Computational codes in physics and engineering often use implicit solution algorithms that require linear algebra tools such as Ax=b solvers, eigenvalue, optimization, or graph algorithms. Developers face major challenges in selecting linear algebra tools that can support their algorithms, numerical schemes, meshes, and computing hardware and to minimize the time, space and complexity. The existing libraries such as PETSc or LAPACK are "stretched" to the limits by new generation application codes which create big, unsymmetric, often dense, and poorly conditioned matrices. One of the obstacles in effective utilization of linear algebra libraries is lack of benchmark quality representative test cases and benchmarking toolkits for these types of problems. This project will develop, demonstrate and deliver a comprehensive numerical test suite for benchmark evaluation of linear algebra solvers for computational application software on High Performance Computers. Unlike existing benchmarks on static, Ax=b matrix, problems CFDRC-TACC team proposes new generation of dynamic, discipline specific and multidisciplinary functional benchmarks accounting for sparse/dense and unsymmetric matrices using web accessible benchmark matrix/problem generators. Our team has excellent expertise and tools (multiphysics solvers, sparse/dense solver libraries, benchmark cases, related projects, and understanding of NASA engineering/scientific challenges) as well as HPC resources to achieve this goal.