Performance Analysis And Visualization

By:Mehdi Semsarzadeh

Chapter 15Chapter 15

Outline:

• Introduction

• Grid Performance Problems

• Grid Application Analysis Constraint

• Grid Performance Analysis Techniques

• Current Performance Analysis Tools

Introduction

• Performance measurement techniques provide the raw data needed to identify and correct performance problems

• Performance visualization, correlation, evaluation tools must process raw performance data, correlate it with appropriate network and highlight performance problems in meaningful ways

Grid Performance Problems

• performance measurement at many levels

Grid Performance Problems

• The raw data must be correlated across semantic levels and present in way that match the user’s semantic model

• interactive drilldown to lower semantic levels, allowing users to explore underlying causes of poor performance

• Dynamic optimization during application execution

Grid Application Analysis Constraint

• Complex , High-Dimensional Data• Performance analysts must capture dynamic performance data at all

system levels• The components of these levels interact on a wide variety of time

scale (from ns for HW to second or minutes for interacting with network devices)

• The number of components can be very large (100 or 1000 processors)

• =>Performance analysis tools must rely on strategies that highlight key relationships rather than on multivariant displays that show everything while illuminate nothing

Grid Application Analysis Constraint

• Multilevel Semantic Correlation• The wider semantic gap between user-written code

and HW /SW resource, by increase in application sophistication, (e.g. loop transformation by compiler)

• Different execution behavior (from what developer expected) because of aggressive compiler transformations

• => supporting hierarchical HW/SW representation to identify the true root cause

Grid Application Analysis Constraint

• Mixed Ordinal and Categorical Data– Ordinal Data: the number of messages sent or received, the number of

cache misses, …– Categorical Data: processor states (e.g., blocked, queued, idle, or busy),

software state , ….

• The categorical data lackes a total ordering

• It cannot be directly mapped to the same visual attributes as ordinal data

• => using symbols/colors to identify individual categorical values

• =>mapping categorical values to numerical values

Grid Performance Analysis Techniques

• Dynamics• Static• Dynamic

• Cardinality• Univariate• Multivariate

• Ordinality• Ordinal• Semiordinal• Categorical

Grid Performance Analysis Techniques

• Multivariate Data– scatter plots– kiviat Diagrams

• Eight Processor Utilization With Kiviat Diagram

Grid Performance Analysis Techniques

• Semiordinal Data– Histograms

• use to provide relative counts of operation

• Network traffic x-axis:domain names

• y-axis:message retransmition count

– GanttCharts• usually use to display processor utilization

Grid Performance Analysis Techniques

Grid Performance Analysis Techniques

• Structural Display– correlates dynamic performance data with structural

representation of HW/SW component

– Geographic and network mappings– Architectural mappings– Execution graphs– Source code correlation

Geographic and network mappings

Execution graph

Source code correlation

Current Performance Analysis Tools

• ParaGraph

• IPS-2 and Paradyn

• Chitra

• Pablo and Svpablo

• Avatar

Paradyn• Relies on a hierarchical approach• The hierarchy is represented as a tree• The root is the program and branches represent machines and

processes• Bottleneck identification by supporting real-time insertion and removal

of measurement probes during execution.• Use w3 technique to automatically determine the causes of bottleneck

– Why is the application performance poorly?– Where is the performance problem?– When does the problem occur?

Paradyn

Paradyn

Paradyn

Avatar

• A virtual reality framework for analyzing complex, time-varying, multivariate data

• Displays a three-dimensional globe, the analyst can rotate the globe, view traffic on a worldwide basis

• The analyst can select a node to obtain detailed information

Avatar

Grid Performance Analysis Challenges

• Application codes become more complex,dynamic =>Their behavior is increasingly nondeterministic

• the posterior analysis becomes ineffective• => performance optimization softwares must support realtime

correction of performance problems• =>closed-loop performance optimization:

• distributed sensors that provide low-overhaed,are accessible from any site on the grid

• resource actuators that enable tasks to modify resource allocation policies

• a set of fuzzy logic decision mechanism to realize system modification

References

• The Paradyn Parallel Performance Measurement Tools– Barton P. Miller Mark, D. Callaghan, Jonathan M. Cargille, Jeffrey ,…

• Real-Time Geographic Visualization of World Wide Web Traffic– Stephen E. Lamm and Daniel A. Reed

• www-pablo.cs.uiuc.edu