The C programming language
Nonparametric econometrics
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI-2: Advanced Features of the Message Passing Interface
Using MPI-2: Advanced Features of the Message Passing Interface
Efficient Nonparametric Density Estimation on the Sphere with Applications in Fluid Mechanics
SIAM Journal on Scientific Computing
User-Friendly Parallel Computations with Econometric Examples
Computational Economics
Vector nonlinear time-series analysis of gamma-ray burst datasets on heterogeneous clusters
Scientific Programming - International Symposium of Parallel and Distributed Computing & International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogenous Networks
Parallel Computing of Kernel Density Estimates with MPI
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
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Non-parametric kernel methods are becoming more commonplace for data analysis, modeling, and inference. Unfortunately, these methods are known to be computationally burdensome. The burden increases as the amount of available data rises and can quickly overwhelm the computational resources present in modern desktop workstations. Approximation-based approaches exist which can dramatically reduce execution time, however, these approaches remain just that--approximations, however good they may be. Along with the approximate nature of such approaches, they do not admit multivariate kernel estimation with general bandwidths (fixed, variable, and adaptive). In this paper, I consider a parallel implementation of a number of popular kernel methods based on the MPI standard. MPI is a freely available parallel distributed library that runs on 'commodity hardware' such as a network of workstations typically found in many office environments. A simple demonstration indicates how one can dramatically reduce the computational burden often associated with kernel methods thereby achieving an almost 'ideal' parallel speed-up, while the approach is valid for multivariate kernel estimation with general bandwidths and does not rely on approximations. Some straightforward applications illustrate just how disarmingly simple the MPI library can be to use.