DescriptionThe architectures of supercomputers are increasing in heterogeneity. It is important to maintain efficient code portability to take advantage of the computing capabilities of the 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 oneAPI by porting the dense linear algebra library Matrix Algebra on GPU and Multicore Architectures to Data Parallel C++, the direct programming language of oneAPI. Performance of the migrated code for GEMM is compared to MKL, OpenMP GEMM and native CUDA implementations on multicore CPUs and GPUs. The initial migrated code demonstrates impressive performance on multicore CPUs. It also retains the performance of CUDA on NVIDIA GPUs. It performs poorly on the Intel GPU but is improved through autotuning. Intel's oneAPI allowed for a successful extension of MAGMA portability to multicore CPUs and Intel GPUs.