A Case Study of k-means Clustering using SYCL

As opposed to the OpenCL programming model in which host and device codes are written in two programming languages, the SYCL programming model combines them for an application in a type-safe way to improve development productivity. As a popular cluster analysis algorithm, k-means has been implemented using programming models such as OpenMP, OpenCL, and CUDA. Developing a SYCL implementation of k-means as a case study allows us to have a better understanding of performance portability and programming productivity of the SYCL programming model. Specifically, we explained the k-means benchmark in Rodinia, described our efforts of porting the OpenCL k-means benchmark, and evaluated the performance of the OpenCL and SYCL implementations on the Intel^® Haswell, Broadwell, and Skylake processors. We summarized the migration steps from OpenCL to SYCL, compiled the SYCL program using Codeplay and Intel^® SYCL compilers, analyzed the SYCL and OpenCL programs using an open-source profiling tool which can intercept OpenCL runtime calls, and compared the performance of the implementations on Intel^® CPUs and integrated GPU. The experimental results show that the SYCL version in which the kernels run on the GPU is 2% and 8% faster than the OpenCL version for the two large datasets. However, the OpenCL version is still much faster than the SYCL version on the CPUs. Compared to the Intel^® Haswell and Skylake CPUs, running the k-means benchmark on the Intel^® Broadwell low-power processor with a CPU and an integrated GPU can achieve the lowest energy consumption. In terms of programming productivity, the lines of code of the SYCL program are 51% fewer than those of the OpenCL program.