Joël Goossens Nathan Fisher

1. The Chilling Effect of Parallelism: Analysis and Allocation of Parallel Real-Time Jobs for Peak System-Temperature Minimization JoëlGoossens Nathan Fisher UniversitéLibre de Bruxelles Wayne State University

2. Challenge: Thermal Management Heatis a by-product of computation. Processors must operate within thermal thresholds: • Reliability • Safety • Cooling Costs Dynamic Voltage/Frequency Scaling (DVFS) utilized to ensure no thresholds violations.

3. Current Research Trend: Thermal-Aware Real-Time Systems Common Objective: Minimize peak system temperature platform using DVFS cores while guaranteeing real-time constraints. Our Setting: Multicore Architectures with DVFS

4. Our Setting: MulticoreThermal Management • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Core-to-Sink Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

5. Our Setting: MulticoreThermal Management • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Sink-to-Core Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

6. Our Setting: MulticoreThermal Management • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Sink-to-Sink Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

7. Our Setting: MulticoreThermal Management • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Core-to-Core Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

8. Our Setting: MulticoreThermal Management • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Sink-to-Environment Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2 Previous Work: Most prior work has focused on minimizing peak temperature in multicore processors for non-parallel real-time jobs. Open Question: Can parallel jobs help further minimize peak temperature?

9. Our Setting: Parallel Real-Time Jobs “Traditional” (Sequential) Recurring Task i = (ei,di,pi): • execution requirement. • relative deadline. • minimum inter-arrival separation (“period”). • Parallel Recurring Task i= (ei,Γi,di,pi) • execution requirement. • relative deadline. • minimum inter-arrival separation (“period”). • parallel speed-up vector Γi = (γi,1, γi,2, …,γi,m) Execution on ℓ processorsfor t time units: γi,ℓx t Each parallel execution is called a “thread”

10. Our Setting: Parallel Real-Time Jobs • Degree of Parallelism Models: • Rigid: degree determined a priori. • Moldable: chosen by scheduler at start of each job. • Malleable: may dynamically change over job execution. • Type of Parallelism Models: • Multi-Threaded: threads can execute concurrently. • Which includes Fork-Join task model. • Gang: threads must execute in unison.

11. Motivating Example • Consider two-core processor with one task: • i= (ei,Γi,di,pi) = (1,[1,2],1,1) • Assume that processor speed is fixed at design-time. Option 1 (No Parallelism): One processor must execute at speed one. Option 2 (Degree-2 Parallelism): Each processor can execute at half-speed. Observation: Option 1 has greater peak temperature than Option 2 (even if some overhead is added for parallelism). Parallelism helps by spreading out heat generation!

12. Open Problems Problem 1: Schedulability analysis for parallel jobs on platforms where cores run at different speeds. Problem 2: Online scheduling algorithms for thermal-aware parallel jobs. Problem 3: Core-speed assignment algorithms for DVFS-capable cores.

13. Open Problems ? ? ? ? ? ?

14. Thank You! Questions?