With the advent of high-throughput sequencing, an efficient computing strategy is required to deal with large genomic data sets. The challenge of estimating a large precision matrix has garnered substantial research attention for its direct application to discriminant analyses and graphical models. Most existing methods either use a lasso-type penalty that may lead to biased estimators or are computationally intensive, which prevents their applications to very large graphs. We propose using an L 0 penalty to estimate an ultra-large precision matrix (scalnetL0). We apply scalnetL0 to RNA-seq data from breast cancer patients represented in The Cancer Genome Atlas and find improved accuracy of classifications for survival times. The estimated precision matrix provides information about a large-scale co-expression network in breast cancer. Simulation studies demonstrate that scalnetL0 provides more accurate and efficient estimators, yielding shorter CPU time and less Frobenius loss on sparse learning for large-scale precision matrix estimation. K E Y W O R D S L 0 penalty, genomics, network, scalable 1 INTRODUCTION With the advent of high-throughput sequencing, an efficient computing strategy is required to deal with larger and denser genomic data. The challenge of estimating a Abbreviations: scalnetL0, scalable network estimation with L 0 penalty; TCGA, The Cancer Genome Atlas. large precision matrix has garnered substantial research attention for its direct application to classification and discriminant analyses [12, 5, 10]. For example, we can predict whether individual patients with breast cancer will experience long or short survival times using their gene