Parallel Approaches Performance Evaluation Using Curves B-spline
Abstract
Given the importance of algorithm parallelization for performance, this paper proposes the performance analysis for different levels of parallel approaches allowing to conclude the best approach for b-spline curves. Performance was evaluated using different levels of parallelization, through instructions, with vectoring, and through cores, through threads. Our results demonstrated that a nucleus approach obtained very good performance while the vectorization did not gain.
References
Buss, S. R. (2003). 3D computer graphics: a mathematical introduction with OpenGL. Cambridge University Press.
Holewinski, J.; Ramamurthi, R. (2012). Dynamic trace-based analysis of vectorization potential of applications. ACM SIGPLAN Notices, 47:371–382.
Stock, K.; Pouchet, P. S. (2012). Using machine learning to improve automatic vectorization. ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers, 8(50).
Wolberg, G. (1990). Digital image warping, volume 10662. IEEE computer society press Los Alamitos, CA.