Scalable Algorithms for Adaptive Statistical Designs
We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clini...
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| Veröffentlicht in: | ACM/IEEE SC 2000 Conference (SC'00) S. 6 |
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2000
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| ISBN: | 9780780398023, 0780398025 |
| ISSN: | 1063-9535 |
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| Abstract | We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clinical trials. While adaptive designs have significant ethical and cost advantages, they are rarely utilized because of the complexity of optimizing and analyzing them. Computational challenges include massive memory requirements, few calculations per memory access, and multiply-nested loops with dynamic indices. We analyze the effects of various parallelization options, and while standard approaches do not work well, with effort an efficient, highly scalable program can be developed. This allows us to solve problems thousands of times more complex than those solved previously, which helps make adaptive designs practical. Further, our work applies to many other problems involving neighbor recurrences, such as generalized string matching. |
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| AbstractList | We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clinical trials. While adaptive designs have significant ethical and cost advantages, they are rarely utilized because of the complexity of optimizing and analyzing them. Computational challenges include massive memory requirements, few calculations per memory access, and multiply-nested loops with dynamic indices. We analyze the effects of various parallelization options, and while standard approaches do not work well, with effort an efficient, highly scalable program can be developed. This allows us to solve problems thousands of times more complex than those solved previously, which helps make adaptive designs practical. Further, our work applies to many other problems involving neighbor recurrences, such as generalized string matching. |
| Author | Oehmke, R. Stout, Q.F. Hardwick, J. |
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| Snippet | We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important... |
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| SubjectTerms | Algorithm design and analysis bandit models Clinical trials computational learning theory Cost function Design optimization dynamic domain decomposition Dynamic programming experimental algorithms load balancing Medical treatment memory-intensive computing message-passing Multidimensional systems Performance analysis sequential analysis Standards development Stochastic processes |
| Title | Scalable Algorithms for Adaptive Statistical Designs |
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