Fast and Highly Scalable Bayesian MDP on a GPU Platform Conference Paper uri icon

abstract

  • 2017 Copyright held by the owner/author(s). By employing the Optimal Bayesian Robust (OBR) policy, Bayesian Markov Decision Process (BMDP) can be used to solve the Gene Regulatory Network (GRN) control problem. However, due to the "curse of dimensionality", the data storage limitation hinders the practical applicability of the BMDP. To overcome this impediment, we propose a novel Duplex Sparse Storage (DSS) scheme in this paper, and develop a BMDP solver with the DSS scheme on a heterogeneous GPU-based platform. The simulation results demonstrate that our approach achieves a 5x reduction in memory utilization with a 2.4% "decision difference" and an average speedup of 4.1x compared to the full matrix based storage scheme. Additionally, we present the tradeoff between the runtime and result accuracy for our DSS techniques versus the full matrix approach. We also compare our results with the well known Compressed Sparse Row (CSR) approach for reducing memory utilization, and discuss the benefits of DSS over CSR.

name of conference

  • Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

published proceedings

  • ACM-BCB' 2017: PROCEEDINGS OF THE 8TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY,AND HEALTH INFORMATICS

author list (cited authors)

  • Zhou, H. e., Khatri, S. P., Hu, J., Liu, F., & Sze, C.

citation count

  • 3

complete list of authors

  • Zhou, He||Khatri, Sunil P||Hu, Jiang||Liu, Frank||Sze, Cliff

publication date

  • August 2017