TY - GEN

T1 - Parallelization strategies for continuum-generalized method of moments on the multi-thread systems

AU - B., Alhadi

AU - Handhika, T.

AU - Ernastuti,

AU - Kirani, Djati

N1 - Publisher Copyright:
© 2017 Author(s).

PY - 2017/7/10

Y1 - 2017/7/10

N2 - Continuum-Generalized Method of Moments (C-GMM) covers the Generalized Method of Moments (GMM) shortfall which is not as efficient as Maximum Likelihood estimator by using the continuum set of moment conditions in a GMM framework. However, this computation would take a very long time since optimizing regularization parameter. Unfortunately, these calculations are processed sequentially whereas in fact all modern computers are now supported by hierarchical memory systems and hyperthreading technology, which allowing for parallel computing. This paper aims to speed up the calculation process of C-GMM by designing a parallel algorithm for C-GMM on the multi-thread systems. First, parallel regions are detected for the original C-GMM algorithm. There are two parallel regions in the original C-GMM algorithm, that are contributed significantly to the reduction of computational time: the outer-loop and the inner-loop. Furthermore, this parallel algorithm will be implemented with standard shared-memory application programming interface, i.e. Open Multi-Processing (OpenMP). The experiment shows that the outer-loop parallelization is the best strategy for any number of observations.

AB - Continuum-Generalized Method of Moments (C-GMM) covers the Generalized Method of Moments (GMM) shortfall which is not as efficient as Maximum Likelihood estimator by using the continuum set of moment conditions in a GMM framework. However, this computation would take a very long time since optimizing regularization parameter. Unfortunately, these calculations are processed sequentially whereas in fact all modern computers are now supported by hierarchical memory systems and hyperthreading technology, which allowing for parallel computing. This paper aims to speed up the calculation process of C-GMM by designing a parallel algorithm for C-GMM on the multi-thread systems. First, parallel regions are detected for the original C-GMM algorithm. There are two parallel regions in the original C-GMM algorithm, that are contributed significantly to the reduction of computational time: the outer-loop and the inner-loop. Furthermore, this parallel algorithm will be implemented with standard shared-memory application programming interface, i.e. Open Multi-Processing (OpenMP). The experiment shows that the outer-loop parallelization is the best strategy for any number of observations.

UR - http://www.scopus.com/inward/record.url?scp=85026261460&partnerID=8YFLogxK

U2 - 10.1063/1.4991250

DO - 10.1063/1.4991250

M3 - Conference contribution

AN - SCOPUS:85026261460

T3 - AIP Conference Proceedings

BT - International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016

A2 - Sugeng, Kiki Ariyanti

A2 - Triyono, Djoko

A2 - Mart, Terry

PB - American Institute of Physics Inc.

T2 - 2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016

Y2 - 1 November 2016 through 2 November 2016

ER -