High Performance Graph Data Imputation on Multiple GPUs

multiple imputation in survival models: applied on breast cancer data

High Performance Relevance Vector Machine on GPUs

The Relevance Vector Machine (RVM) algorithm has been widely utilized in many applications, such as machine learning, image pattern recognition, and compressed sensing. However, the RVM algorithm is computationally expensive. We seek to accelerate the RVM algorithm computation for time sensitive applications by utilizing massively parallel accelerators such as GPUs. In this paper, the computati...

Bitsliced High-Performance AES-ECB on GPUs

In order to perform high-performance Monte Carlo simulations of fracture in certain composite materials, we needed fast methods for generating deterministic random numbers. We made several design choices, and due to the fact that the entire simulation was to be done on both CPUs and GPUs, we designed new methods for fast implementation of the AES in the ECB mode on such architectures. This pape...

Multiple imputation and analysis for high‐dimensional incomplete proteomics data

Multivariable analysis of proteomics data using standard statistical models is hindered by the presence of incomplete data. We faced this issue in a nested case-control study of 135 incident cases of myocardial infarction and 135 pair-matched controls from the Framingham Heart Study Offspring cohort. Plasma protein markers (K = 861) were measured on the case-control pairs (N = 135), and the maj...

Multiple Imputation for Missing Data

Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...