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High-performance and Scalable Bayesian Group Testing and Real-time fMRI Data Analysis

Chen, Weicong
http://orcid.org/0000-0003-0573-8808
http://rave.ohiolink.edu/etdc/view?acc_num=case1671110589494134

Abstract Details

2023, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
The COVID-19 pandemic has necessitated disease surveillance using group testing. Novel Bayesian methods using lattice models Ire proposed, which offer substantial improvements in group testing efficiency by precisely quantifying uncertainty in diagnoses, acknowledging varying individual risk and dilution effects, and guiding optimally convergent sequential pooled test selections using Bayesian Halving Algorithms. Computationally, however, Bayesian group testing poses considerable challenges as computational complexity grows exponentially with sample size. This can lead to shortcomings in reaching a desirable scale without practical limitations. To overcome these challenges, I propose a high-performance Bayesian group testing framework named HiBGT, which systematically explores the design space of Bayesian group testing and provides comprehensive heuristics on how to achieve high-performance Bayesian group testing. I show that HiBGT can perform large-scale test selections ($>2^{50}$ state iterations) and accelerate statistical analyzes up to 15.9x (up to 363x with little trade-offs) through a varied selection of sophisticated parallel computing techniques while achieving near linear scalability using up to 924 CPU cores. I further propose to scale HiBGT using a lightning fast and highly scalable framework, named SGBT. In particular, SBGT is up to 376x, 1733x, and 1523x faster than HiBGT in manipulating lattice models, performing test selections, and conducting statistical analyses, respectively, while achieving up to 97.9\% scaling efficiency up to 4096 CPU cores. I propose algorithms and workflows for next-generation real-time analysis of fMRI data and dynamically adjustment of experiment stimuli through early stopping. To overcome significant computational challenges raised in this setting, I design a \underline{S}calable, \underline{P}arallel, and \underline{R}eal-\underline{T}ime \underline{S}equential \underline{P}robability \underline{R}atio \underline{T}est ($\text{SPRT}^2$) toolkit. Our experiments have demonstrated that $\text{SPRT}^2$ is over 200x faster than traditional offline approaches and can achieve a strong scaling of up to 85.6\%. In 23 real-subject experiments, $\text{SPRT}^2$ can successfully perform real-time fMRI analysis (< 1s per scan) of the whole brain using less than 180 CPU cores, helping achieve dynamic stopping of stimulus administration with typical time saving of up to 33\%. I also show that $\text{SPRT}^2$ is readily available for next-gen big-data-oriented rtfMRI analysis by retaining real-time analysis on 8x larger datasets with near-linear data scaling up to 256x using over 1,000 CPU cores.
Curtis Tatsuoka (Advisor)
Vipin Chaudhary (Committee Chair)
Xiaoyi Lu (Committee Member)
Vincenzo Liberatore (Committee Member)
Computer Science
HPC, Big Data, Bayesian Methods, Combinatorial Optimization, Group Testing, Real-time fMRI

Recommended Citations

Citations

  • Chen, W. (2023). High-performance and Scalable Bayesian Group Testing and Real-time fMRI Data Analysis [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1671110589494134

    APA Style (7th edition)

  • Chen, Weicong. High-performance and Scalable Bayesian Group Testing and Real-time fMRI Data Analysis. 2023. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1671110589494134.

    MLA Style (8th edition)

  • Chen, Weicong. "High-performance and Scalable Bayesian Group Testing and Real-time fMRI Data Analysis." Doctoral dissertation, Case Western Reserve University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=case1671110589494134

    Chicago Manual of Style (17th edition)

case1671110589494134
137
© 2022, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.
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