The SDPA (Semidefinite Programming Algorithm) is a C{\small ++ }
implementation of a Mehrotra-type primal-dual predictor-corrector
interior-point method for solving the standard form semidefinite
program and its dual.  We report numerical results of large scale
problems to evaluate its performance, and investigate how major
time-consuming parts of the SDPA vary with the problem size, the
number of constraints and the sparsity of data matrices.