiling thread behavior, allows the system to allocate each thread its resources in a way that improves overall system performance. We study an online resource al- locationproblem,wherearesourcemanagersimulta- neously allocates resources (exploration), learns the impact on the different consumers (learning) and im- proves allocation towards optimal performance (ex- ploitation). We build on the rich framework of multi- armed bandits and present online and offline algo- rithms. Through extensive experiments with both synthetic data and real-world cache allocation to threads we show the merits and properties of our al- gorithms