As the architectures of super-computing systems are continually changing, it is important to maintain efficient code portability in order to continue to take advantage of the computing capabilities of the diverse and evolving hardware in these systems. Intel has adopted an open standard programming interface for heterogeneous systems called oneAPI, designed to allow code portability across different processor architectures. This paper evaluates Intel’s oneAPI by migrating a general matrixmatrix multiplication (GEMM) CUDA algorithm from the dense linear algebra library Matrix Algebra on GPU and Multicore Architectures (MAGMA) to Data Parallel C++ (DPC++), the direct programming language of oneAPI. (…)