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Migration Cost Aware Task Scheduling

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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