Solving Dynamic Programming Problems on a Computational Grid / Yongyang Cai, Kenneth L. Judd, Greg Thain, Stephen J. Wright.

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Cai, Yongyang

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w18714.Publication details: Cambridge, Mass. National Bureau of Economic Research 2013.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: We implement a dynamic programming algorithm on a computational grid consisting of loosely coupled processors, possibly including clusters and individual workstations. The grid changes dynamically during the computation, as processors enter and leave the pool of workstations. The algorithm is implemented using the Master-Worker library running on the HTCondor grid computing platform. We implement value function iteration for several large dynamic programming problems of two kinds: optimal growth problems and dynamic portfolio problems. We present examples that solve in hours on HTCondor but would take weeks if executed on a single workstation. The use of HTCondor can increase a researcher's computational productivity by at least two orders of magnitude.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w18714 (Browse shelf(Opens below))	Not For Loan

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January 2013.

We implement a dynamic programming algorithm on a computational grid consisting of loosely coupled processors, possibly including clusters and individual workstations. The grid changes dynamically during the computation, as processors enter and leave the pool of workstations. The algorithm is implemented using the Master-Worker library running on the HTCondor grid computing platform. We implement value function iteration for several large dynamic programming problems of two kinds: optimal growth problems and dynamic portfolio problems. We present examples that solve in hours on HTCondor but would take weeks if executed on a single workstation. The use of HTCondor can increase a researcher's computational productivity by at least two orders of magnitude.