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Andorra-I. ACE. SBA. ParAKL. Penny. DAOS. 30. Andorra-I. Determinate and-parallelism. or ... Andorra-I 1047 214918 835 8496 5757 1517. Reduction in search space. 38 ...
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1. Claudio F. R. Geyer II - UFRGS Inês de Castro Dutra COPPE - Sistemas - UFRJ
2. Outline
Introduction Sequential Implementation Parallel Implementation Performance Conclusions Future Work
3. Introduction
Why logic programming? Formal basis expression power implicit parallelism suitability to some problems Main Language: Prolog syntax declarative and operational semantics
4. 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 Sintxe semantica unificacao backtrackingSintxe semantica unificacao backtracking
5. Sequential Implementation
Interpreters x Compilers WAM (WarrenAbstract Machine) structure copying environments choicepoints heap trail
6. Sequential Implementation
7. Parallel Implementation
Control Parallelism ORP: Or-parallelism ANDP: And-parallelism And + Or Data Parallelism Unification Path
8. Parallel Implementation: ORP
Problems representation of multiple bindings to the same variable Solutions stack sharing stack copying
9. Parallel Implementation: ORP
10. Parallel Implementation: ORP
Stack sharing binding arrays hash windows version vectors variable importation …
11. Parallel Implementation: ORP
Speculative work Prolog semantics? Side-effects and pruning Scheduling
12. Parallel Implementation: ANDP
IAP: Independent and-parallelism DAP: Dependent and-parallelism DetAP: Determinate and-parallelism
13. Parallel Implementation: IAP
Goals that do not share variables can proceed in parallel. Compiler support CGEs: Conditional Graph Expressions Example of iap, execution tree and programExample of iap, execution tree and program
14. 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)
15. 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).
16. 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
17. 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([]).
18. Parallel Implementation: DetAP
Goals that match at most one clause can be executed first and in parallel Compiler support Reduction of search space Tree and programTree and program
19. Parallel Platforms
Shared-memory Distributed memory Distributed-shared memory Implicit x Explicit Parallelism Programming Model Process or processor-based
20. Shared-memory Or-Parallel Systems
Aurora WAM-based processor-based shared stacks binding arrays
21. Aurora: Binding Arrays
22. Shared-memory Or-Parallel Systems
Scheduling in Aurora Wavefront Argonne Manchester Bristol Dharma
23. Shared-memory Or-Parallel Systems
Wavefront, Manchester and Argonne: topmost dispatching Bristol and Dharna: bottom-most dispatching speculative work
24. Shared-memory Or-Parallel Systems
Muse WAM-based processor-based stack copying
25. Muse: Stack Copying
Multiple environments maintained via stack-copying Memory space divided into identical address spaces to avoid pointer relocation Incremental copying Tree and example of MuseTree and example of Muse
26. 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
27. Shared-memory And-Parallel Systems
&-Prolog &ACE DASWAM
28. Shared-memory And-Parallel Systems
&-Prolog RAP-WAM CGEs compiler support &ACE based on &-Prolog DASWAM DAP and IAP, producer determined at runtime
29. Shared-memory And+Or Systems
Andorra-I ACE SBA ParAKL Penny DAOS
30. Andorra-I
Determinate and-parallelism or-parallelism side-effects, cuts and commits teams of workers scheduling reduction of search space
31. Andorra-I
DetAP phase ORP phase #det goals = 0 #det goals <> 0
32. Shared-memory And+Or Systems
ACE IAP + ORP Stack copying IAP a la &-Prolog Composition tree Last parallel call optimisation
33. ACE
34. 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>
35. Performance Andorra-I
36. 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 ---- ---- ----
37. 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
38. Performance: bt_cluster
39. Performance: chat-80
40. Performance: floorplan design
41. Applications
Optimisation Problems Databases Natural Language Processing Data Mining Constraint Satisfaction Problems ….
42. Conclusions
Logic programming: high level of abstraction Favours Implicit Parallelism Several applications Good performance on small to medium parallel architectures High performance is coming!
43. 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
44. Future Work
Ideal System
45. Perspectives
