Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with SW Sequence Alignment

TL;DR

This study evaluates SYCL's performance portability across diverse CPUs, GPUs, and hybrid systems, demonstrating its versatility and identifying performance variations in heterogeneous configurations for bioinformatics applications.

Contribution

It extends previous research by analyzing SYCL's effectiveness on a broader range of architectures, including hybrid CPU-GPU systems from multiple vendors, with detailed performance insights.

Findings

SYCL performs comparably to CUDA on NVIDIA GPUs.

SYCL achieves similar efficiency on AMD and Intel GPUs.

Performance varies significantly in multi-GPU and CPU-GPU setups.

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.