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1. INSTRUMENTATION
AND MEASUREMENT
M.PRASAD NAIDU
Msc Medical Biochemistry,
Ph.D Research scholar.

2. Abstract
 Why Instrumentation and Measurement ?
 Instrumentation Techniques
 Resources
 Data Analysis
 Case Study: Paradyn
› Guiding Principles
› System Overview
› W3
Search Model

3. Why Instrumentation and Measurement ?
• Gathering data to improve the next execution
of the program.
• Guiding scheduling decisions
• Adapting to computations while in execution

4. 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.

5.  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.

6. Data Storage Representation
• Scalars
– Counters
– Times
• Traces
• Vector series

7. Resources
• Software Abstractions
– Program Components
– Code in Executions
– Synchronization Objects
– Other Software Abstractions
• Hardware Abstractions
• Network Abstractions

8. Data Analysis
• Quantitative Performance
• Automating Performance Diagnosis
• Perturbation Analysis

9. Harrison institute of
biotechnology
Case Study

10. 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

11. 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.

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

13. Dynamic Instrumentation
 Dynamic Instrumentation Interface
› Metric Manager
› Instrumentation Manager
 Points, Primitives and Predicates
addCounter(fooFlg, 1)
addCounter(fooFlg, 1)
Foo()
{
….
….
}
SendMsg( dest, ptr, cnt, size)
{
….
….
}
if (fooFlg)
startTimer(msgTme, ProcTime)
if (fooFlg)
stopTimer(msgTme)

14. • Instrumentation generation
– Base Trampolines
– Mini-Trampolines
• Data Collection
• Internal Uses of Dynamic Instrumentation
– Resource Discovery
– Collection of dynamic mapping information for
HLL.

15. 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.

16. A sample “why” axis with several hypotheses
TopLevelHypotheses
SyncBottleNeck
HighSyncBlockingTimeFrequentSyncOperations
HighSyncContentionHighSyncHoldingTime

17. › 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
Semaphores Message SpinLock Barier

18. – 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.

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

20. Examples of Use

21. 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.

22. THANK YOU