Algorithm portfolios

Gomes, Carla P.; Selman, Bart

doi:10.1016/s0004-3702(00)00081-3

Cited by 366 publications

(246 citation statements)

References 8 publications

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“…Closest to our work, Huberman [9] designs a portfolio of search instruments for hard computational problems. This seminal work has led to the creation of a broad field in satisfiability and algorithm portfolio design [34]. Since then, extensive work has focused on improving the selection by and use of heuristics in the portfolio.…”

Section: Related Workmentioning

confidence: 99%

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

Deng

Verboon

Ren

et al. 2014

Job Scheduling Strategies for Parallel Processing

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Abstract. The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customerbases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, we focus in this work on portfolio schedulinghere, the dynamic selection and use of a scheduling policy, depending on the current system and workload conditions, from a portfolio of multiple policies. We design a periodic portfolio scheduler for the workload of the entire data center, and equip it with a portfolio of resource provisioning and allocation policies. Through simulation based on real and synthetic workload traces, we show evidence that portfolio scheduling can automatically select the scheduling policy to match both user and data center objectives, and that portfolio scheduling can perform well in the data center, relative to its constituent policies.

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Section: Related Workmentioning

confidence: 99%

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

Deng

Verboon

Ren

et al. 2014

Job Scheduling Strategies for Parallel Processing

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“…Another important contribution was the inception of algorithm portfolios [4,9,15]. Based on the observation that solvers have complementary strengths and thus exhibit incomparable behavior on different problem instances, the ideas of running multiple solvers in parallel or to select one solver based on the features of a given instance were introduced.…”

Section: Solver Portfoliosmentioning

confidence: 99%

Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction

Malitsky

Sabharwal²,

Samulowitz³

et al. 2013

Lecture Notes in Computer Science

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Abstract. Sequential algorithm portfolios for satisfiability testing (SAT), such as SATzilla and 3S, have enjoyed much success in the last decade. By leveraging the differing strengths of individual SAT solvers, portfolios employing older solvers have often fared as well or better than newly designed ones, in several categories of the annual SAT Competitions and Races. We propose two simple yet powerful techniques to further boost the performance of sequential portfolios, namely, a generic way of knowledge sharing suitable for sequential SAT solver schedules which is commonly employed in parallel SAT solvers, and a meta-level guardian classifier for judging whether to switch the main solver suggested by the portfolio with a recourse action solver. With these additions, we show that the performance of the sequential portfolio solver 3S, which dominated other sequential categories but was ranked 10th in the application category of the 2011 SAT Competition, can be boosted significantly, bringing it just one instance short of matching the performance of the winning application track solver, while still outperforming all other solvers submitted to the crafted and random categories.

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“…Another prevalent parallelization approach for CSP solvers uses an algorithm portfolio [15]. A portfolio-based method schedules a set of algorithms (its portf olio) on one or more processors, hoping to outperform any of its constituent algorithms [12,16].…”

Section: Background and Related Workmentioning

confidence: 99%

“…If a worker proves its subproblem P S unsatisfiable, however, it notifies the manager with message 0 (Algorithm 2, line 15). The manager replies with a new subproblem from Q (if any is waiting, Algorithm 3, lines [14][15]. Otherwise, the worker has exhausted its resources r and returns its subproblem to the manager with message 1 (Algorithm 2, line 16).…”

Section: The Spread Paradigmmentioning

confidence: 99%

A Hybrid Paradigm for Adaptive Parallel Search

Epstein

2012

Lecture Notes in Computer Science

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Abstract. Parallelization offers the opportunity to accelerate search on constraint satisfaction problems. To parallelize a sequential solver under a popular message passing protocol, the new paradigm described here combines portfolio-based methods and search space splitting. To split effectively and to balance processor workload, this paradigm adaptively exploits knowledge acquired during search and allocates additional resources to the most difficult parts of a problem. Extensive experiments in a parallel environment show that this paradigm significantly improves the performance of an underlying sequential solver, outperforms more naive approaches to parallelization, and solves many difficult problems left open after recent solver competitions.

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Algorithm portfolios

Cited by 366 publications

References 8 publications

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction

A Hybrid Paradigm for Adaptive Parallel Search

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