Abstract
As opposed to the Open Computing Language (OpenCL) programming model in which host and device codes are written in different languages, the SYCL programming model can combine host and device codes for an application in a type-safe way to improve development productivity. In this paper, we chose two medical imaging applications (Heart Wall and Particle Filter) in the Rodinia benchmark suite to study the performance and programming productivity of the SYCL programming model. More specifically, we introduced the SYCL programming model, shared our experience of implementing the applications using SYCL, and compared the performance and programming portability of the SYCL implementations with the OpenCL implementations on an Intel® Xeon® CPU and an Iris® Pro integrated GPU. The results are promising. For the Heart Wall application, the SYCL implementation is on average 15% faster than the OpenCL implementation on the GPU. For the Particle Filter application, the SYCL implementation is 3% slower than the OpenCL implementation on the GPU, but it is 75% faster on the CPU. Using lines of code as an indicator of programming productivity, the SYCL host program reduces the lines of code of the OpenCL host program by 52% and 38% for the Heart Wall and Particle Filter applications, respectively.
| Original language | English |
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| Title of host publication | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
| Editors | Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2259-2264 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728118673 |
| DOIs | |
| State | Published - Nov 2019 |
| Externally published | Yes |
| Event | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019 |
Publication series
| Name | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Conference
| Conference | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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| Country/Territory | United States |
| City | San Diego |
| Period | 11/18/19 → 11/21/19 |
Funding
We appreciate the reviewers for their constructive criticism. Results presented were obtained using the Chameleon testbed supported by the National Science Foundation. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.