2013 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2013) (2013)
Feb. 27, 2013 to March 1, 2013
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PDP.2013.20
P. D. Michailidis , Univ. of Western Macedonia, Florina, Greece
K. G. Margaritis , Univ. of Macedonia, Thessaloniki, Greece
Kernel density estimation is nowadays very popular tool for nonparametric probabilistic density estimation. One of its most important disadvantages is computational complexity of computations needed, especially for large data sets. One way for accelerating these computations is to use the parallel computing with multi-core platforms. In this paper we parallelize two kernel estimation methods such as the univariate and multivariate kernel estimation from the field of the computational econometrics on multi-core platform using different programming frameworks such as Pthreads, OpenMP, Intel Cilk++, Intel TBB, SWARM and FastFlow. The purpose of this paper is to present an extensive quantitative (i.e., performance) and qualitative (i.e., the ease of programming effort) study of the multi-core programming frameworks for these two kernel estimation methods.
Kernel, Estimation, Program processors, Parallel programming, Multicore processing, Optimization
P. D. Michailidis and K. G. Margaritis, "Parallel Computing of Kernel Density Estimation with Different Multi-core Programming Models," 2013 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2013)(PDP), Belfast, 2013, pp. 77-85.