INSTRUMENTATION AND MEASUREMENT.

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INSTRUMENTATION AND MEASUREMENT

• M.PRASAD NAIDU
• Msc Medical Biochemistry,
• Ph.D Research scholar.

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Abstract

• Why Instrumentation and Measurement ?
• Instrumentation Techniques
• Resources
• Data Analysis
• Case Study Paradyn
• Guiding Principles
• System Overview
• W3 Search Model

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Why Instrumentation and Measurement ?

• Gathering data to improve the next execution of
  the program.
• Guiding scheduling decisions
• Adapting to computations while in execution

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Instrumentation Techniques

• Program Instrumentation Techniques
• Manual Programmer inserted directives
• Automatic No direct user involvement
• Binary Rewriting
• Dynamic Instrumentation
• Processor Instrumentation Techniques
• Information includes timers, memory system
  performance, processor usage, etc.
• Available mostly through special registers or
  memory mapped location.
• Example Pentium Pro provides performance data
  through MSRs. These registers include 64 bit
  cycle clock and counts of memory read /write, L1
  cache misses, pipeline flushes, etc.
• Hardware assisted trace generation.

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• Operating System Instrumentation Techniques
• Information includes behavior of virtual memory,
  file system, file cache etc.
• Instrumentation in the form of APIs for
  applications to access these variables.
• Network Instrumentation Techniques
• Ways of measuring
• Passive
• Example RMON protocol defines SNMP MIB variables
  to report traffic statistics over hubs and
  switches.
• Active
• Example Ping, NWS in grid style computing.

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Data Storage Representation

• Scalars
• Counters
• Times
• Traces
• Vector series

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Resources

• Software Abstractions
• Program Components
• Code in Executions
• Synchronization Objects
• Other Software Abstractions
• Hardware Abstractions
• Network Abstractions

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Data Analysis

• Quantitative Performance
• Automating Performance Diagnosis
• Perturbation Analysis

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Harrison institute of biotechnology

• Case Study

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Guiding Principles and Characteristics

• Scalability
• Automate the search for performance problems
• Provide well-defined data abstractions
• Support heterogeneous environments
• Support high level parallel languages
• Open interfaces for visualization and new data
  sources
• Streamlined use

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System Overview

• Basic Abstractions
• Metric-focus grid
• Time Histograms
• Components of the System
• Main Paradyn Process
• Performance Consultant
• Visualization Manager
• Data Manager
• User Interface Manager
• Paradyn daemons
• External Visualization Processes.

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HIB
Histogram Visualization
Table Visualization
Tabular Summary CPU 3.0 4.0
Messages 117 81
Visualization Manager
Visi Thread
Visi Thread
User Interface Manager
Performance Consultant
Data Manager
Metric Manager Instrumentation Manager
Metric Manager Instrumentation Manager
HIB Daemon(s)
Application
Processes
Application
Processes
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Dynamic Instrumentation

• Dynamic Instrumentation Interface
• Metric Manager
• Instrumentation Manager
• Points, Primitives and Predicates

Foo() . .
addCounter(fooFlg, 1)
addCounter(fooFlg, 1)
SendMsg( dest, ptr, cnt, size) . .
if (fooFlg) startTimer(msgTme, ProcTime)
if (fooFlg) stopTimer(msgTme)
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• Instrumentation generation
• Base Trampolines
• Mini-Trampolines
• Data Collection
• Internal Uses of Dynamic Instrumentation
• Resource Discovery
• Collection of dynamic mapping information for HLL.

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The W3 Search Model and the Performance Consultant

• Why ? Where ? When ?
• The Why Axis
• Why is the application performing poorly ?
• Potential performance problems are represented as
  hypotheses and tests.
• Hypotheses represent activities universal to all
  parallel computations.
• Hypotheses can be refined into more refined
  hypotheses using a search hierarchy.
• Tests are Boolean functions that evaluate the
  validity of a hypotheses.
• Tests are expressed in terms of a threshold and
  metrics calculated by the Instrumentation
  Manager.

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A sample why axis with several hypotheses
TopLevelHypotheses
SyncBottleNeck
HighSyncBlockingTime
FrequentSyncOperations
HighSyncContention
HighSyncHoldingTime
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• The Where Axis
• Where is the performance problem ?
• Pinpoints the problem specific to program
  components.
• Each hierarchy in where axis has multiple
  levels, with the leaf nodes being the instances
  of resources used by the application.

SyncObject
Message
SpinLock
Barier
Semaphores
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• The When Axis
• When does the problem occur ?
• Represents periods of time during which
  performance problems can occur.
• The Performance Consultant
• This module discovers performance problems by
  searching the space defined by W3 Search Model.
• Fully automated search but also allows user to
  make manual refinements.

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Open Visualization Interface

• Paradyn provides a simple library and RPC
  interface to access performance data in
  real-time.
• Visualization modules (visis) in Paradyn are
  external processes that use this library and
  interface.
• Currently provides visis for time-histograms,
  bar charts and tables.

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Examples of Use
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Conclusion

• Computational grids are focused on high
  performance distributed computing. To achieve
  high performance, such systems need to provide
  tools that enable the programmer to realize the
  potential performance inherent in such a system.

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• THANK YOU