Bandit-based optimization on graphs with application to library performance tuning

Mesmay, Frédéric de; Rimmel, Arpad; Voronenko, Yevgen; Püschel, Markus

doi:10.1145/1553374.1553468

Cited by 39 publications

(33 citation statements)

References 19 publications

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“…It achieved particularly good results in two-player games like computer Go [12] or Havannah [15]. Moreover, it was also successfully applied on one-player problems like the automatic generation of libraries for linear transforms [7], non-linear optimization [2] or active learning [14].…”

Section: Introductionmentioning

confidence: 99%

Biasing Monte-Carlo Simulations through RAVE Values

Rimmel

Teytaud

2011

Computers and Games

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Abstract. The Monte-Carlo Tree Search algorithm has been successfully applied in various domains. However, its performance heavily depends on the Monte-Carlo part. In this paper, we propose a generic way of improving the Monte-Carlo simulations by using RAVE values, which already strongly improved the tree part of the algorithm. We prove the generality and efficiency of our approach by showing improvements on two different applications: the game of Havannah and the game of Go.

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Section: Introductionmentioning

confidence: 99%

Biasing Monte-Carlo Simulations through RAVE Values

Rimmel

Teytaud

2011

Computers and Games

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“…In order to find the best possible low-level expressions for a given target device, we have developed a simple automatic search strategy based loosely on Bandit-based optimization [17]. Our current search strategy is rather basic and just designed to prove that it is possible to find good implementations automatically.…”

Section: Automatic Searchmentioning

confidence: 99%

Generating performance portable code using rewrite rules: from high-level functional expressions to high-performance OpenCL code

Steuwer

Fensch

Lindley

et al. 2015

Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming

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Computers have become increasingly complex with the emergence of heterogeneous hardware combining multicore CPUs and GPUs. These parallel systems exhibit tremendous computational power at the cost of increased programming effort resulting in a tension between performance and code portability. Typically, code is either tuned in a low-level imperative language using hardware-specific optimizations to achieve maximum performance or is written in a high-level, possibly functional, language to achieve portability at the expense of performance.We propose a novel approach aiming to combine high-level programming, code portability, and high-performance. Starting from a high-level functional expression we apply a simple set of rewrite rules to transform it into a low-level functional representation, close to the OpenCL programming model, from which OpenCL code is generated. Our rewrite rules define a space of possible implementations which we automatically explore to generate hardware-specific OpenCL implementations. We formalize our system with a core dependently-typed λ-calculus along with a denotational semantics which we use to prove the correctness of the rewrite rules.We test our design in practice by implementing a compiler which generates high performance imperative OpenCL code. Our experiments show that we can automatically derive hardwarespecific implementations from simple functional high-level algorithmic expressions offering performance on a par with highly tuned code for multicore CPUs and GPUs written by experts.

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“…NMC can be described in a very natural way by our grammar. The basic search level of NMC simply performs a random simulation (10) Level of NMC relies on level in the following way: (11) • Single-player MCTS [8]- [10] selects actions one after the other. In order to select one action, it relies on combined with random simulations.…”

Section: Description Of Previously Proposed Algorithmsmentioning

confidence: 99%

Monte Carlo Search Algorithm Discovery for Single-Player Games

Maes

St-Pierre

Ernst

2013

IEEE Trans. Comput. Intell. AI Games

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Abstract-Much current research in AI and games is being devoted to Monte Carlo search (MCS) algorithms. While the quest for a single unified MCS algorithm that would perform well on all problems is of major interest for AI, practitioners often know in advance the problem they want to solve, and spend plenty of time exploiting this knowledge to customize their MCS algorithm in a problem-driven way. We propose an MCS algorithm discovery scheme to perform this in an automatic and reproducible way. First, we introduce a grammar over MCS algorithms that enables inducing a rich space of candidate algorithms. Afterwards, we search in this space for the algorithm that performs best on average for a given distribution of training problems. We rely on multiarmed bandits to approximately solve this optimization problem. The experiments, generated on three different domains, show that our approach enables discovering algorithms that outperform several well-known MCS algorithms such as upper confidence bounds applied to trees and nested Monte Carlo search. We also show that the discovered algorithms are generally quite robust with respect to changes in the distribution over the training problems.

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Bandit-based optimization on graphs with application to library performance tuning

Cited by 39 publications

References 19 publications

Biasing Monte-Carlo Simulations through RAVE Values

Biasing Monte-Carlo Simulations through RAVE Values

Generating performance portable code using rewrite rules: from high-level functional expressions to high-performance OpenCL code

Monte Carlo Search Algorithm Discovery for Single-Player Games

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