Event Title
Increasing the Performance of R Package ‘Penalized’ Through Integration of C++ with Rcpparmadillo
Location
Hager-Lubbers Exhibition Hall
Start Date
18-4-2017 3:30 PM
Description
BACKGROUND AND PURPOSE: Penalized is a R package which allows users to fit penalized regression models to high dimensional data. It can perform linear regression, logistic regression, Poisson regression, and Cox proportional hazards regression. Penalized allows for the application of ℓ1, ℓ2, or fused lasso penalties to each of these models. Due to the nature of the problems that Penalized is used for, performance is essential. The purpose of this project was to increase the performance of Penalized by rewriting portions of its code in a faster programming language while maintaining an interface with R. PROCEDURES: First, the code base was profiled using the package GUIProfiler. This was done to determine which sections of code should be converted. Next, these sections were rewritten in a faster programming language. C++ was the high-performance language chosen for this conversion. RcppArmadillo was used to provide an interface between the C++ and R code. OUTCOME: Speedups were obtained in the range of 1.3-2.2 depending on the function, parameters and input data. Models fitted with an ℓ2 penalty had the largest performance increase. Cox regression with an ℓ2 penalty performed on the nki70 dataset resulted in a median speedup of 2.05. IMPACT: Having more efficient software tools allows statisticians, data scientists, and computational biologists to work on larger datasets and develop pipelines with lower turnaround time.
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Increasing the Performance of R Package ‘Penalized’ Through Integration of C++ with Rcpparmadillo
Hager-Lubbers Exhibition Hall
BACKGROUND AND PURPOSE: Penalized is a R package which allows users to fit penalized regression models to high dimensional data. It can perform linear regression, logistic regression, Poisson regression, and Cox proportional hazards regression. Penalized allows for the application of ℓ1, ℓ2, or fused lasso penalties to each of these models. Due to the nature of the problems that Penalized is used for, performance is essential. The purpose of this project was to increase the performance of Penalized by rewriting portions of its code in a faster programming language while maintaining an interface with R. PROCEDURES: First, the code base was profiled using the package GUIProfiler. This was done to determine which sections of code should be converted. Next, these sections were rewritten in a faster programming language. C++ was the high-performance language chosen for this conversion. RcppArmadillo was used to provide an interface between the C++ and R code. OUTCOME: Speedups were obtained in the range of 1.3-2.2 depending on the function, parameters and input data. Models fitted with an ℓ2 penalty had the largest performance increase. Cox regression with an ℓ2 penalty performed on the nki70 dataset resulted in a median speedup of 2.05. IMPACT: Having more efficient software tools allows statisticians, data scientists, and computational biologists to work on larger datasets and develop pipelines with lower turnaround time.