Active Learning in Performance Analysis

Dmitry Duplyakin

Jed Brown

Robert Ricci

09/14/2016

 

dmitry.duplyakin@colorado.edu

Outline

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

  • Motivation
  • Approach
  • Implementation
  • Datasets and Visualizations
  • Evaluation
  • Summary and Future Work

Motivation

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Performance Analysis:

  • Take a set of measurements
  • Build a model
  • Understand behavior
    of a complex system
  • Predict outcomes of future experiments 

Main Challenges:

  • Often too many factors
  • Inability to take equal number of measurements at every configuration
  • Inefficient exploration of input space

Motivation: Example 1

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Each point represents a run of HPGMG-FE benchmark on a 4-node cluster provisioned on CloudLab testbed

Motivation: Example 2

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Each point represents a run of HPGMG-FE benchmark on a 4-node cluster provisioned on CloudLab testbed

Approach: Active Learning

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

  • Use Active Learning (AL) -- techniques from Machine Learning
    where "learner" interacts with "data source"
    1. Train a model on a small set of measurements
    2. Let the model suggest a point for the next experiment  
    3. Run the suggested experiment
    4. Retrain the model with the new measurement
    5. Go back to 2 or exit
  • Sometimes called: adaptive experiment design
    and optimal experiment design

Approach: Gaussian Process Regression

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

  • Use Gaussian Process Regression (GPR) -- non-parametric
    non-linear interpolation technique that provides best linear unbiased prediction (under suitable assumptions)
    • Build a model for 
    • For every new       ,
      calculate estimates of  
                         and
  • Sometimes called: kriging
    (in geostatistics) and
    Wiener–Kolmogorov prediction
  • GPR works in many dimensions

Approach: Putting it Together

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

  • Combine AL and GPR into a 2-layer system:

 

 

  • Optimization problem at each layer:

   Upper: AL

   Lower: GPR

   Upper: Choose "best" experiment

   Lower: Choose "best" hyperparameters

Approach: Details

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

   Upper: Choose "best" experiment

   Lower: Choose "best" hyperparameters

Consider strategies:
Variance Reduction (VR):
Cost Efficiency (CE):

Use: Bayesian Model Selection
(Marginal Likelihood Maximization)
with 3 hyperparameters:
noise level, length scale, and amplitude

Implementation

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

  • Developed a prototype in Python which supports:
    • single realizations of AL in "offline" mode*
    • batches of realizations for comparison of Variance Reduction and Cost Efficiency strategies 
  • GPR: used code for Gaussian Processes in scikit-learn (0.18.dev0)

* Note: offline refers to the fact that the prototype queries a database with collected data. Future work: in online mode, run AL alongside the computation

Analyzed Datasets

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

  • Measured runtimes and estimated energy consumption
    for a large set of HPGMG-FE benchmark runs
    on a cluster provisioned on the CloudLab testbed
  • Organized this data into two datasets:
  • 3d visualizations are available here

Active Learning: 10 Iterations

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Shown points represent a subset of measurements in the Performance dataset; runtimes are log-transformed

Active Learning: 100 Iterations

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Shown points represent a subset of measurements in the Performance dataset; runtimes are log-transformed

Evaluation: Convergence Analysis

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Evaluation: Cost Analysis

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Summary and Future Work

Dmitry Duplyakin, University of Colorado

Active Learning in Performance Analysis

09/14/2016

Summary:

  • Proposed using Active Learning + Gaussian Process Regression
    for efficient regression learning in performance analysis
  • Demonstrated tradeoffs between two Active Learning algorithms, with and without adjustment for experiment cost

Future Work:

  • Investigate computational requirements
  • Leverage continuous optimization techniques
  • Run Active Learning in the online mode

Dmitry Duplyakin, University of Colorado

Thank you!

Questions?

 

dmitry.duplyakin@colorado.edu

Active Learning in Performance Analysis

09/14/2016

IEEE Cluster 2016 - Active Learning in Performance Analysis

By Dmitry Duplyakin

IEEE Cluster 2016 - Active Learning in Performance Analysis

Slide deck for presenting a paper titled "Active Learning in Performance Analysis" at IEEE Cluster 2016.

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