Heterogeneous and multi-device nodes are widely used in high-performance computing and data centers. However, current programming models do not provide simple, transparent, and portable support for automatically targeting heterogeneous nodes. In this paper, we present SEER, a new C++ library that provides a descriptive programming model to enable applications to benefit from heterogeneous nodes in a transparent and portable way across multiple device types. SEER provides efficient memory management and can select the proper device[s] depending on the computational cost of the applications. All this is completely transparent to the programmer, thereby providing a highly productive programming environment. We evaluate the SEER library on two heterogeneous nodes of Summit (#5 TOP500) and Crusher supercomputers. Notably, the smaller-scale Crusher test-bed machine uses identical hardware and software as ORNL’s Frontier (#1 TOP500). This work also includes a detailed performance study conducted with a set of representative test cases in high-performance computing (e.g., Basic Linear Algebra Subprograms (BLAS), Tridiagonal Solve, and Conjugate Gradient). SEER provides high accelerations of up to \(30\times \) for sparse matrix and \(8\times \) for batch BLAS applications thanks to automatic and transparent device selection and multi-device exploitation respectively.