Embed Size (px)
DESCRIPTION
The Chilling Effect of Parallelism: Analysis and Allocation of Parallel Real-Time Jobs for Peak System-Temperature Minimization. Joël Goossens Nathan Fisher Université Libre de Bruxelles Wayne State University. Challenge: Thermal Management. - PowerPoint PPT Presentation
Citation preview
The Chilling Effect of Parallelism: Analysis and Allocation of Parallel Real-Time Jobs for Peak System-
Temperature MinimizationJoël Goossens Nathan FisherUniversité Libre de Bruxelles Wayne State
University
Challenge: Thermal Management
Heat is 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.
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
o Thermal Challenges:4 Heat transfer between elements (e.g., Core-to-Sink).
Core 3 Core
4Sink 1Core
1Sink 2
Core 2
Core-to-Sink
Our Setting: Multicore Thermal Management
o Thermal Challenges:4 Heat transfer between elements (e.g., Core-to-Sink).
Our Setting: Multicore Thermal Management
Core 3 Core
4Sink 1Core
1Sink 2
Core 2
Sink-to-Core
o Thermal Challenges:4 Heat transfer between elements (e.g., Core-to-Sink).
Our Setting: Multicore Thermal Management
Core 3 Core
4Sink 1Core
1Sink 2
Core 2
Sink-to-Sink
o Thermal Challenges:4 Heat transfer between elements (e.g., Core-to-Sink).
Our Setting: Multicore Thermal Management
Core 3 Core
4Sink 1Core
1Sink 2
Core 2
Core-to-Core
o Thermal Challenges:4 Heat transfer between elements (e.g., Core-to-Sink).
Our Setting: Multicore Thermal Management
Core 3 Core
4Sink 1Core
1Sink 2
Core 2
Sink-to-Environment
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?
Our Setting: Parallel Real-Time Jobs
“Traditional” (Sequential) Recurring Task i = (ei,di,pi): 4execution requirement.4relative deadline.4minimum inter-arrival separation (“period”).
Parallel Recurring Task i = (ei,Γi,di,pi)4execution requirement.4relative deadline.4minimum inter-arrival separation (“period”).4parallel speed-up vector Γi = (γi,1, γi,2, …, γi,m)
Execution on ℓ processors for t time units: γi,ℓ x t
Each parallel execution is called a “thread”
Our Setting: Parallel Real-Time Jobs
Degree of Parallelism Models: 4Rigid: degree determined a priori.4Moldable: chosen by scheduler at start of each job.4Malleable: may dynamically change over job execution.
Type of Parallelism Models: 4Multi-Threaded: threads can execute concurrently.
4Which includes Fork-Join task model. 4Gang: threads must execute in unison.
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!
Open ProblemsProblem 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.
Open ProblemsMulti-Threaded Gang
Rigid
Moldable
Malleable
?
?
?
?
?
?
Thank You!
Questions?
