domain-specific profiling - tools 2011

Domain-Specific

Profiling

Alexandre Bergel, Oscar Nierstrasz, Lukas Renggli and Jorge Ressia

Profiling:Is the activity of analyzing a program execution.

Traditional Profilers

Source Report

Profile

Examples

CPU time

Mondrian

System Complexity

Lanza and Ducasse 2003

D omain-Sp ecific P rofiling 3

C P U t i m e p rofi li ng

Mondrian [9] is an open and agile visualiza t ion engine. Mondrian describes avisualiza t ion using a graph of (possibly nested) nodes and edges. In June 2010a serious performance issue was raised1 . Tracking down t he cause of t he poorperformance was not t rivial. We first used a st andard sample-based profiler.

E xecu t ion sampling approxima tes t he t ime spent in an applica t ion’s met hodsby periodically stopping a program and recording t he current set of met hodsunder execu t ions. Such a profiling technique is rela t ively accurate since i t hasli t t le impact on t he overall execu t ion. T his sampling technique is used by almostall mainst ream profilers, such as J P rofiler, Your K i t , xprof [10], and hprof.

M essage Tally, t he st andard sampling-based profiler in P haro Small t alk2 , tex-t ually describes t he execu t ion in terms of C P U consump t ion and invocat ion foreach met hod 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 t hat t he vir t ual machine spent abou t 54% of i ts t ime int he met hod displayOn: defined in t he class MORoot. A root is t he unique non-nested node t hat cont ains all t he nodes of t he edges of t he visualizat ion. T hisgeneral profiling informat ion says t hat rendering nodes and edges consumes agrea t share of t he C P U t ime, bu t i t does not help in pinpoint ing which nodesand edges are responsible for t he t ime spent . Not all graphical elements equallyconsume resources.

Tradi t ional execu t ion sampling profilers center t heir resul t on t he frames oft he execu t ion st ack and completely ignore t he ident i ty of t he ob ject t hat receivedt he met hod call and i ts arguments. A s a consequence, i t is hard to t rack downwhich ob jects cause t he slowdown. For t he example above, t he t radi t ional profilersays t ha t we spent 30.9% in MONode > >displayOn: wi t hou t saying which nodeswere act ually refreshed too often.

C over age

Pet i t Parser is a parsing framework combining ideas from scannerless parsing,parser combinators, parsing expression grammars and packrat parsers to modelgrammars and parsers as ob jects t hat can be reconfigured dynamically [11].1 h t t p : / / f o r um . wo r l d . s t / Mond r i an - i s - s l ow - nex t - s t ep - t c2257050 . h t m l #

a22611162 h t t p : / / www . pha r o - p r o j ec t . o r g /

Which is the relationship?

D omain-Sp ecific P rofiling 3

C P U t i m e p rofi li ng

Mondrian [9] is an open and agile visualiza t ion engine. Mondrian describes avisualiza t ion using a graph of (possibly nested) nodes and edges. In June 2010a serious performance issue was raised1 . Tracking down t he cause of t he poorperformance was not t rivial. We first used a st andard sample-based profiler.

E xecu t ion sampling approximates t he t ime spent in an applica t ion’s met hodsby periodically stopping a program and recording t he current set of met hodsunder execu t ions. Such a profiling technique is rela t ively accurate since i t hasli t t le impact on t he overall execu t ion. T his sampling technique is used by almostall mainst ream profilers, such as J P rofiler, Your K i t , xprof [10], and hprof.

M essage Tally, t he st andard sampling-based profiler in P haro Small t alk2 , tex-t ually describes t he execu t ion in terms of C P U consump t ion and invocat ion foreach met hod 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 t hat t he vir t ual machine spent abou t 54% of i ts t ime int he met hod displayOn: defined in t he class MORoot. A root is t he unique non-nested node t hat cont ains all t he nodes of t he edges of t he visualizat ion. T hisgeneral profiling informa t ion says t ha t rendering nodes and edges consumes agrea t share of t he C P U t ime, bu t i t does not help in pinpoint ing which nodesand edges are responsible for t he t ime spent . Not all graphical elements equallyconsume resources.

Tradi t ional execu t ion sampling profilers center t heir resul t on t he frames oft he execu t ion st ack and completely ignore t he ident i ty of t he ob ject t ha t receivedt he met hod call and i ts arguments. A s a consequence, i t is hard to t rack downwhich ob jects cause t he slowdown. For t he example above, t he t radi t ional profilersays t ha t we spent 30.9% in MONode > >displayOn: wi t hou t saying which nodeswere act ually refreshed too often.

C over age

Pet i t Parser is a parsing framework combining ideas from scannerless parsing,parser combinators, parsing expression grammars and packrat parsers to modelgrammars and parsers as ob jects t ha t can be reconfigured dynamically [11].1 h t t p : / / f o r um . wo r l d . s t / Mond r i an - i s - s l ow - nex t - s t ep - t c2257050 . h t m l #

a22611162 h t t p : / / www . pha r o - p r o j ec t . o r g /

?

Coverage

scg.unibe.ch/research/helvetia/petitparser

210 Methods

Java Grammar

100% coverage to build grammar

210 Methods

Java Grammar

100% coverage to build grammar

Is each production of the grammar covered?

Source Report

Domain

Profile

SourceCode

Domain

TraditionalProfilers

SourceCode

Domain

TraditionalProfilers

Domain-SpecificProfilers

What does it

mean?

Specify

Capture

Present

MetaSpy

ProfilerInstrumenter

DomainObject

DomainObject

DomainObject

Domain

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DomainObject

DomainObject

DomainObject

Domain

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DomainObject

DomainObject

DomainObject

Domain

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MetaInstrumenter

installsetUptearDownuninstall

handler

installuninstall

announcer

AnnouncementInstrumenter

doesNotUnderstand:run:with:in:installsetUptearDownuninstall

theClassselectormethdo

MethodInstrumenter

installuninstall

parsergrammarreplacement

ParserInstrumenter

Profiler

observeClass:do:observeClass:selector:doobservePackage:do:observePackagesMatching:do:observeParser:in:do:installsetUptearDownuninstall

modelstrategies

setUpvisualize

MondrianProfilersetUpvisualize

OmniBrowserProfilersetUpvisualize

PetitParserProfiler

Instrumentation strategies Profilers

User provided classes

Specify the Domain interests

Capture the runtime

information

Present the results

Mondrian Profiler

System Complexity

Lanza, Ducasse 2003

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

Profiler

PetitParserProfiler>>setUp self model allParsers

do: [ :parser | self

observeParser: parser in: self grammar do: [ ... counting ... ] ]

Implementation

Instrumentation

Two options

Hooking to the Domain

Reflection

Reflection

scg.unibe.ch/research/bifrost

Organize the Meta-level

ExplicitMeta-objects

Object

InstrumentationMeta-object

Class

Object

InstrumentationMeta-object

Class

Instrumented Object

InstrumentationMeta-object

Class

Partial ReflectionSelective Reifications

UnanticipatedRuntime Integration

Adaptation Composition

Source

Profile

Source

Profile

Domain-specific

Information

MetaSpy

SourceCode

Domain

TraditionalProfilers

Domain-SpecificProfilers