Recent development in Graphic Processing Units (GPUs) has opened a new challenge in harnessing their computing power as a new general-purpose computing paradigm with its CUDA parallel programming. However, porting applications to CUDA remains a challenge to average programmers. We have developed a restructuring software compiler (RT-CUDA) with best possible kernel optimizations to bridge the gap between high-level languages and the machine dependent CUDA environment. RT-CUDA is based upon a set of compiler optimizations. RT-CUDA takes a C-like program and convert it into an optimized CUDA kernel with user directives in a con.figuration .file for guiding the compiler. While the invocation of external libraries is not possible with OpenACC commercial compiler, RT-CUDA allows transparent invocation of the most optimized external math libraries like cuSparse and cuBLAS. For this, RT-CUDA uses interfacing APIs, error handling interpretation, and user transparent programming. This enables efficient design of linear algebra solvers (LAS). Evaluation of RT-CUDA has been performed on Tesla K20c GPU with a variety of basic linear algebra operators (M+, MM, MV, VV, etc.) as well as the programming of solvers of systems of linear equations like Jacobi and Conjugate Gradient. We obtained significant speedup over other compilers like OpenACC and GPGPU compilers. RT-CUDA facilitates the design of efficient parallel software for developing parallel simulators (reservoir simulators, molecular dynamics, etc.) which are critical for Oil & Gas industry. We expect RT-CUDA to be needed by many industries dealing with science and engineering simulation on massively parallel computers like NVIDIA GPUs.