Domain-Specific Profiling - TOOLS 2011

1. Domain-Specific
Profiling
Alexandre Bergel, Oscar Nierstrasz,
Lukas Renggli and Jorge Ressia

2. Profiling:
Is the activity of analyzing a program
execution.

3. Traditional Profilers

4. Profile
Source Report

5. Examples

6. CPU time

7. Mondrian

9. o mpl exity
S m C ucasse 2003
yste d D n
Lanza a

10. lit tle impact on the overall execution. T his sampling technique is u
all mainstream profilers, such as JProfiler, Your K it, xprof [10], an
Message Tally, the standard sampling-based profiler in Pharo Sm
tually describes the execution in terms of C P U consumption and i
each method of Mondrian:
54.8% {11501ms} MOCanvas > > drawOn:
54.8% {11501ms} MORoot(MONode) > > displayOn:
30.9% {6485ms} MONode > > displayOn:
| 18.1% {3799ms} MOEdge > > displayOn:
...
| 8.4% {1763ms} MOEdge > > displayOn:
| | 8.0% {1679ms} MOStraightLineShape > > display:on:
| | 2.6% {546ms} FormCanvas > > line:to:width:color:
...
23.4% {4911ms} MOEdge > > displayOn:
...
We can observe that the virtual machine spent about 54% o
the method displayOn: defined in the class MORoot. A root is the
nested node that contains all the nodes of the edges of the visua
general profiling information says that rendering nodes and edge

11. Domain-Specific P rofiling 3
C P U t i me p rofiling
Which is the relationship?
Mondrian [9] is an open and agile visualization engine. Mondrian describes a
visualization using a graph of (possibly nested) nodes and edges. In June 2010
a serious performance issue was raised1 . Tracking down the cause of the poor
performance was not trivial. We first used a standard sample-based profiler.
E xecution sampling approximates the time spent in an application’s methods
by periodically stopping a program and recording the current set of methods
under executions. Such a profiling technique is relatively accurate since it has
lit tle impact on the overall execution. T his sampling technique is used by almost
all mainstream profilers, such as JProfiler, Your K it, xprof [10], and hprof.
Message Tally, the standard sampling-based profiler in Pharo Smalltalk 2 , tex-
tually describes the execution in terms of C P U consumption and invocation for
each method of Mondrian:
54.8% {11501ms} MOCanvas > > drawOn:
54.8% {11501ms} MORoot(MONode) > > displayOn:
30.9% {6485ms} MONode > > displayOn:
?
| 18.1% {3799ms} MOEdge > > displayOn:
...
| 8.4% {1763ms} MOEdge > > displayOn:
| | 8.0% {1679ms} MOStraightLineShape > > display:on:
| | 2.6% {546ms} FormCanvas > > line:to:width:color:
...
23.4% {4911ms} MOEdge > > displayOn:
...
We can observe that the virtual machine spent about 54% of its time in
the method displayOn: defined in the class MORoot. A root is the unique non-
nested node that contains all the nodes of the edges of the visualization. T his
general profiling information says that rendering nodes and edges consumes a
great share of the C P U time, but it does not help in pinpointing which nodes
and edges are responsible for the time spent. Not all graphical elements equally
consume resources.
Traditional execution sampling profilers center their result on the frames of
the execution stack and completely ignore the identity of the ob ject that received
the method call and its arguments. As a consequence, it is hard to track down
which ob jects cause the slowdown. For the example above, the traditional profiler
says that we spent 30.9% in MONode > > displayOn: without saying which nodes
were actually refreshed too often.
C overage
Petit Parser is a parsing framework combining ideas from scannerless parsing,

12. Coverage

13. scg.unibe.ch/research/helvetia/petitparser

14. Java Grammar
210 Methods
100% coverage to build grammar

16. Java Grammar
210 Methods
100% coverage to build grammar
Is each production of the grammar covered?

17. Profile
Source Report
Domain

18. Domain
Source Traditional
Code Profilers

19. Domain-Specific
Domain
Profilers
Source Traditional
Code Profilers

20. What does it
mean?

21. Specify

22. Capture

23. Present

24. MetaSpy

25. Instrumenter Profiler

26. Domain
Domain
Object
Domain
Object
Domain
Object

27. Domain
Domain
Object
Domain
Object
Domain
Object

28. Domain
Domain
Object
Domain
Object
Domain
Object

29. Instrumentation strategies Profilers
MetaInstrumenter Profiler
handler model
install strategies
setUp observeClass:do:
tearDown observeClass:selector:do
uninstall observePackage:do:
observePackagesMatching:do:
observeParser:in:do:
install
Announcement MethodInstrumenter ParserInstrumenter setUp
Instrumenter theClass parser tearDown
announcer selector grammar uninstall
install methdo replacement
uninstall doesNotUnderstand: install
run:with:in: uninstall
install
setUp MondrianProfiler OmniBrowserProfiler PetitParserProfiler
tearDown
setUp setUp setUp
uninstall
visualize visualize visualize
User provided classes

30. Specify the Domain interests
Capture the runtime
information
Present the results

31. Mondrian Profiler

33. o mpl exity
S m Ccasse 2003
yste Du
Lanza,

34. MondrianProfiler>>setUp
self model root allNodes do: [ :node |
self
observeObject: node
selector: #displayOn:
do: [ ... counting ... ] ]

36. Profiler

37. PetitParserProfiler>>setUp
self model allParsers
do: [ :parser |
self
observeParser: parser
in: self grammar
do: [ ... counting ... ] ]

39. Implementation

40. Instrumentation

41. Two options
Hooking to the Domain
Reflection

42. Reflection

43. scg.unibe.ch/research/
bifrost

44. Organize the
Meta-level

45. Explicit
Meta-objects

46. Class
Instrumentation
Meta-object
Object

47. Class
Instrumentation
Meta-object
Object

48. Class
Instrumentation
Meta-object
Instrumented Object

49. Partial Reflection
Selective Reifications
Unanticipated
Runtime Integration
Adaptation Composition

50. Profile
Source

51. Profile
Source

52. Domain-specific
Information

53. MetaSpy
Domain-
Domain Specific
Profilers
Source Traditional
Code Profilers