Bifrost

Letting Smalltalk Loose

Jorge Ressia

www.scg.unibe.ch

Tuesday, August 23, 11

http://www.scg.unibe.ch

http://www.scg.unibe.ch

Computer revolution has not

happened yet


Computer revolution has not

happened yetAlan Kay

Keynote OOPSLA 1997


improve previous ways of thinking


create new ways of thinking


Object-orientedProgramming


Dynamic


We still go back to the source code


Debugging


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Profiling


Profile

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Domain-Specific Profiling 3

CPU time profiling

Mondrian [9] is an open and agile visualization engine. Mondrian describes avisualization using a graph of (possibly nested) nodes and edges. In June 2010a serious performance issue was raised1. Tracking down the cause of the poorperformance was not trivial. We first used a standard sample-based profiler.

Execution sampling approximates the time spent in an application’s methodsby periodically stopping a program and recording the current set of methodsunder executions. Such a profiling technique is relatively accurate since it haslittle impact on the overall execution. This sampling technique is used by almostall mainstream profilers, such as JProfiler, YourKit, xprof [10], and hprof.

MessageTally, the standard sampling-based profiler in Pharo Smalltalk2, tex-tually describes the execution in terms of CPU consumption and invocation foreach 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 inthe 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. Thisgeneral profiling information says that rendering nodes and edges consumes agreat share of the CPU time, but it does not help in pinpointing which nodesand edges are responsible for the time spent. Not all graphical elements equallyconsume resources.

Traditional execution sampling profilers center their result on the frames ofthe execution stack and completely ignore the identity of the object that receivedthe method call and its arguments. As a consequence, it is hard to track downwhich objects cause the slowdown. For the example above, the traditional profilersays that we spent 30.9% in MONode>>displayOn: without saying which nodeswere actually refreshed too often.

Coverage

PetitParser is a parsing framework combining ideas from scannerless parsing,parser combinators, parsing expression grammars and packrat parsers to modelgrammars and parsers as objects that can be reconfigured dynamically [11].

1 http://forum.world.st/Mondrian-is-slow-next-step-tc2257050.html#a2261116

2 http://www.pharo-project.org/


Domain

Profile

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CPU time profiling










Coverage





Mondrian


System ComplexityLanza and Ducasse 2003



CPU time profiling










Coverage





Which is the relationship?Domain-Specific Profiling 3

CPU time profiling










Coverage




?


What is the problem?


Fixed Object Model


Most of the time this is good


Restricts what we can do


Time


Time is a very useful concept


considering it absolute limit us


absolute object model limit us


objects that evolve


Why?


ExecutionReification

StructureEvolution

ProfilingDebugging


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When is the next state written?


Stop when the next message is received


Close the gap


Object Debugger



StructureEvolution

Debugging Profiling


MetaSpy


MetaSpy

TOOLS 2011Bergel etal.


Mondrian Profiler


System ComplexityLanza, Ducasse 2003


Profiling

StructureEvolution

Debugging



What if we do not know what to evolve?


?Tuesday, August 23, 11

Prisma


ScarringTuesday, August 23, 11

ScanningTuesday, August 23, 11

Back in time Debugger


Back in time Debugger

Object Flow Debugger

Lienhard etal. ECOOP 2008


Instance variable history


:Person field-write@t2

field-write@t3

init@t1

'Doe'

person := Person new t1...name := 'Doe' t2...name := 'Smith' t3

'Smith'

nullpredecessor

predecessor

value

value

value

name

name

name


Profiling


Debugging

StructureEvolution


Talents

scg.unibe.ch/research/talents


Talents

scg.unibe.ch/research/talents

IWST 2011J. Ressia, T. Gîrba, O. Nierstrasz, F. Perin and

L. Renggli


Dynamically composable units of

reuse


moosetechnology.org


aFAMIXClass isTestClass

Keyinstance-ofmessage sendlookup

FAMIXEntity

FAMIXType

isTestClassFAMIXClass

aFAMIXClass

1

2

3

self inheritsFrom: 'TestCase'

MooseEntity

...


aFAMIXClass isTestClass

FAMIXClass

aFAMIXClass

1

2

4

aFAMIXClass inheritsFrom: 'TestCase'

aJeeClassTalent

Keyinstance-ofmessage sendlookupacquire

3aJeeClassTalent talent isTestClass

FAMIXEntity

FAMIXType

MooseEntity

...


•@ alias

•- exclusion

•, composition

Operators


Flattening


Scoping


Evolution friendly Object Model


Organize the Meta-level


ExplicitMeta-objects


Object

Meta-object

Class


Evolved Object

Meta-object

Class


Object>>haltAtNextMessage | aMetaObject | aMetaObject := BFBehavioralMetaObject new. aMetaObject when: (BFMessageReceiveEvent new) do: [ self metaObject unbindFrom: self.

TransparentBreakpoint signal ]. aMetaObject bindTo: self


We let Smalltalk loose


Object Debugger Prisma

Subjectopia MetaSpy

Talents Chameleon


scg.unibe.ch/jenkins/


http://www.scg.unibe.ch/jenkins/

http://www.scg.unibe.ch/jenkins/

scg.unibe.ch/research/bifrost


Bifrost

Technology

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