On efficient parallel computations for some dynamic programming problems
Theoretical Computer Science
Dynamic programming on two-dimensional systolic arrays
Information Processing Letters
String editing on an SIMD hypercube multicomputer
Journal of Parallel and Distributed Computing
On efficient parallel algorithms for solving set recurrence equations
Journal of Algorithms
Load balancing techniques for dynamic programming algorithms on hypercube multiprocessors
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Asynchronous Analysis of Parallel Dynamic Programming Algorithms
IEEE Transactions on Parallel and Distributed Systems
Using Path Induction to Evaluate Sequential Allocation Procedures
SIAM Journal on Scientific Computing
A program for sequential allocation of Bernoulli populations
Computational Statistics & Data Analysis - Special issue on parallel processing and statistics
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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 pratical. Further, our work applies to many other problems involing neighbor recurrences, uch as generalized string matching.