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Scalability-Based Manycore Partitioning. PACT 2012. Hiroshi Sasaki Kyushu University Koji Inoue Kyushu University. Teruo Tanimoto The University of Tokyo Hiroshi Nakamura The University of Tokyo. Presented by Kim, Jong- yul 2013. 7. 31. Contents. Motivation SBMP Scheduler

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scalability based manycore partitioning

Scalability-BasedManycore Partitioning

PACT 2012

Hiroshi Sasaki

Kyushu University

Koji Inoue

Kyushu University

TeruoTanimoto

The University of Tokyo

Hiroshi Nakamura

The University of Tokyo

Presented by Kim, Jong-yul

2013. 7. 31

contents
Contents
  • Motivation
  • SBMP Scheduler
    • Scalability Prediction
    • Core Partition
    • Core Donation
    • Phase Change Detection
  • Evaluation Results
  • Conclusions
prospects
Prospects

APP2

APP3

  • Limitation of increasing F
    • ILP, power wall, transistor scaling
  • Multi-core, many-core system

System

APP1

Multi-threaded multiprogramming

problem
Problem
  • Traditional OS Assign equal CPU to all running apps
  • Programs have different Scalability

Linux: 2.04

Best Partitioning: 1.38

Performance

Average

Workloads

Workloads

Average

Clock cycles when multiprogrammed with others

NormalizedTurnaroundTime

Clock cycles when solo-run

experimental system
Experimental System

allocation unit

sbmp scheduler
SBMP Scheduler

Scalability Prediction

Core Partitioning

Core Donation

Phase Change Detection

overview
Overview
  • Assign cores considering scalability of applications
  • SBMP: Scalability-Based ManycorePartitioning scheduler

Detect

Scalability Prediction

Core Partitioning

Core Donation

Steady

Partitioning

slide8

Detect

Scalability Prediction

Core Partitioning

Core Donation

Steady

scalability prediction 1 2
Scalability Prediction (1/2)
  • Cumulative retired instructions per second (IPS)

Little effect from # of cores

Total # of instructions

Workloads

8%

Total # of instructions

scalability prediction 2 2
Scalability Prediction (2/2)
  • If obtained directly…
    • Warm up branch prediction & cache system
    • Need 8 allocations (6, 12, 18, …, 48)
  • Simple model
  • 3 coefficients (α,β, γ)
    • 3 Samplings: 1 single core + 2 different configurations

Over 3 seconds

Performance

Amdahl’s law

Overhead caused by additional core

slide11

Detect

Scalability Prediction

Core Partitioning

Core Donation

Steady

core partitioning 1 2
Core Partitioning (1/2)

High

Relative

performance

Medium

# of cores

Relative

performance

Low

# of cores

core partitioning 2 2
Core Partitioning (2/2)
  • Scalability-tablefor each program
    • Key -value
      • Key : # of cores
      • Value : performance with [key] cores
  • Goal
  • Hill climbing algorithm Near optimal assignment

Multiprogrammed

Single-run

slide14

Detect

Scalability Prediction

Core Partitioning

Core Donation

Steady

core donation
Core Donation
  • 1 program for each processor die
    • CPU utilization

CPU utilization ratio < Threshold (70%)

Donor

Core1

Program1

Donee

Core2

Program2

Program2

time

  • Donee: most beneficial one
      • Utilization, scalability
  • Priority: Donee < Donor
  • Finer granularity
      • Processor die (6 cores)
slide16

Detect

Scalability Prediction

Core Partitioning

Core Donation

Steady

slide17

Detect

Scalability Prediction

Core Partitioning

Core Donation

Steady

detection 1 2
Detection (1/2)
  • Creation or termination of program
  • Phase transition detected in any of the programs

Performance

detection 2 2 phase prediction
Detection (2/2) – Phase Prediction
  • SBMP scheduler monitors performanceevery epoch (2.5s)
  • Threshold ( > or <

Scalability Prediction

Detect

Core Partitioning

Core Donation

Steady

evaluation
Evaluation

Core Partitioning

Phase Prediction

Core Donation

Overall Performance

experimental system1
Experimental System
  • PARSEC benchmark suite 2.1
core partitioning
Core Partitioning
  • SBMP-base
    • Scalability Prediction + Core Partitioning
  • Single-phase application (2 Medium + 2 Low)

Linux: 1.88

SBMP-base: 1.54

Performance

Average

Workloads

Workloads

phase prediction
Phase Prediction
  • SBMP-PP (Phase Prediction)
    • SBMP-base + Phase Prediction
  • Multiple-phase application

Linux: 1.89

SBMP-base: 2.09

SBMP-PP: 1.77

Workloads

core donation1
Core Donation
  • SBMP-CD (Core Donation)
    • SBMP-PP + Core Donation
  • 2 low CPU utilization + 2 normal

Linux: 2.06

SBMP-PP: 1.68

SBMP-CD: 1.60

Workloads

overall results
Overall Results

Linux: 1.83

SBMP-base: 1.99

SBMP-PP: 1.70 (8%)

SBMP-CD: 1.65 (11%)

  • All programs

72 Workloads

conclusions
Conclusions
  • OS scheduling on many core system
    • Multiple Multi-threaded applications
  • SBMP Scheduler
    • Dynamic scalability prediction + Core partitioning
    • Phase recognition
    • Core Donation
  • 11% over Linux
hill climbing algorithm
Hill Climbing Algorithm
  • Find near optimal solution
    • Start with arbitrary solution
    • Incrementally changing a single element