The optimization software SDPA which has been developed by our group is a solver for symmetric cone programs. The symmetric cone program is a large scheme which includes linear programs, second-order cone programs and semidefmite programs. It has many applications covering various fields such as combinatorial optimization, systems and control theory, robust optimization and quantum chemistry. Primal-dual interior-point methods, which are polynomial-time algorithms, were proposed to solve symmetric cone programs. SDPA is based on the primal-dual interior-point method. In addition, SDPA utilizes sparsity of data in several ways and parallel computation to solve huge size problems efficiently. Using SDPA, we can obtain the solution of symmetric cone programs easily without knowing the details of the algorithm and its implementation techniques. This paper briefly explain the SDPA and its variants. Then outlines an algorithmic framework of the primal-dual interior-point method.