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High-Performance Psychometrics: The Parallel-E Parallel-M Algorithm for Generalized Latent Variable Models

von Davier, Matthias
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
EM Algorithm, Computation, Psychometric Models, Precision of Estimation, Latent Variable Models, Multitrait Multimethod Techniques, Expectation-Maximization Algorithms, CPU


The overall gain (including parts of the program that cannot be executed in parallel) can reach a reduction in time by a factor of 6 or more for a 12-core machine. The basic approach is to utilize the architecture of modern CPUs, which often involves the design of processors with multiple cores that can run programs simultaneously. The use of this type of architecture for algorithms that produce posterior moments has straightforward appeal: The calculations conducted for each respondent or each distinct response pattern can be split up into simultaneous calculations.

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