Analyzing the performance portability of SYCL across CPUs, GPUs, and hybrid systems with SW sequence alignment

The high-performance computing (HPC) landscape is undergoing rapid transformation, with an increasing emphasis on energy-efficient and heterogeneous computing environments. This comprehensive study extends our previous research on SYCL’s performance portability by evaluating its effectiveness across a broader spectrum of computing architectures , including CPUs, GPUs , and hybrid CPU–GPU configurations from NVIDIA, Intel, and AMD. Our analysis covers single-GPU, multi-GPU, single-CPU, and CPU–GPU hybrid setups, using two common, bioinformatic applications as a case study . The results demonstrate SYCL’s versatility across different architectures, maintaining comparable performance to CUDA on NVIDIA GPUs while achieving similar architectural efficiency rates on AMD and Intel GPUs in the majority of cases tested. SYCL also demonstrated remarkable versatility and effectiveness across CPUs from various manufacturers, including the latest hybrid architectures from Intel. Although SYCL showed excellent functional portability in hybrid CPU–GPU configurations, performance varied significantly based on specific hardware combinations. Some performance limitations were identified in multi-GPU and CPU–GPU configurations, primarily attributed to workload distribution strategies rather than SYCL-specific constraints. These findings position SYCL as a promising unified programming model for heterogeneous computing environments, particularly for bioinformatic applications. • SYCL’s performance portability was analyzed on diverse, multiple-vendor architectures. • SYCL reached comparable performance to CUDA on NVIDIA GPUs. • SYCL achieved higher efficiency rates in most test cases from the other GPU vendors. • SYCL obtained similar architectural efficiency rates on most CPU configurations. • SYCL showed excellent functional portability in multi-GPU and CPU–GPU configurations. • SYCL stands as a promising unified programming model for heterogeneous systems.