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Shared resource interference is observed by applications as dynamicperformance asymmetry. Prior art has developed approaches to reduce the impactof performance asymmetry mainly at the operating system and architecturallevels. In this work, we study how application-level scheduling techniques canleverage moldability (i.e. flexibility to work as either single-threaded ormultithreaded task) and explicit knowledge on task criticality to handlescenarios in which system performance is not only unknown but also changingover time. Our proposed task scheduler dynamically learns the performancecharacteristics of the underlying platform and uses this knowledge to devisebetter schedules aware of dynamic performance asymmetry, hence reducing theimpact of interference. Our evaluation shows that both criticality-awarescheduling and parallelism tuning are effective schemes to address interferencein both shared and distributed memory applications

Scheduling Task-parallel Applications in Dynamically Asymmetric Environments