Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with SW Sequence Alignment
Journal:
arXiv
Published Date:
Dec 11, 2024
Abstract
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.
