Improving the performance of medical imaging applications using SYCL

Abstract

As opposed to the Open Computing Language (OpenCL) programming model, in which host and device codes are generally written in different programming languages, SYCL can combine host and device codes for an application in a type-safe way to improve development productivity and performance portability. Hence, this report shows the experimental results of applying the SYCL programming model to medical imaging applications for a study on performance portability and programming productivity between OpenCL and SYCL. Rodinia is a widely used open-source benchmark suite for heterogeneous computing. We choose two medical imaging applications (Heart Wall and Particle Filter) in the benchmark suite, migrate the OpenCL implementations of the applications to the SYCL implementations, and evaluate their performance and productivity on Intel® microprocessors that contain a central processing unit (CPU) and an integrated graphics processing unit (GPU). The maturing SYCL compilers, which are based on a conformant implementation of the SYCL 1.2.1 Khronos specification, have been actively optimized for Intel® computing platforms. The experimental results are promising in terms of the raw performance and productivity. Although the SYCL implementation of the Heart Wall application does not execute successfully on a CPU, the SYCL implementations of the application can achieve comparable or better performance than the OpenCL implementation on Intel® integrated GPUs. For the Particle Filter application, the performance difference between the SYCL and OpenCL implementations is comparable on the GPUs for most cases, but the SYCL implementations are on average 4X faster than the OpenCL implementations on Intel® Xeon® CPUs. For programming productivity, we arguably use lines of code as a way to measure programming productivity in software. The SYCL programs reduce the lines of code of the OpenCL programs by 52% and 38% for the Heart Wall and Particle Filter, respectively. The results indicate that SYCL is a promising programming model for heterogeneous computing with the maturing compilers. We organize the remainder of the report as follows. Section II introduces the SYCL programming model, compares the major differences between an OpenCL program and a SYCL program, and describes the characteristics of the two applications. Section III describes the SYCL programming model in more details, and shows the SYCL implementation of a kernel function in the Particle Filter application as an example. In Section IV, we evaluate the performance of the applications on the CPUs and GPUs. Section V concludes the report.