Visualizing Distributed Memory Computations with Hive Plots. VizSec 2012, October 15, 2012, Seattle, Washington Sophie Engle and Sean Whalen
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1 Visualizing Distributed Memory Computations with Hive Plots Sophie Engle and Sean Whalen
2 2 Introduction
3 Introduction 3 High performance computing environments Used for scientific computing applications at several national laboratories Potential for misuse by insiders and outsiders Anomaly detection Determine normal versus abnormal behavior for these environments to prevent unauthorized use Can classify codes into computational dwarves to determine normal (Asanovic 2006)
4 Introduction 4 Several network measures can be used as features in classification Time consuming to calculate these measures Time consuming to compare how well these measures perform for classification Use visualization to choose network measures to use as classification features Which measures look similar for similar codes? Which measures look distinct for distinct codes?
5 5 Dataset
6 Original Dataset 6 Data collection Collected by NERSC at LBNL Used IPM to monitor MPI calls between ranks (captures communication between compute nodes) Dataset contents Total of 1681 IPM logs Covers 29 different scientific computing codes with varying ranks, parameters, and architectures
7 Original Dataset 7 Src,Dst,MPICall,Bytes,Repeats,Code 0,1,29,99856,52,cactus 0,4,29,99856,52,cactus 0,0,2,4,5,cactus 0,0,2,8,7,cactus 0,1,22,599136,26,cactus 0,-1,5,0,1,cactus 0,4,22,599136,26,cactus 0,16,29,99856,52,cactus
8 Original Dataset 8 Src,Dst,MPICall,Bytes,Repeats,Code 0,1,29,99856,52,cactus 0,4,29,99856,52,cactus 0,0,2,4,5,cactus 0,0,2,8,7,cactus 0,1,22,599136,26,cactus 0,-1,5,0,1,cactus 0,4,22,599136,26,cactus 0,16,29,99856,52,cactus
9 Subset Analyzed 9 Code Description Nodes Edges cactus astrophysics ij algebraic multi-grid milc lattice gauge theory namd molecular dynamics paratec materials science superlu sparse linear algebra tgyro magnetic fusion vasp materials science
10 Subset Analyzed 10 Code Description Nodes Edges cactus astrophysics ij algebraic multi-grid milc lattice gauge theory namd molecular dynamics paratec materials science superlu sparse linear algebra tgyro magnetic fusion vasp materials science
11 Network Measures 11 Measure degree betweenness closeness eccentricity page rank transitivity Description the number of adjacent edges number of shortest paths going through a node measures steps required to reach every other node shortest path distance from farthest node measures relative importance of node probability adjacent nodes are connected (clustering coefficient) Calculated in R using the igraph library.
12 12 Motivation
13 Traditional Degree CCDF Plot 13 cactus ij milc namd superlu tgyro
14 Traditional Degree CCDF Plot 14 Pros: Able to compare individual metrics across datasets Simple approach, widely used Cons: Contains no information on topology Lines look visually similar, may not be appropriate for generating visual signatures
15 Adjacency Matrices 15
16 Adjacency Matrices 16 Pros: Comparable across datasets Easy to see communication patterns Many distinct codes look distinct Cons: No information on metrics needed for classification
17 Issues Identified 17 Traditional CCDF plot does not convey any information about network topology Traditional adjacency matrices do not convey any information about network properties Traditional network layout algorithms are not repeatable or comparable across networks
18 18 Hive Plots
19 Introduction to Hive Plots 19 What are hive plots? Network layout algorithm using network properties for consistent node placement A radially-arranged parallel coordinate plot Why use hive plots? Repeatable, comparable network layouts Integration of network properties with topology
20 Understanding Hive Plots tgyro degree
21 Understanding Hive Plots 21 edges b/w nodes on same axis 32 primary axis duplicate axis edges b/w nodes on different axes node degree b/w 260 and 837 max axis value self loop tgyro degree degree ranges from 0 to 837 across datasets
22 Implementation 22 Existing implementations exist JHive (Java) HiveR (R) HiveGraph (webapp) Prototypes in Perl and d3.js Custom implementation in R and ggplot2 Implements grammar of graphics (Wilkinson) Polar plots to create hive plots Facets to create hive panels* Non-interactive
23 Implementation 23 evenly spaced nodes linear interpolation interpolation and jitter page rank (milc) page rank (cactus)
24 Implementation 24 consistent alpha 32 variable alpha 32 closeness (cactus) degree (superlu)
25 Hive Plot References 25 Hive Plots Rational Approach to Visualizing Networks by Martin Krzywinski, Inanc Birol, Steven JM Jones and Marco A Marra in Briefings in Bioinformatics, volume 13, issue 5, pages , 2012 Hive Plots: Rational Network Visualization Farewell to Hairballs by Martin Krzywinski at online Getting Into Visualization of Large Biological Data Sets by Martin Krzywinski, Inanc Birol, Steven Jones, Marco Marra in BioVis 2012 Posters, 2nd floor foyer, Sunday 8:30am Monday 5:55pm
26 26 Initial Results
27 Degree 27 cactus ij milc namd superlu tgyro
28 Betweenness 28 cactus ij milc namd superlu tgyro
29 Closeness 29 cactus ij milc namd superlu tgyro
30 Eccentricity 30 cactus ij milc namd superlu tgyro
31 Page Rank 31 cactus ij milc namd superlu tgyro
32 Transitivity 32 cactus ij milc namd superlu tgyro
33 Visually Distinct 33 cactus ij milc namd superlu tygro degree x x x x betweenness x x x x x x closeness x x x x eccentricity x x x page rank x x x x transitivity x x x x x x
34 Visually Distinct 34 cactus ij milc namd superlu tygro degree x x x x betweenness x x x x x x closeness x x x x eccentricity x x x page rank x x x x transitivity x x x x x x
35 35 Next Steps
36 Next Steps 36 Improve hive plot visualizations Explore variable-length axes Explore better axes assignment Incorporate more information from data set Multiple-edge connections Type of IPM calls Amount of data transmitted
37 Next Steps 37 Feature identification Compare hive plots for more distinct codes Compare hive plots for similar codes Identify features that visually distinguish codes Classification and anomaly detection Determine if features identified by visualization lead to better classifiers and anomaly detection
38 38 Conclusion
39 Summary 39 Motivation and goals Improve anomaly detection in HPC environments Improve classification of HPC codes Use exploratory visualization for feature selection
40 Summary 40 Motivation and goals Improve anomaly detection in HPC environments Improve classification of HPC codes Use exploratory visualization for feature selection Initial results Hive plots allow visual comparison of HPC codes Some features distinguish distinct HPC codes
41 References 41 Hive Plots Rational Approach to Visualizing Networks by Martin Krzywinski, Inanc Birol, Steven JM Jones and Marco A Marra in Briefings in Bioinformatics, volume 13, issue 5, pages , 2012 Network-Theoretic Classification of Parallel Computation Patterns by Sean Whalen, Sophie Engle, Sean Peisert, and Matt Bishop in International Journal of High Performance Computing Applications (IJHPCA), volume 26, number 2, pages , May 2012 Multiclass Classification of Distributed Memory Parallel Computations by Sean Whalen, Sean Peisert, and Matt Bishop to appear in Pattern Recognition Letters (PRL), 2012
42 Contact Information 42 Sophie Engle University of San Francisco Department of Computer Science w Sean Whalen Mount Sinai School of Medicine Institute for Genomics and Multiscale Biology w
43 Questions? 43
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