  Programming efficiently heterogeneous systems is a major challenge, due to
the complexity of their architectures. Intel oneAPI, a new and powerful
standards-based unified programming model, built on top of SYCL, addresses
these issues. In this paper, oneAPI is provided with co-execution strategies to
run the same kernel between different devices, enabling the exploitation of
static and dynamic policies. On top of that, static and dynamic load-balancing
algorithms are integrated and analyzed.
  This work evaluates the performance and energy efficiency for a well-known
set of regular and irregular HPC benchmarks, using an integrated GPU and CPU.
Experimental results show that co-execution is worthwhile when using dynamic
algorithms, improving efficiency even more when using unified shared memory.
