Authors: Tong Li, Dan Baumberger, David A. Koufaty, and Scott Hahn [Systems Technology Lab, Intel...
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- Slide 1
- Authors: Tong Li, Dan Baumberger, David A. Koufaty, and Scott
Hahn [Systems Technology Lab, Intel Corporation] Source: 2007
ACM/IEEE conference on Supercomputing Efficient Operating System
Scheduling for Performance- Asymmetric Multi-core Architecture
- Slide 2
- Outline Introduction Scheduling For Performance-Asymmetric
Architecture Evaluation Conclusion 2
- Slide 3
- Introduction 3 Over the next decade, we expect to see
processors with tens and even hundreds of cores on a chip. Resent
research advocates the performance- asymmetric multi-core
architectures. core1 core3 core4 core2 same instruction set
different performance characteristics Deliver higher performance at
lower cost
- Slide 4
- Introduction (cont.) 4 OS schedulers traditionally assume
homogeneous hardware and do not directly work well on asymmetric
architectures. This paper presents AMPS (Asymmetric Multi-
Processor Scheduler) that efficiently supports both SMP- and
NUMA-style performance-asymmetric architectures.
- Slide 5
- Outline Introduction Scheduling For Performance-Asymmetric
Architecture Evaluation Conclusion 5
- Slide 6
- Scheduling For Performance- Asymmetric Architecture 6 Run-queue
models: Distributed run-queue model Centralized run-queue model
Scheduling policies: Thread-dependent policies Thread-independent
policies
- Slide 7
- Scheduling For Performance- Asymmetric Architecture (cont.) 7
Optimization metrics: Performance Fairness Repeatability Three
components of AMPS: 1. Asymmetric-aware load balancing 2.
Faster-core-first scheduling 3. NUMA-aware migration
- Slide 8
- Asymmetric-Aware Load Balancing 8 AMPS approximate core
computing power using core frequencies. Quantifying core computing
power Define a cores scaled computing power as P. Let the core with
lowest frequency have P=1. Let the core have F times higher
frequency have P=FS, where S is a scaling factor and S