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：异构计算范式要求具备可移植且高效的编程解决方案，以便充分利用NVIDIA、Intel及AMD GPU等各类硬件设备的能力。本研究评估了SYCL和CUDA两种语言在一种基础生物信息学应用（Smith-Waterman蛋白质数据库搜索）中，跨不同GPU架构（涵盖不同厂商的单GPU与多GPU配置）的可移植性和性能。实验结果表明，虽然CUDA和SYCL版本在NVIDIA设备上性能相当，但后者在AMD和Intel等其他GPU架构上展现出显著代码可移植性。此外，在测试的4种情况中，SYCL在3种设备上取得了更优的架构效率。本项简要研究突显了SYCL在异构计算生态系统中同时实现性能与可移植性的潜力。