Survival analysis has been a topic of active statistical research in the past few decades with applications spread across several areas. Traditional applications usually consider data with only a small numbers of predictors with a few hundreds or thousands of observations. Recent advances in data acquisition techniques and computation power have led to considerable interest in analyzing very‐high‐dimensional data where the number of predictor variables and the number of observations range between 104and 106. In this paper, we present a tool for performing large‐scale regularized parametric survival analysis using a variant of the cyclic coordinate descent method. Through our experiments on two real data sets, we show that application of regularized models to high‐dimensional data avoids overfitting and can provide improved predictive performance and calibration over corresponding low‐dimensional models. Copyright © 2013 John Wiley & Sons, Ltd.