Dynamic Energy-Centric List Scheduling for Heterogeneous Compute Nodes

1. reMinMin: A Novel Static Energy-Centric List Scheduling Approach Based on Real Measurements Achim Lösch and Marco Platzner {achim.loesch, platzner}@upb.de

2. Heterogeneous Compute Node Contribution: Novel energy-optimizing list scheduling approach for single heterogeneous compute nodes based on real measurements

3. Energy Scheduling • Related Work: • Energy-minimizing list schedulers, e.g., Energy-Aware MinMin [1] and Minimum Energy-Minimum Energy [2] • Do not consider energy consumed by idling resources • MINMIN [3] adjusts estimated energy consumption between a min and max value, depending on the number of cores allocated to a task • Considers energy consumed by idling resources but energy model not applicable to non-CPU architectures • Our approach: • Considers both, dynamic and static energy consumption • Energy model more precise than in related work • Feasible for CPUs and accelerators with power sensors • Considers that tasks executed on accelerators induce energy consumption on host • Energy data measured on real system instead of estimations

4. Energy Model – Determining Idle Power R = { rCPU, rGPU, rFPGA} P Pidle(R) Etotal(R|τSLEEP,rCPU) rFPGA ① t P rGPU t P Heterogeneous Compute Node Pidle(rCPU) ≈ 16.9 W Pidle(rGPU) ≈ 26.9 W Pidle(rFPGA) ≈ 23.8 W Pidle(R) ≈67.7 W rCPU t T(τSLEEP,rCPU)

5. Energy Model – Task-induced Energy R = { rCPU, rGPU, rFPGA} P Pidle(R) rFPGA ① t P Etask(R|τi,rGPU) = Etotal(R|τi,rGPU) – Eidle(R|τi,rGPU) Etotal(R|τi,rGPU) rGPU ③ t P Eidle(R|τi,rGPU) = T(τi,rGPU) ·Pidle(R) rCPU ② t T(τi,rGPU)

6. Energy Model – Total Energy R = { rCPU, rGPU, rFPGA} P rFPGA t P Etotal(R|τi,rGPU) = Etask(R|τi,rGPU) + T(τi,rGPU) ·Pidle(R) rGPU t ③ ② ① P Measure N tasks @ M resources (N ∙M task-resource pairs) Scheduler input: ①  Pidle(R) ②  ETC[N][M] Expected Time for Completion ③  Etask[N][M] rCPU t T(τi,rGPU)

7. reMinMin Approach repeat: for each task-resource pair: • Calculate completion time () • Update system’s static energy consumption () • Calculate system’s total energy consumption () end • Assign task to resource with overall minimum • Remove assigned task from task set until all tasks are assigned

8. Example ② ① ③ P [W] 50 40 30 20 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18 P [W] 50 40 30 20 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18

9. Example P [W] 1) 50 40 30 20 2) 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18 P [W] 50 3) 40 30 20 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18

10. Example P [W] 1) 50 40 30 20 2) 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18 P [W] 50 3) 40 30 20 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18

11. Example P [W] 50 40 30 20 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18 P [W] 50 40 30 20 10 t [s] 0 4 10 2 8 0 6 12 22 20 16 14 24 18  Considering idle energy is key to optimize total energy consumption.

12. Paper/Poster Outline • Present reMinMin in more detail • Experiments • Comparison to 2 scheduling approaches • Minimum of • Optimum scheduler (exhaustive search) • 3 task sets • Homogeneous task set • Heterogeneous task set • Mixed task set • Task sets consist of 16 tasks • Instances of 4 applications • Results from experiments • reMinMin outperforms Minimum of approach • reMinMin even close to Optimum scheduler

13. Thank you for your attention! References: [1] Y. Li, Y. Liu, and D. Qian, “A heuristic energy-aware scheduling algorithm for heterogeneous clusters,” in 2009 15th International Conference on Parallel and Distributed Systems, Dec 2009 [2] J. K. Kim, H.J. Siegel, A. A. Maciejewski, and R. Eigenmann, “Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling,” IEEETransactions on Parallel and Distributed Systems, vol. 19, no. 11, Nov 2008 [3] S. Nesmachnow, B. Dorronsoro, J. E. Pecero, and P. Bouvry, “Energy-aware scheduling on multi- core heterogeneous grid computing systems,” Journal of Grid Computing, vol. 11, no. 4, 2013