TY - GEN
T1 - Evaluation of Medical Imaging Applications using SYCL
AU - Jin, Zheming
AU - Finkel, Hal
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85084339904&partnerID=8YFLogxK
U2 - 10.1109/BIBM47256.2019.8982983
DO - 10.1109/BIBM47256.2019.8982983
M3 - Conference contribution
AN - SCOPUS:85084339904
T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
SP - 2259
EP - 2264
BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
A2 - Yoo, Illhoi
A2 - Bi, Jinbo
A2 - Hu, Xiaohua Tony
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Y2 - 18 November 2019 through 21 November 2019
ER -