Claudio F. R. Geyer

1
Parallelism in Logic Programming

• Claudio F. R. Geyer
• II - UFRGS
• Inês de Castro Dutra
• COPPE - Sistemas - UFRJ

2
Outline

• Introduction
• Sequential Implementation
• Parallel Implementation
• Performance
• Conclusions
• Future Work

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Introduction

• Why logic programming?
• Formal basis
• expression power
• implicit parallelism
• suitability to some problems
• Main Language Prolog
• syntax
• declarative and operational semantics

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Introduction

• parent(arthur,carol).
• parent(carol,john).
• grandparent(X,Y) - parent(X,Z), parent(Z,Y).
• length(HT,N) - length(T,N1), N is N11.
• length(,0).

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Sequential Implementation

• Interpreters x Compilers
• WAM (WarrenAbstract Machine)
• structure copying
• environments
• choicepoints
• heap
• trail

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Sequential Implementation
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Parallel Implementation

• Control Parallelism
• ORP Or-parallelism
• ANDP And-parallelism
• And Or
• Data Parallelism
• Unification
• Path

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Parallel Implementation ORP

• Problems
• representation of multiple bindings to the same
  variable
• Solutions
• stack sharing
• stack copying

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Parallel Implementation ORP
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Parallel Implementation ORP

• Stack sharing
• binding arrays
• hash windows
• version vectors
• variable importation

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Parallel Implementation ORP

• Speculative work
• Prolog semantics?
• Side-effects and pruning
• Scheduling

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Parallel Implementation ANDP

• IAP Independent and-parallelism
• DAP Dependent and-parallelism
• DetAP Determinate and-parallelism

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Parallel Implementation IAP

• Goals that do not share variables can proceed in
  parallel.
• Compiler support
• CGEs Conditional Graph Expressions

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Parallel Implementation IAP
paper(P,A,D,L) - author(A), date(D),
loc(P,A,D,L).
Possible CGE indep(A) indep(D) gt author(A)
date(D),loc(P,A,D,L)
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Parallel Implementation IAP

• Cross-product of solutions
• Recomputation

qsort(, ). qsort(PT,L) -
partition(T,P,A,B),
qsort(A,L1), qsort(B,L2),
append(L1,PL2,L).
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Parallel Implementation DAP

• Goals that share variables can proceed in
  parallel
• Producer and consumer
• Chosen at compile-time or runtime
• one value or stream
• Compiler support

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Parallel Implementation DAP
producer(N,Out) - N gt 0, N1 is N - 1,
Out ferrariMs,
producer(N1,Ms). producer(
0,Out) - Out . consumer(ferrariMs) -
go-ride-ferrari,
consumer(Ms). consumer(
).
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Parallel Implementation DetAP

• Goals that match at most one clause can be
  executed first and in parallel
• Compiler support
• Reduction of search space

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Parallel Platforms

• Shared-memory
• Distributed memory
• Distributed-shared memory
• Implicit x Explicit Parallelism
• Programming Model
• Process or processor-based

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Shared-memory Or-Parallel Systems

• Aurora
• WAM-based
• processor-based
• shared stacks
• binding arrays

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Aurora Binding Arrays
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Shared-memory Or-Parallel Systems

• Scheduling in Aurora
• Wavefront
• Argonne
• Manchester
• Bristol
• Dharma

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Shared-memory Or-Parallel Systems

• Wavefront, Manchester and Argonne topmost
  dispatching
• Bristol and Dharna bottom-most dispatching
• speculative work

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Shared-memory Or-Parallel Systems

• Muse
• WAM-based
• processor-based
• stack copying

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Muse Stack Copying

• Multiple environments maintained via
  stack-copying
• Memory space divided into identical address
  spaces to avoid pointer relocation
• Incremental copying

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Shared-memory Or-Parallel Systems

• Scheduling in Muse
• Sophisticated operations to avoid data race
• workers keep data structures about idle and busy
  workers below their subtrees
• Shadowing
• Preference to leftmost work

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Shared-memory And-Parallel Systems

• -Prolog
• ACE
• DASWAM

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Shared-memory And-Parallel Systems

• -Prolog
• RAP-WAM
• CGEs
• compiler support
• ACE
• based on -Prolog
• DASWAM
• DAP and IAP, producer determined at runtime

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Shared-memory AndOr Systems

• Andorra-I
• ACE
• SBA
• ParAKL
• Penny
• DAOS

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Andorra-I

• Determinate and-parallelism
• or-parallelism
• side-effects, cuts and commits
• teams of workers
• scheduling
• reduction of search space

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Andorra-I
DetAP phase
det goals 0
ORP phase
det goals ltgt 0
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Shared-memory AndOr Systems

• ACE
• IAP ORP
• Stack copying
• IAP a la -Prolog
• Composition tree
• Last parallel call optimisation

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ACE
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SBA

• IAP ORP
• Stack sharing
• Shared Binding Arrays
• IAP a la ACE
• Binding array divided into fixed segment sizes
• Conditional variable bound to a pair
  ltseg,offsetgt

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PerformanceAndorra-I
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Performance
prog name Andorra-I JAM Aurora
Muse nrv400 8.25 8.37
---- ---- bt_cluster 9.37
9.70 ---- ---- bt_wms
3.32 ---- ----
---- road_markings 6.24 ----
---- ---- chat_80_db5 7.30
---- 7.30 5.91 5x4x3_puzzle 9.66
---- 9.51 8.69 warplan
1.20 ---- 2.63 1.06 protein_all
6.81 ---- 9.49
8.64 protein_1st 2.78 ----
4.10 3.12 fly_pan 6.88
---- ---- ---- scanner
5.47 ---- ---- ---- cipher
5.65 ---- ----
----
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Performance
Pgm map 8queen Xword 8queenp
zebra flypan Prolog 5003 383146
6377 133612 19404 10539 Andorra-I 1047
214918 835 8496 5757
1517 Reduction in search space
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Performance bt_cluster
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Performance chat-80
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Performance floorplan design
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Applications

• Optimisation Problems
• Databases
• Natural Language Processing
• Data Mining
• Constraint Satisfaction Problems
• .

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Conclusions

• Logic programming high level of abstraction
• Favours Implicit Parallelism
• Several applications
• Good performance on small to medium parallel
  architectures
• High performance is coming!

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Future Work

• More efficient methods to combine and or
  parallelism
• Scheduling is an important issue
• Sophisticated compiler support
• Memory management
• Parallel constraint logic programming
• Efficient cluster implementations
• Applications

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Future Work
Ideal System
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Perspectives