RT Journal Article
T1 Exploring the performance and portability of the k-means algorithm on SYCL across CPU and GPU architectures
A1 García Sánchez, Carlos
A1 El Faqir El Rhazoui, Youssef
AB The aim of SYCL is to reduce the gap between the performance and code portability of the main accelerators used in HPC, such as multi-vendor CPUs, GPUs, and FPGAs. To evaluate SYCL’s performance portability, this paper uses the k-means algorithm as a case study. The k-means algorithm is simple to code but can be complex to optimize. In this research, we compare our developed SYCL version with the most efficient implementations of CUDA and OpenMP. Our resulting SYCL code can potentially run on multi-vendor CPUs and GPUs. Additionally, we have created a hand-tuned SYCL variation that is optimized for specific device architectures (CPU, NVIDIA GPU, and Intel GPU) to evaluate the performance difference between a standard version and an optimized one. The results show that SYCL outperforms Intel GPUs and CPUs compared to the state-of-the-art He-Vialle version, while on NVIDIA GPUs SYCL offers equivalent performance compared to its native CUDA implementation.
PB Springer Nature
YR 2023
FD 2023-05-15
LK https://hdl.handle.net/20.500.14352/104112
UL https://hdl.handle.net/20.500.14352/104112
LA eng
NO 2023 Acuerdos transformativos CRUE
DS Docta Complutense
RD 21 abr 2025