A Characterization of Reuse Technologies

A Characterization of Reuse Technologies

Ted J. BiggerstaffTed J. [email protected]@austin.rr.com

Reuse Technologies Concrete componentsConcrete components

e.g., Functions, OOP, Frameworks, DCOM, …e.g., Functions, OOP, Frameworks, DCOM, … Compositionally Derived Components Compositionally Derived Components

e.g., Templates, GenVoca, …e.g., Templates, GenVoca, … Pattern-directed transformation systemsPattern-directed transformation systems

e.g., Draco, CAPE, IP, DMS, Sinapse, ...e.g., Draco, CAPE, IP, DMS, Sinapse, ...

Control Regimes

PD Search – Independent Local ChoicesPD Search – Independent Local Choices Strategically Blind – exploding search spaceStrategically Blind – exploding search space Biased Toward Local Syntactic StructureBiased Toward Local Syntactic Structure Rules are General & Allow Easy ExtensionRules are General & Allow Easy Extension

Control Regimes PD Search – Independent Local ChoicesPD Search – Independent Local Choices

Strategically Blind – exploding search spaceStrategically Blind – exploding search space Biased Toward Local Syntactic StructureBiased Toward Local Syntactic Structure Rules are General & Allow Easy ExtensionRules are General & Allow Easy Extension

MetaPrograms – Algorithmic MetaPrograms – Algorithmic Strategically & Tactically PurposefulStrategically & Tactically Purposeful State Dependent Actions Reduce Search SpaceState Dependent Actions Reduce Search Space Representation Makes Extensions HardRepresentation Makes Extensions Hard

Constraint Propagation Problem

Refine tox

Program

x Constraint

Reuse LibraryAlternative Refinementsof

PD Explosion Control

Group Rules by PhaseGroup Rules by Phase Implicit Phases based on Distinct DSLsImplicit Phases based on Distinct DSLs

Mutually Exclusive DSL Operators & OperandsMutually Exclusive DSL Operators & Operands

Draco Domains

Abstractions:

Keys,Relations,Attributes, …Operations:

Search, Delete,Add, Update,

Attach,Detach, ...

Abstractions:Cursor, Container,

Keys,Relations,Attributes, …Operations:

Search, Delete,Add, Update,

Attach,Detach, ...

Data Base

RelationalAlgebra

Abstract DataTypes

C Code

Tuple Index

Refinements

Data Base

Relational

Algebra

Abstract Data

TypesC Code

Tuple Index

Component: Insert(Value V, Container C){Refinement: Stack Condition: LIFO(C) Code: {Push(V,C)}}{Refinement: Sorted Container Condition: SortedOn(C, Value) Assertion: SortedOn(C, Value) Code: {Merge(V,C)}}

...}

PD Explosion Control

Group Rules by PhaseGroup Rules by Phase Implicit Phases based on Distinct DSLsImplicit Phases based on Distinct DSLs

Mutually Exclusive DSL Operators & OperandsMutually Exclusive DSL Operators & Operands … … and based MetaProgramand based MetaProgram

{Refine DSL; Optimize Refined DSL}{Refine DSL; Optimize Refined DSL}

Domain to self:Join(?Relation1, Empty_Relation, ?Attribute)=>Empty_RelationSelect(?Relation1,TRUE_expression)=>Relation1Select(?Relation1,FALSE_expression)=> Empty_Relation

Data Base

Relational

Algebra

Abstract Data

TypesC Code

Tuple Index

Optimizing Transforms

Kinds of Optimization

Simplification & DS OptimizationsSimplification & DS Optimizations Key Point: DS Reductions Not CombinationsKey Point: DS Reductions Not Combinations

Pattern-Directed Systems Example domain: communications protocols Example domain: communications protocols Benefits:Benefits:

Fine grain rules allow great horizontal scalingFine grain rules allow great horizontal scaling Small search spaces (DSL->DSL) vs. one largeSmall search spaces (DSL->DSL) vs. one large Powerful in-domain optimizationsPowerful in-domain optimizations

Shortcomings:Shortcomings: Global reorganizing optimizations can explode Global reorganizing optimizations can explode

search spacesearch space

Essence of The Technology

ClassClass ElementsElements OperationsOperationsConcrete Reuse PL Struct. Concrete Reuse PL Struct. Hand Assem.Hand Assem.CompositionComposition Abstract PL Abstract PL InliningInliningPD GeneratorPD Generator DSL Struct. DSL Struct. PD XformsPD Xforms




e.g., Draco, CAPE, IP, DMS, Sinapse, ...e.g., Draco, CAPE, IP, DMS, Sinapse, ... Reorganizing generator systemsReorganizing generator systems

AO generator (AOG), AOP, ...AO generator (AOG), AOP, ...

DSL for Sobel Edge Detection

b=[(a s)2 + (a s’)2]1/2

a b

Problem: Antagonistic Goals

High level operators and operands provide High level operators and operands provide programming leverage & variationsprogramming leverage & variations

E.g., (image E.g., (image neighborhood) convolution neighborhood) convolution

But fracture and de-localize code piecesBut fracture and de-localize code pieces b=((a b=((a s) s)22 + (a + (a s’) s’)22 ) )1/21/2

Needed optimizations: code sharing, re-org. & re-weavingNeeded optimizations: code sharing, re-org. & re-weaving

Conventional Optimization approaches induce Conventional Optimization approaches induce large search spaces (EXPLOSION)large search spaces (EXPLOSION)

Kinds of Optimization Simplification & DS OptimizationsSimplification & DS Optimizations

Key Point: DS Reductions Not CombinationsKey Point: DS Reductions Not Combinations LocalizationLocalization

Separate Parts Woven into Computational FormSeparate Parts Woven into Computational Form

General Interdependent Parts Problem NP CompleteGeneral Interdependent Parts Problem NP Complete Tractable Via Narrow Domain Knowledge & GroupingTractable Via Narrow Domain Knowledge & Grouping

Weave

Explosion Control Group Rules by PhaseGroup Rules by Phase

Implicit Phases based on Distinct DSLs & Implicit Phases based on Distinct DSLs & {Refine; Optimize} MetaProgram{Refine; Optimize} MetaProgram

Explicit Finer Grain PhasesExplicit Finer Grain Phases {Loop Optimization, Code Generation, …}{Loop Optimization, Code Generation, …}

Group Rules by Named EntityGroup Rules by Named Entity E.g., Type or Operator or Arbitrary EntityE.g., Type or Operator or Arbitrary Entity Mechanisms in AOG, Stratego, etc.Mechanisms in AOG, Stratego, etc.

AOG Uses Both Entity and PhaseAOG Uses Both Entity and Phase

Explicit Entities & Phases Phase (Job)Phase (Job)

Type Type (Semantics)(Semantics)

Loop Loop OptimizationOptimization(Fusion2)(Fusion2)

CodeCodeGenerationGeneration(Fusion3)(Fusion3)

. . .. . .

OperatorOperator ,, ,, . . .. . .ImageImage , , , , . . .. . .Neighbor-Neighbor-hoodhood

,, , , . . .. . .

. . .. . . . . .. . . . . .. . . . . .. . .

Example Loop Opt. Transform Phase (Job)Phase (Job)

Type Type (Semantics)(Semantics)

Loop Loop OptimizationOptimization(Fusion2)(Fusion2)

CodeCodeGenerationGeneration(Fusion3)(Fusion3)

. . .. . .

OperatorOperator . . .. . .ImageImage , , . . .. . .Neighbor-Neighbor-hoodhood

. . .. . .

. . .. . . . . .. . . . . .. . . . . .. . .

Example Loop Opt. Transform

(=> compositeleaf fusion2 image

Refine Image to BWPixel with loop shorthand tags

Name, Phase, Location

LHS

RHS

PreRoutine PostRoutine)





`$(pand $(por (leaf ?op) ?op)) …sub-pattern to get type …

…sub-pattern to get dimensions of ?op…)

RHS

LHS






`$(pand $(por (leaf ?op) ?op)) ($(spanto ?pre (tags)) (tags $(spanto ?pretags (itype ?itype))

?spaceover $(remain ?posttags))) …sub-pattern to get dimensions of ?op…)

RHS

LHS






RHS

enablecompositeleaf nil)

Enablecompositeleaf creates ?newleaf, ?idx1, ?idx2

Pre &Post Routines



LHS





`(leaf ?newleaf (tags (commasplice ?pretags)

(_Q (_FORALL (?newleaf) (,arefop (?op) ?idx1 ?idx2)))

(Itype BWPixel) (commasplice ?posttags)))enablecompositeleaf nil)

Enablecompositeleaf creates ?newleaf, ?idx1, ?idx2

Pre &Post Routines



LHS

RHS

Loop Control Phase Traversal

b=[(a s)2 + (a s’)2]1/2

You Are Here

(leaf a (tags (itype image)))

BehindThe

Scenes

CLOS Object

PropertyList

Pattern Match Resultb=[(a s)2 + (a s’)2]1/2


BehindThe

Scenes


?spaceover $(remain ?posttags)))…)

{ {

{

?op?spaceover

?pre?pretags ?itype ?posttags

Pattern Match Resultb=[(a s)2 + (a s’)2]1/2


BehindThe

Scenes

Define enablecompositeleaf (at, bindings){?idx1 = Create iterator; ?idx2 = Create iterator; ?newleaf = Create bwpixel; return extended bindings}

{ {

{

?op?spaceover

?pre?pretags ?itype ?posttags

?idx1 idx48146?idx2 idx48149?newleaf bwpixel48143

Rewrite Resultb=[(bwpixel48143 s)2 + (a s’)2]1/2

BehindThe

Scenes

`(leaf ?newleaf (tags (commasplice ?pretags)

(_Q (_FORALL (?newleaf) (,arefop (?op) ?idx1 ?idx2)))

(itype bwpixel) (commasplice ?posttags)))

(leaf bwpixel48143 (tags (_q (_forall (bwpixel48143) (aref (a) idx48146 idx48149))) (itype bwpixel)))

Kinds of Optimization Simplification & DS OptimizationsSimplification & DS Optimizations

Key Point: DS Reductions Not CombinationsKey Point: DS Reductions Not Combinations LocalizationLocalization

Separate Parts Woven into Computational FormSeparate Parts Woven into Computational Form Architectural ShapingArchitectural Shaping

Interdependent, Composed Parts Changed per Interdependent, Composed Parts Changed per Global ConstraintsGlobal Constraints

Architectural Shaping

Computational Structure Shaped per Computational Structure Shaped per External ConstraintsExternal Constraints SIMD (MMX), DB, UI, Middleware, NetworkSIMD (MMX), DB, UI, Middleware, Network

Non-Parallel Architectural EffectsB=(A s)

Image A

B[i,j]=0

B[i,j]+

* s-1,-1

* s-1,0

* s-1,1

* s1,1

“b=[(a s)2 + (a s’)2]1/2”

INT JM1, JP1, IM1, IP1; FOR (I=0; I < (M-1); ++I)

{ IM1=I-1; IP1= I+1; FOR (J=0; J < (N-1); ++J)

{ JM1= J-1; JP1 = J+1; IF(I==0 || J==0 || (I==(M-1)) || (J==(N-1))) THEN {B[I, J] = 0 ; } ELSE

{T1=A[IP1 , JP1] - A[IP1 , JM1] +(A[I , JP1] * 2) - (A[I , JM1] * 2) + A[IM1 , JP1] - A[IM1 , JM1];

T2 = A[IP1 , JP1] + (A[IP1 , J] * 2) +A[IP1 , JM1] - A[IM1 , JP1] -(A[IM1 , J] * 2) - A[IM1 , JM1];

B[I, J] = SQRT(T1*T1 + T2*T2 );}}}

Parallel Architectural EffectsB=(A s)

Image AB[0,j]=0

B[(m-1),j]=0B[i,0]=0

B[i, (n-1)]=0

Pixels Weights S

B[i,j]+

Pmadd3( * )

Pmadd3( * )

Pmadd3( * )

“b=[(a s)2 + (a s’)2]1/2”

{int s[(-1:1), (-1:1)]={{-1, 0, 1}, {-2, 0 , 2}, {-1, 0, 1}};int sp [(-1:1), (-1:1)]={{-1, -2, -1}, {0, 0, 0}, {-1, -2, -1}};for (j=0; j < n; j++) b[0,j] = 0 ;for (i=0; i < m; i++) b[i,0] = 0 ;for (j=0; j < n; j++) b[(m-1),j] = 0 ;for (i=0; i < m; i++) b[i,(n-1)] = 0 ;{for (i=1; i < (m-1); i++) {for (j=1; j < (n-1); j++) {t1 = UNPACKADD(PADD2

(PADD2(PMADD3 (&(a[i-1, j-1]), &(s[-1, -1])) , PMADD3 (&(a[i, j-1]), &(s[ 0, -1]))), PMADD3 (&(a[i+1,j-1]), &(s[ 1, -1])));

t2 = UNPACKADD(PADD2 (PADD2(PMADD3 (&(a[i-1, j-1]), &(sp[-1, -1])),

PMADD3 (&(a[i, j-1]), &(sp[ 0, -1]))), PMADD3 (&(a[i+1, j-1]), &(sp[ 1, -1])))));

b[i,j] = sqrt(t1*t1 + t2*t2);}}}

Architectural Shaping Computational Structure Shaped per External Computational Structure Shaped per External

ConstraintsConstraints SIMD (MMX), DB, UI, Middleware, NetworkSIMD (MMX), DB, UI, Middleware, Network

Central MetaProgram Combinatorial ComplexityCentral MetaProgram Combinatorial Complexity O(100) Transforms To Parallelize ExampleO(100) Transforms To Parallelize Example Must Recognize Opportunity and PlaceMust Recognize Opportunity and Place Must Rediscover Domain Knowledge (DK)Must Rediscover Domain Knowledge (DK)

Much DK Known About Parts at Creation TimeMuch DK Known About Parts at Creation Time Optimization Trajectories of Parts Known at Optimization Trajectories of Parts Known at

CreationCreation

Explosion Control Constraint Propagation NP CompleteConstraint Propagation NP Complete Simplify Constraint PropagationSimplify Constraint Propagation

MetaPrograms Exploit DS KnowledgeMetaPrograms Exploit DS Knowledge Global Constraints (MMX) Determine Cooperating, Global Constraints (MMX) Determine Cooperating,

Distributed MetaProgramsDistributed MetaPrograms Pre-Propagation of Constraints via TagsPre-Propagation of Constraints via Tags Pre-Plan Ordering Constraints on Distributed Pre-Plan Ordering Constraints on Distributed

MetaProgramsMetaPrograms Result: Tag-Directed TransformationsResult: Tag-Directed Transformations

Explosion Control Tag-Directed TransformationsTag-Directed Transformations MetaPrograms Tagged onto Reusable PartsMetaPrograms Tagged onto Reusable Parts

WHATWHAT Transformation Transformation (SplitLoopOnCases)(SplitLoopOnCases)

WHENWHEN to Fire to Fire (on (on EventNameEventName (SplitLoopOnCases)) (SplitLoopOnCases))

WHEREWHERE to Apply to Apply (if (if CaseTestCaseTest then SpecialCase then SpecialCase

else else GeneralCaseGeneralCase (Tags (on … (Tags (on …

(SplitLoopOnCases))))(SplitLoopOnCases))))

Example: SplitLoopOnCases

for(i=0, j=0; i<m && j<n; i++, j++)if(i==0 || j==0 || i==(m-1) ||

j==(n-1)) ...then case...; ...else case...;

for(j=0; j<n; j++)...then case with i=0...;for(j=0; j<n; j++)...then case with i=(m-1)...;for(i=0; i<m; i++)...then case with j=0...;for(i=0; i<m; i++)...then case with j=(n-1)...;for(i=1, j=1; i<(m-1) && j<(n-1); i++, j++)

...else case...;

Cooperating TD Transforms

Generate Weight MatricesGenerate Weight Matrices Create Loops, Unwrap, Partially EvaluateCreate Loops, Unwrap, Partially Evaluate

Split Loop+If into Loop CasesSplit Loop+If into Loop Cases Simplify Special Case LoopsSimplify Special Case Loops

Create Loops, Infer Control, Partially EvaluateCreate Loops, Infer Control, Partially Evaluate Reshape Image Loop BodyReshape Image Loop Body

Ordering Dependencies

Dependencies SupportedDependencies Supported Per Strategic Plan – (on PlanStep …)Per Strategic Plan – (on PlanStep …) Before & After – (on (completion Xform)…)Before & After – (on (completion Xform)…) On Local Action – (on substitutionofme …)On Local Action – (on substitutionofme …) On Signal – (on ?signalvbl …)On Signal – (on ?signalvbl …)

Explosion Control Summary Refinements – Implied DSL to DSL PhasesRefinements – Implied DSL to DSL Phases Specialized OptimizationsSpecialized Optimizations

Simplification & DS Opt. – PE & PD RulesSimplification & DS Opt. – PE & PD Rules Localization – Explicit Rule GroupingLocalization – Explicit Rule Grouping Architectural Shaping –Tag-Directed ControlArchitectural Shaping –Tag-Directed Control

Distributed MetaPrograms on Reusable PartsDistributed MetaPrograms on Reusable Parts Avoid General NP Complete ProblemAvoid General NP Complete Problem

Specialized Sub-Problems & Use DKSpecialized Sub-Problems & Use DK

References Katz & Volper, Constraint Propagation in Software Katz & Volper, Constraint Propagation in Software

Libraries of Transformation Systems, Libraries of Transformation Systems, IJSE&KE 2,3, IJSE&KE 2,3, 19921992..

Biggerstaff, Fixing Some Transformation Problems, Biggerstaff, Fixing Some Transformation Problems, Proc. Of Automated Software Engineering, 1999.Proc. Of Automated Software Engineering, 1999.

Biggerstaff, A New Control Structure for Biggerstaff, A New Control Structure for Transformation-Based GeneratorsTransformation-Based Generators, ICSR 2000ICSR 2000.

Neighbors, Draco: A Method for Engineering Neighbors, Draco: A Method for Engineering Reusable Software Systems, inReusable Software Systems, in Software Software Reusability, 1989. Reusability, 1989.



e.g., Templates, GenVoca, …e.g., Templates, GenVoca, …


ClassClass ElementsElements OperationsOperationsConcrete Reuse PL Struct. Concrete Reuse PL Struct. Hand Assem.Hand Assem.CompositionComposition Abstract PL Abstract PL InliningInlining

The scaling plane

Vertical(Programming

leverage)

Horizontal (Optimality of fit)

Feature variabilityOne-sizefits all

Customized

Small, lowpayoff

Large, highpayoff

Reu

se P

ayof

f

Reorganizing Generators

Benefits:Benefits: Inter-component optimizations (reweavings)Inter-component optimizations (reweavings) Small search space = reasonably fastSmall search space = reasonably fast

Shortcomings:Shortcomings: Technology immatureTechnology immature Unclear how far it can be pushedUnclear how far it can be pushed


e.g., Functions, OOP, Frameworks, DCOM, …e.g., Functions, OOP, Frameworks, DCOM, …


ClassClass ElementsElements OperationsOperationsConcrete Reuse PL Struct. Concrete Reuse PL Struct. Hand Assem.Hand Assem.

Niches in the scaling plane


leverage)


PatternDirected

ReorganizingSubsystem

LevelComponents

DerivedComponents

ConventionalComponents



leverage)


SubsystemLevel

Components

DerivedComponents

PatternDirected


Concrete Components Small components don’t work wellSmall components don’t work well

Assembly work per unit payoff is lowAssembly work per unit payoff is low Small components specialized niches OKSmall components specialized niches OK

Overhead masked by human response (e.g., UI)Overhead masked by human response (e.g., UI) Standards or domain narrowness mitigateStandards or domain narrowness mitigate

Big components work wellBig components work well Programs or subsystemsPrograms or subsystems High programming leverageHigh programming leverage Payoff dominates overhead costsPayoff dominates overhead costs

Example: UI Controls



leverage)


SubsystemLevel

Components


GenVoca derives a container

deque_usm = deq_sync [ deque2c [ dlist [ avail [ heap [ transient ]]]]];

ContainerClass

ElementClass

dlist

heap

deq-sync

deque2c

avail

transient

Refinements

List elements from heap

Deque

Synchronized deque

Container

Doubly linked list

Managed linked list

Transient elements

TypeEquation

Compositional Derivation Example domain: Data structuresExample domain: Data structures Benefits:Benefits:

Improve reuse by raising level of abstractionImprove reuse by raising level of abstraction Combinatorial amplification of library (horz.)Combinatorial amplification of library (horz.) Generation is fastGeneration is fast

Shortcomings:Shortcomings: Level of abstraction still lowLevel of abstraction still low Large grain components mitigate horz. scalingLarge grain components mitigate horz. scaling Dependencies limit extensibilityDependencies limit extensibility Inter-component optimization difficultInter-component optimization difficult



leverage)


SubsystemLevel

Components

DerivedComponents





e.g., Draco, CAPE, IP, DMS, ...e.g., Draco, CAPE, IP, DMS, ... Reorganizing generator systemsReorganizing generator systems

AO generator, AOP, ...AO generator, AOP, ...

Inference-driven generator systemsInference-driven generator systems Kids, Synapse, ...Kids, Synapse, ...

Inference-Based Generators

Paradigm: Schema + rules of inferenceParadigm: Schema + rules of inference Example: Divide-and-conquer schemaExample: Divide-and-conquer schema

Broad framework for solutionBroad framework for solution User specification: Formal specification User specification: Formal specification

(e.g., predicate calculus)(e.g., predicate calculus)

Inference-Based Generators Example: Kids generatorExample: Kids generator Benefits:Benefits:

Highest levels of horizontal scalingHighest levels of horizontal scaling Shortcomings:Shortcomings:

Formal specifications require ultra-stable Formal specifications require ultra-stable applicationapplication

Requires well-understood domain with deep theoryRequires well-understood domain with deep theory Immaturity and narrow => low vertical scaling Immaturity and narrow => low vertical scaling


ClassClass ElementsElements OperationsOperationsConcrete Reuse PL Struct. Concrete Reuse PL Struct. Hand Assem.Hand Assem.CompositionComposition Abstract PL Abstract PL InliningInliningPD GeneratorPD Generator DSL Struct. DSL Struct. PD XformsPD XformsReorg Generator Tagged DSLReorg Generator Tagged DSL PDX & PDX &

TDXTDXInfer GeneratorInfer Generator DSL+Logic DSL+Logic InferenceInference



leverage)


SubsystemLevel

Components

DerivedComponents

PatternDirected

Reorganizing

InferenceBasedConventional

Components


Limited islands of successful reuseLimited islands of successful reuse Compositionally Derived Components Compositionally Derived Components

More horizontal scaling but no inter-part reweavingMore horizontal scaling but no inter-part reweaving Pattern-directed transformation systemsPattern-directed transformation systems

Extends both but big search space for reweavingExtends both but big search space for reweaving Reorganizing generator systemsReorganizing generator systems

Reweavings gain horiz. scaling but immature Reweavings gain horiz. scaling but immature

Inference-driven generator systemsInference-driven generator systems Greatest horiz. scaling but formality limits useGreatest horiz. scaling but formality limits use


ClassClass ElementsElements OperationsOperationsConcrete Reuse PL Struct. Concrete Reuse PL Struct. Hand Assem.Hand Assem.CompositionComposition Abstract PL Abstract PL InliningInliningPD GeneratorPD Generator DSL Struct. DSL Struct. PD XformsPD XformsReorg Generator Tagged DSLReorg Generator Tagged DSL PDX & PDX &

TDXTDX



leverage)


SubsystemLevel

Components

DerivedComponents

PatternDirected

Reorganizing

InferenceBasedConventional

Components

Operators with Implicit ControlB=(A s)

Image A

B[i,j]=0

B[i,j]+

* s-1,-1

* s-1,0

* s-1,1

* s1,1

Non-Parallel Pseudo-Code

If (Off Edge)then t1 = 0else t1 = ( a[i-1, j-1]*(-1)+

a[i-1, j]*(-2) + a[i-1, j+1]*(-1)+ a[i+1, j-1]*1 + a[i+1, j]*2 + a[i+1, j+1]*1 ) ;

. . . code for t2 . . .

b[i,j] = sqrt((t1 * t1) + (t2 * t2));

Parallel Pseudo-Code

t1 = unpackadd(padd2(padd2(pmadd3(&(a[i-1,j-1]), &(s[-1, -1])),

pmadd3(&(a[i, j-1]), &(s[0, -1]))),

pmadd3(&(a[i+1,j-1]), &(s[ 1, -1])));

. . . code for t2 . . .

b[i,j] = sqrt((t1 * t1) + (t2 * t2));

AOG Features Schema language foundationSchema language foundation Pattern-driven & tag-driven transformsPattern-driven & tag-driven transforms

Organized by inheritance type and by stageOrganized by inheritance type and by stage Tags capture non-pattern-driven knowledgeTags capture non-pattern-driven knowledge Tags act like AST-localized interrupts for performing Tags act like AST-localized interrupts for performing

optimizationsoptimizations

TD’s are large-grain programmatic transformsTD’s are large-grain programmatic transforms Specialists: Partial evaluator & inference engineSpecialists: Partial evaluator & inference engine No explicit loopsNo explicit loops PerformancePerformance with small search spaceswith small search spaces

Generate Weight Matrices Create Loops, Unwrap, Partially EvaluateCreate Loops, Unwrap, Partially Evaluate dsdeclare dsarrayofint arr57391 dsdeclare dsarrayofint arr57391 [ -1, 1][ -1, 1] ((1 0 -1) (2 0 -2) (1 0 -1)) ;[ -1, 1][ -1, 1] ((1 0 -1) (2 0 -2) (1 0 -1)) ;

simplsimpl

{for (i = [0, (m – 1)], j =[0, (n – 1)], (i == 0)) {tmp1= 0; tmp2 = 0; b[i, j] = sqrt (( (tmp2 * tmp2) + (tmp1 *tmp1))) }}

{for (j =[0, (n – 1)] ) b[0, j] = 0}

Control Regimes

PD Search – Independent Local ChoicesPD Search – Independent Local Choices Strategically Blind – exploding search spaceStrategically Blind – exploding search space Biased Toward Local Syntactic StructureBiased Toward Local Syntactic Structure Rules are General & Allow Easy ExtensionRules are General & Allow Easy Extension

Results of Compositeleaf Xform


(leaf bwpixel48143 (tags (_q (_forall (bwpixel48143) (aref (a) idx48146 idx48149))) (itype bwpixel)))

BeforeBefore

AfterAfter

Internal PD Transform View

See Bindings onPrevious Chart

Documents

A Characterization of Reuse Technologies