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Migration Cost Aware Task Scheduling

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This research addresses the challenge of efficient task scheduling in heterogeneous computing systems by incorporating the costs associated with task migration. We explore dynamic schedulers that allocate compute-intensive tasks to high-performance cores and less intensive tasks to low-power cores while considering the overhead of migration, such as cache misses and congestion. Our methodology employs the Sniper Multi-Core Simulator to evaluate performance and power statistics, aiming to develop a dynamic task scheduling algorithm that factors in migration costs for optimized energy-aware computing.

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Migration Cost Aware Task Scheduling

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  1. Migration Cost AwareTask Scheduling 18-743 Energy Aware Computing ShraddhaJoshi, Brian Osbun 9/24/2013

  2. Outline • Motivation • Problem to be solved • Methodology • Proposed system configuration • Simulation environment • Cost and scheduling formulation • Milestones • Q&A

  3. Motivation • Heterogeneous systems can improve power/ performance efficiency by scheduling tasks on the most suitable cores • Compute-intensive tasks run on high-performance cores • Less intensive tasks run on low-power cores

  4. Motivation (cont.) • Dynamic schedulers allow this task mapping to be updated during program execution • Migrating threads to new cores can have hidden costs • Moving architectural state • Increase in cache misses • Congestion on interconnect during transfer

  5. Problem to be solved • Most task schedulers ignore migration overhead • Solution: quantify and consider the task migration cost when evaluating scheduling possibilities

  6. Methodology (configuration) • Cluster: a set of cores with different performance levels • Shared L1 cache per cluster • System: a set of multiple clusters • Shared L2 cache between all clusters • This creates a cost difference • Intra-cluster migration • Inter-cluster migration

  7. Methodology (simulator) • Use the Sniper Multi-Core Simulator • Interval based, x86 simulation • Supports heterogeneous configurations • Python interface for runtime control • Ability to schedule tasks among cores • Produces performance and power statistics • Integrated with McPAT framework

  8. Methodology (scheduling) • Determine the conditions for initiating a task migration • Current IPC can be a good predictor for the future IPC • Determine acceptable ranges of migration costs • Migration cost related to predicted number of memory intensive instructions • Choose whether migration provides a net benefit • Cluster architecture allows different tiers of migration

  9. Milestones • Be able to quantify migration cost in terms of cycles • Develop a dynamic task scheduling algorithm • Incorporate migration cost into our algorithm

  10. Q&A

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