Claudio F. R. GeyerII - UFRGS

Inês de Castro DutraCOPPE - Sistemas - UFRJ

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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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parent(arthur,carol).parent(carol,john).

grandparent(X,Y) :- parent(X,Z), parent(Z,Y).

length([H|T],N) :- length(T,N1), N is N1+1.length([],0).

Introduction

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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) => author(A) & date(D),loc(P,A,D,L)

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Parallel Implementation: IAP Cross-product of solutions Recomputation

qsort([], []).qsort([P|T],L) :- partition(T,P,A,B), qsort(A,L1), qsort(B,L2), append(L1,[P|L2],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 > 0, N1 is N - 1, Out = [ferrari|Ms], producer(N1,Ms).producer(0,Out) :- Out = [].

consumer([ferrari|Ms]) :- 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 And+Or 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

ORP phase

#det goals = 0

#det goals <> 0

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Shared-memory And+Or 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 <seg#,offset>

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Performance

Andorra-I

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Performanceprog name Andorra-I JAM Aurora Musenrv400 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 5.915x4x3_puzzle 9.66 ---- 9.51 8.69warplan 1.20 ---- 2.63 1.06protein_all 6.81 ---- 9.49 8.64protein_1st 2.78 ---- 4.10 3.12fly_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