Commodity multi-cores are still uncommon in real-Time systems, as resource sharing complicates traditional timing analysis. The Predictable Execution Model (PREM) tackles this issue in software, through scheduling and code refactoring. State-of-The-Art PREM compilers analyze tasks one at a time, maximizing task-level performance metrics, and are oblivious to system-level scheduling effects (e.g. memory serialization when tasks are co-scheduled). We propose a solution that allows PREM code generation and system scheduling to interact, based on a genetic algorithm aimed at maximizing overall system performance. Experiments on commodity hardware show that the performance increase can be as high as 31% compared to standard PREM code generation, without negatively impacting the predictability guarantees.
A Synergistic Approach to Predictable Compilation and Scheduling on Commodity Multi-Cores / Forsberg, B.; Mattheeuws, M.; Kurth, A.; Marongiu, A.; Benini, L.. - (2020), pp. 108-118. (Intervento presentato al convegno 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems, LCTES 2020 tenutosi a gbr nel 2020) [10.1145/3372799.3394369].
A Synergistic Approach to Predictable Compilation and Scheduling on Commodity Multi-Cores
Marongiu A.;
2020
Abstract
Commodity multi-cores are still uncommon in real-Time systems, as resource sharing complicates traditional timing analysis. The Predictable Execution Model (PREM) tackles this issue in software, through scheduling and code refactoring. State-of-The-Art PREM compilers analyze tasks one at a time, maximizing task-level performance metrics, and are oblivious to system-level scheduling effects (e.g. memory serialization when tasks are co-scheduled). We propose a solution that allows PREM code generation and system scheduling to interact, based on a genetic algorithm aimed at maximizing overall system performance. Experiments on commodity hardware show that the performance increase can be as high as 31% compared to standard PREM code generation, without negatively impacting the predictability guarantees.File | Dimensione | Formato | |
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