parallel backtracking with answer memoing for independent
play

Parallel Backtracking with Answer Memoing for Independent - PowerPoint PPT Presentation

Dec 13, 2023 •147 likes •504 views

Parallel Backtracking with Answer Memoing for Independent And-Parallelism Pablo Chico de Guzman 1 Amadeo Casas 2 Manuel Carro 1 , 3 Manuel V. Hermenegildo 1 , 3 1 School of Computer Science, Technical University of Madrid, Spain 2 Samsung Research,

  1. Parallel Backtracking with Answer Memoing for Independent And-Parallelism Pablo Chico de Guzman 1 Amadeo Casas 2 Manuel Carro 1 , 3 Manuel V. Hermenegildo 1 , 3 1 School of Computer Science, Technical University of Madrid, Spain 2 Samsung Research, USA 3 IMDEA Software Institute, Spain ICLP 2011 Lexington, KY – July 7, 2011 Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 1 / 16

  2. Introduction and Motivation Introduction Two main sources of parallelism in LP: ◮ OR-Parallelism: Aurora, MUSE, etc. ◮ AND-Parallelism: &-Prolog, DDAS, etc. ◮ Both: (&)ACE, AKL, Andorra-I, EAM, etc. Classical approach in Independent AND-Parallelism (IAP): ◮ Conery approach: ⋆ Copying goals overhead. ⋆ Nonterminating programs. ◮ Recomputation + sequential backtracking. Saves memory and preserves sequential semantics, but... ⋆ Recomputation can be inefficient. ⋆ Sequential backtracking limits parallelism. Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 2 / 16

  3. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  4. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X) , g2(Y). g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 1 ���� Time main 1 = 2 Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  5. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 1 ���� ���� Time main 1 = 2 + (2) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  6. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 1 � �� � ���� Time main 1 = 2 + (2 + 5) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  7. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 1 � �� � ���� Time main 1 = 2 + (2 + 5 + 5) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  8. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X) , g2(Y). g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 1 g 1 � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  9. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y) . g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � ���� ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  10. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y) . g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  11. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y) . g1(X) & g2(Y). g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  12. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y) . g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. g 1& g 2 � �� � Time main 2 = max (2 , 2) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  13. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y) . g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. g 1& g 2 g 2 � �� � ���� Time main 2 = max (2 , 2) + (5) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  14. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y) . g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. g 1& g 2 g 2 � �� � � �� � Time main 2 = max (2 , 2) + (5 + 5) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  15. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y) . g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. g 1& g 2 g 2 g 1& g 2 � �� � � �� � � �� � Time main 2 = max (2 , 2) + (5 + 5) + max (10 , 2) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  16. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y) . g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. g 1& g 2 g 2 g 1& g 2 g 2 � �� � � �� � � �� � ���� Time main 2 = max (2 , 2) + (5 + 5) + max (10 , 2) + (5) Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

  17. Introduction and Motivation Classical approach in IAP Example (Traditional IAP execution - main1 vs. main2) main1(X, Y) :- main2(X, Y) :- g1(X), g2(Y). g1(X) & g2(Y) . g1(X) :- X=1, work(2). g2(Y) :- Y=1, work(2). g1(X) :- X=2, work(10). g2(Y) :- Y=2, work(5). g2(Y) :- Y=3, work(5). g 2 g 2 g 1 g 1 � �� � � �� � ���� ���� Time main 1 = 2 + (2 + 5 + 5) + 10 + (2 + 5 + 5) = 36 secs. g 1& g 2 g 2 g 1& g 2 g 2 � �� � � �� � � �� � � �� � Time main 2 = max (2 , 2) + (5 + 5) + max (10 , 2) + (5 + 5) = 32 secs. Speedup = 1.12 . Can we do better? Pablo Chico de Guzman et al (UPM) Parallel Backtracking with Answer. . . ICLP 2011 - Lexington, KY 3 / 16

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

In SVN: explain the concept of backtracking solve the n-queens problem using backtracking Qu

In SVN: explain the concept of backtracking solve the n-queens problem using backtracking Qu

Exhaustive search, backtracking, object-oriented Queens After today, you should be able to In SVN: explain the concept of backtracking solve the n-queens problem using backtracking Qu Queens ens A taste of artificial intelligence

637 views • 19 slides
Backtracking And Branch And Bound Subset & Permutation Problems Subset problem of size n.

Backtracking And Branch And Bound Subset & Permutation Problems Subset problem of size n.

Backtracking And Branch And Bound Subset & Permutation Problems Subset problem of size n. Nonsystematic search of the space for the answer takes O(p2 n ) time, where p is the time needed to evaluate each member of the solution space.

65 views • 5 slides
Backtracking And Branch And Bound Subset & Permutation Problems Subset problem of size n.

Backtracking And Branch And Bound Subset & Permutation Problems Subset problem of size n.

Backtracking And Branch And Bound Subset & Permutation Problems Subset problem of size n. Nonsystematic search of the space for the answer takes O(p2 n ) time, where p is the time needed to evaluate each member of the solution space.

310 views • 4 slides
24.1 CSP Algorithms 22.23. Introduction 24.26. Basic Algorithms 24. Backtracking

24.1 CSP Algorithms 22.23. Introduction 24.26. Basic Algorithms 24. Backtracking

Foundations of Artificial Intelligence April 8, 2020 24. Constraint Satisfaction Problems: Backtracking Foundations of Artificial Intelligence 24.1 CSP Algorithms 24. Constraint Satisfaction Problems: Backtracking 24.2 Naive Backtracking

260 views • 6 slides
Embarrassingly Parallel Computations Embarrassingly Parallel Computations A computation that

Embarrassingly Parallel Computations Embarrassingly Parallel Computations A computation that

Embarrassingly Parallel Computations Embarrassingly Parallel Computations A computation that can be divided into completely independent parts, each of which can be executed on a separate process(or) is called embarrassingly parallel. An

727 views • 58 slides
CS 758/858: Algorithms http://www.cs.unh.edu/~ruml/cs758 Backtracking Local Search Wheeler Ruml

CS 758/858: Algorithms http://www.cs.unh.edu/~ruml/cs758 Backtracking Local Search Wheeler Ruml

CS 758/858: Algorithms http://www.cs.unh.edu/~ruml/cs758 Backtracking Local Search Wheeler Ruml (UNH) Class 25, CS 758 1 / 20 Backtracking Hardness Optimization Backtracking Depth-first Search DFS Order Problems

466 views • 20 slides
Parallel Answer Set Programming Agostino Dovier 1 Andrea Formisano 2 Enrico Pontelli 3 1.

Parallel Answer Set Programming Agostino Dovier 1 Andrea Formisano 2 Enrico Pontelli 3 1.

Parallel Answer Set Programming Agostino Dovier 1 Andrea Formisano 2 Enrico Pontelli 3 1. Universit di Udine 2. Universit di Perugia 3. New Mexico State University PCR17 @ CADE17 Gothenburg, August 2017 Material from Handbook of

704 views • 45 slides
Exhaustive Generation: Backtracking and Branch-and-bound Lucia Moura Fall 2013 Exhaustive

Exhaustive Generation: Backtracking and Branch-and-bound Lucia Moura Fall 2013 Exhaustive

Backtracking Intro Generating all cliques Estimating tree size Exact Cover Bounding Branch-and-Bound Exhaustive Generation: Backtracking and Branch-and-bound Lucia Moura Fall 2013 Exhaustive Generation: Backtracking and Branch-and-bound

719 views • 50 slides
Three-way Parallel Independent Component Analysis for Imaging Genetics Using Multi-Objective

Three-way Parallel Independent Component Analysis for Imaging Genetics Using Multi-Objective

Three-way Parallel Independent Component Analysis for Imaging Genetics Using Multi-Objective Optimization & Alvaro Ulloa, Jingyu Liu, Victor Vergara, Jiayu Chen, Vince Calhoun, and Marios Pattichis August 30, 2014 3pICA for Imaging

166 views • 14 slides
Foundations of Artificial Intelligence 24. Constraint Satisfaction Problems: Backtracking Malte

Foundations of Artificial Intelligence 24. Constraint Satisfaction Problems: Backtracking Malte

Foundations of Artificial Intelligence 24. Constraint Satisfaction Problems: Backtracking Malte Helmert and Thomas Keller University of Basel April 8, 2020 CSP Algorithms Naive Backtracking Variable and Value Orders Summary Constraint

1.13k views • 37 slides
Factors that Determine Speedup Characteristics of parallel code ECE 1747 Parallel Programming

Factors that Determine Speedup Characteristics of parallel code ECE 1747 Parallel Programming

Factors that Determine Speedup Characteristics of parallel code ECE 1747 Parallel Programming granularity load balance locality Machine-independent communication and synchronization Performance Optimization Techniques

745 views • 11 slides
ffi wonder whether t. Preventi ng backtracki ng To find out about assume the user a Prolog will

ffi wonder whether t. Preventi ng backtracki ng To find out about assume the user a Prolog will

State of alert Y normal aler 0 J Figure 5.1 State of lling Backtracking ro as follows: f( X, normal) :- 5.1 Preventing backtracking 126 5.2 Examples of using cut 131 f( X, alertl) :- 3 5.3 Negation as failure 135 f( X, alert2) :- 6 5.4

552 views • 31 slides
Trying to run EvtGen in parallel provided some usefull information S. Longo 2nd SuperB

Trying to run EvtGen in parallel provided some usefull information S. Longo 2nd SuperB

Wishing to reuse part of the code written by the BaBar collaboration, there are two main questions that require an answer: Is it possible to run in parallel BaBar code (legacy code)? If this is the case, what kind of performances can be

847 views • 15 slides
Tests, Facts and Backtracking Contents Tests Returning results Facts More on

Tests, Facts and Backtracking Contents Tests Returning results Facts More on

Lecture 2 Tests, Facts and Backtracking Contents Tests Returning results Facts More on backtracking The story so far... A Prolog program may succeed or fail . Prolog programs consist of predicate definitions Predicate

576 views • 19 slides
A Platform-Independent Tool for Modeling Parallel Programs ACM Southeast 2011 Kennesaw State

A Platform-Independent Tool for Modeling Parallel Programs ACM Southeast 2011 Kennesaw State

A Platform-Independent Tool for Modeling Parallel Programs ACM Southeast 2011 Kennesaw State University March 2011 Ferosh Jacob, Jeff Gray, Yu Sun, Purushotham Bangalore Department of Computer Science University of Alabama

169 views • 15 slides
CS 310 Advanced Data Structures and Algorithms Backtracking July 2, 2018 Mohammad Hadian

CS 310 Advanced Data Structures and Algorithms Backtracking July 2, 2018 Mohammad Hadian

CS 310 Advanced Data Structures and Algorithms Backtracking July 2, 2018 Mohammad Hadian Advanced Data Structures and Algorithms July 2, 2018 1 / 15 Backtracking Wikipedia: backtracking is a general algorithm for finding all (or some)

308 views • 15 slides
The physics The physics of AGN jets of AGN jets from 3D simulations from 3D simulations

The physics The physics of AGN jets of AGN jets from 3D simulations from 3D simulations

The physics The physics of AGN jets of AGN jets from 3D simulations from 3D simulations Salvatore Cielo Salvatore Cielo with: with: J. Silk (IAP|JHU) J. Silk (IAP|JHU) M. Volonteri (IAP) M. Volonteri (IAP) V. Antonuccio-Delogu (INAF)

624 views • 19 slides
Indirect dark matter detection in light of high-resolution N-body simulations Julien Lavalle

Indirect dark matter detection in light of high-resolution N-body simulations Julien Lavalle

Indirect dark matter detection in light of high-resolution N-body simulations Julien Lavalle Dept. of Theoretical Physics, Turin Univ. & INFN Main ref: Lidia Pieri, JL, Gianfranco Bertone & Enzo Branchini arXiv:0908.0195 TeV Particle

181 views • 17 slides
Astrophysical uncertainties in direct detection experiments Bradley J Kavanagh IPhT-Saclay

Astrophysical uncertainties in direct detection experiments Bradley J Kavanagh IPhT-Saclay

Astrophysical uncertainties in direct detection experiments Bradley J Kavanagh IPhT-Saclay Based on work with A M Green and M Fornasa: arXiv:1303.6868, arXiv:1312.1852, arXiv:1410.8051 NewDark Bradley Kavanagh - ICAP@IAP 09/01/2015 Outline

332 views • 20 slides
Decadal Predic+ons by LASG/IAP CGCM FGOALS-s2 Bo Wu*, Tianjun Zhou

Decadal Predic+ons by LASG/IAP CGCM FGOALS-s2 Bo Wu*, Tianjun Zhou

CLIVAR-ICTP Workshop on Decadal Climate Variability and Predictability: Challenge and Opportunity Decadal Predic+ons by LASG/IAP CGCM FGOALS-s2 Bo Wu*, Tianjun Zhou

424 views • 15 slides
Random Matrix Theory and applications in cosmology Sbastien Renaux-Petel CNRS - Institut

Random Matrix Theory and applications in cosmology Sbastien Renaux-Petel CNRS - Institut

Random Matrix Theory and applications in cosmology Sbastien Renaux-Petel CNRS - Institut dAstrophysique de Paris IESC Cargse, 5th and 6th of September 2018 School Analytics, Inference, and Computation in Cosmology: Advanced methods

655 views • 48 slides
Designing In-App-Purchase Without Losing Your Soul Shane Neville - Systems and Level Design

Designing In-App-Purchase Without Losing Your Soul Shane Neville - Systems and Level Design

Designing In-App-Purchase Without Losing Your Soul Shane Neville - Systems and Level Design @shaneneville shane@nrdland.com Saturday, 30 March, 13 Battleheart Saturday, 30 March, 13 Saturday, 30 March, 13 Saturday, 30 March, 13 Saturday,

689 views • 27 slides
SmartImpact Meeting DUBLIN 2016 06 16/17 Implementation - Kick-Off Meeting Partner City

SmartImpact Meeting DUBLIN 2016 06 16/17 Implementation - Kick-Off Meeting Partner City

SmartImpact Meeting DUBLIN 2016 06 16/17 Implementation - Kick-Off Meeting Partner City URBACT Local Group and Integrated Action Plans Integrated Action Plans & Urbact Local Groups (ULGs) Prior to the meeting partners:

266 views • 25 slides
from the Instant Games team Chris Hawkins Partner Engineer, Facebook Quick Intro to Instant

from the Instant Games team Chris Hawkins Partner Engineer, Facebook Quick Intro to Instant

Perspective on Web Game Tech from the Instant Games team Chris Hawkins Partner Engineer, Facebook Quick Intro to Instant Games Game Facebook JS BRIDGE WEBVIEW NATIVE Game logic Authentication Graphics Ads Audio IAP

188 views • 5 slides

More recommend