Portability and Performance Assessment of the Non-Negative Matrix Factorization Algorithm with OpenMP and SYCL

Youssef Faqir-Rhazoui, Carlos García, F. Tirado
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Abstract

The SYCL standard was released to improve code portability across heterogeneous environments. Intel released the oneAPI toolkit, which includes the Data-Parallel C++ (DPC++) compiler which is the Intel’s SYCL implementation. SYCL is designed to use a single source code to target multiple accelerators such as: multi-core CPUs, GPUs and even FPGAs. Additionally, the C/C++ compiler provided in the oneAPI toolkit supports OpenMP which also allows targeting codes on both CPU and GPU devices. In this paper, the performance of SYCL and OpenMP is evaluated using the well-known non-negative matrix factorization (NMF) algorithm. Three different NMF implementations are developed: baseline, SYCL and OpenMP versions to analyze the acceleration on CPU and GPU. Experimental results show that while the two programming models perform almost identically on CPU, on GPU, SYCL outperforms its OpenMP counterpart slightly.
基于OpenMP和SYCL的非负矩阵分解算法的可移植性和性能评估
发布SYCL标准是为了提高跨异构环境的代码可移植性。英特尔发布了oneAPI工具包,其中包括数据并行c++ (dpc++)编译器,这是英特尔的SYCL实现。SYCL旨在使用单个源代码来针对多个加速器,例如:多核cpu, gpu甚至fpga。此外,oneAPI工具包中提供的C/ c++编译器支持OpenMP,它也允许在CPU和GPU设备上瞄准代码。本文使用著名的非负矩阵分解(NMF)算法对SYCL和OpenMP的性能进行了评估。开发了三种不同的NMF实现:基线,SYCL和OpenMP版本,以分析CPU和GPU上的加速。实验结果表明,虽然两种编程模型在CPU上的性能几乎相同,但在GPU上,SYCL的性能略优于OpenMP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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