Accelerators such as NVIDIA GPUs and Intel MICs are currently provided as co-processor devices, usable only through a CPU host. For Intel MICs it is planned that this constraint will be lifted in the near future: CPU and accelerator(s) will then form a single, many-core, processor capable of peak performance of several Teraflops with high energy efficiency. In order to exploit the available computational power, the user will be compelled to write a code more “hardware-aware”, in contrast to the common philosophy of hiding hardware details as much as possible. The simple two-sided communication approach often used in message-passing applications introduces synchronization costs that may limit the performance on the next generation machines. PGAS languages, like coarray Fortran and UPC, propose a one-sided approach where a process accesses directly the remote memory of another process without interrupting its execution. In this paper, we propose a CUDA-aware coarray implementation, capable of merging the expressive syntax of coarrays with the computational power of GPUs. We propose a new keyword for the Fortran language, which allows the user to map with a high-level syntax some hardware features of the many-core machines. Our hybrid coarray implementation is based on OpenCoarrays, the coarray transport layer currently adopted by the GNU Fortran compiler.