  The heterogeneous computing paradigm has led to the need for portable and
efficient programming solutions that can leverage the capabilities of various
hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the
portability and performance of the SYCL and CUDA languages for one fundamental
bioinformatics application (Smith-Waterman protein database search) across
different GPU architectures, considering single and multi-GPU configurations
from different vendors. The experimental work showed that, while both CUDA and
SYCL versions achieve similar performance on NVIDIA devices, the latter
demonstrated remarkable code portability to other GPU architectures, such as
AMD and Intel. Furthermore, the architectural efficiency rates achieved on
these devices were superior in 3 of the 4 cases tested. This brief study
highlights the potential of SYCL as a viable solution for achieving both
performance and portability in the heterogeneous computing ecosystem.
