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.

翻译：异构计算范式催生了对便携且高效编程解决方案的需求，这类方案需能充分利用NVIDIA、Intel及AMD GPU等各类硬件设备的能力。本研究针对不同GPU架构（涵盖单GPU与多GPU配置）评估了SYCL与CUDA语言在基础生物信息学应用（Smith-Waterman蛋白质数据库搜索）中的可移植性与性能。实验表明，尽管CUDA与SYCL版本在NVIDIA设备上性能相近，但后者展现出显著的优势——其代码可移植至AMD和Intel等其他GPU架构。此外，在4个测试案例中，SYCL有3个案例在架构效率指标上表现更优。本项简要研究凸显了SYCL作为异构计算生态系统中同时实现性能与可移植性的可行方案的潜力。