Reducing Ranking Effects in Parallel Adaptive Quadrature

Parallel Generation of t-ary Trees

A parallel algorithm for generating t-ary tree sequences in reverse B-order is presented. The algorithm generates t-ary trees by 0-1 sequences, and each 0-1 sequences is generated in constant average time O(1). The algorithm is executed on a CREW SM SIMD model, and is adaptive and cost-optimal. Prior to the discussion of the parallel algorithm a new sequential generation with O(1) average time ...

Parallel Globally Adaptive Quadrature on the KSR

New algorithms for parallel one-dimensional globally adaptive quadrature are developed. The algorithms are implemented on a Kendall Square Research KSR-1 parallel computer and numerical results are presented. The most successful algorithm gives signiicant speedups on a range of hard problems, including ones with singular integrands.

Parallel Algorithms for Adaptive Quadrature - Convergence

Parallel Processing of Irregular Workloads on the GPGPU: Adaptive Quadrature

This paper presents a parallel (GPGPU) approach for dealing with the turbid workload of adaptive quadrature, called ‘parallel block-cutting adaptive quadrature’ (PBCAQ). PBCAQ provides speedups as high as 211 times the performance of its sequential competitors. In addition, it has two intertwined and desirable properties: (1) its speedups increase as the size of the workloads being processed in...

Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects

Gauss–Hermite quadrature is often used to evaluate and maximize the likelihood for random component probit models. Unfortunately, the estimates are biased for large cluster sizes and/or intraclass correlations. We show that adaptive quadrature largely overcomes these problems. We then extend the adaptive quadrature approach to general random coefficient models with limited and discrete dependen...