HiBGT: High-Performance Bayesian Group Testing for COVID-19
29th Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2022
; : 176-185, 2022.
Article
in English
| Scopus | ID: covidwho-2322398
ABSTRACT
The COVID-19 pandemic has necessitated disease surveillance using group testing. Novel Bayesian methods using lattice models were 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. Computationally, however, Bayesian group testing poses considerable challenges as computational complexity grows exponentially with sample size. HPC and big data stacks are needed for assessing computational and statistical performance across fluctuating prevalence levels at large scales. Here, we study how to design and optimize critical computational components of Bayesian group testing, including lattice model representation, test selection algorithms, and statistical analysis schemes, under the context of parallel computing. To realize this, we propose a high-performance Bayesian group testing framework named HiBGT, based on Apache Spark, which systematically explores the design space of Bayesian group testing and provides comprehensive heuristics on how to achieve high-performance, highly scalable Bayesian group testing. We show that HiBGT can perform large-scale test selections (> 250 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. © 2022 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
29th Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2022
Year:
2022
Document Type:
Article
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