A GPU-accelerated software eye tracking system
Proceedings of the Symposium on Eye Tracking Research and Applications
SystemC simulation on GP-GPUs: CUDA vs. OpenCL
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
System architecture and software design for electric vehicles
Proceedings of the 50th Annual Design Automation Conference
Revisiting co-processing for hash joins on the coupled CPU-GPU architecture
Proceedings of the VLDB Endowment
An application-centric evaluation of OpenCL on multi-core CPUs
Parallel Computing
KMA: A Dynamic Memory Manager for OpenCL
Proceedings of Workshop on General Purpose Processing Using GPUs
The continuous differential ant-stigmergy algorithm for numerical optimization
Computational Optimization and Applications
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This paper presents a comprehensive performance comparison between CUDA and OpenCL. We have selected 16 benchmarks ranging from synthetic applications to real-world ones. We make an extensive analysis of the performance gaps taking into account programming models, ptimization strategies, architectural details, and underlying compilers. Our results show that, for most applications, CUDA performs at most 30\% better than OpenCL. We also show that this difference is due to unfair comparisons: in fact, OpenCL can achieve similar performance to CUDA under a fair comparison. Therefore, we define a fair comparison of the two types of applications, providing guidelines for more potential analyses. We also investigate OpenCL's portability by running the benchmarks on other prevailing platforms with minor modifications. Overall, we conclude that OpenCL's portability does not fundamentally affect its performance, and OpenCL can be a good alternative to CUDA.