Efficient p-value estimation in massively parallel testing problems

Efficient p-value estimation in massively parallel testing problems

We present a new method to efficiently estimate very large numbers of p-values using empirically constructed null distributions of a test statistic. The need to evaluate a very large number of p-values is increasingly common with modern genomic data, and when interaction effects are of interest, the number of tests can easily run into billions. When the asymptotic distribution is not easily ava...

Efficient, Massively Parallel Eigenvalue Computation

In numerical simulations of disordered electronic systems, one of the most common approaches is to diagonalize random Hamiltonian matrices and to study the eigenvalues and eigenfunctions of a single electron in the presence of a random potential. In this paper, we describe an effort to implement a matrix diagonalization routine for real symmetric dense matrices on massively parallel SIMD comput...

Efficient Massively Parallel Transport Sweeps

The full-domain “sweep,” in which all angular fluxes in a problem are calculated given previous-iterate values only for the volumetric source, forms the foundation for many iterative methods that have desirable properties [1]. One important property is that iteration counts do not grow with mesh refinement [1]. The sweep solution on parallel machines is complicated by the dependency of a given ...

Stochastic Network Problems: Massively Parallel Solution

Inventory Estimation on the Massively Parallel Processor

This paper describes algorithms for efficiently computing inventory estimates from satellite based images. The algorithms incorporate a one dimensional feature extraction which optimizes the pairwise sum of Fisher distances. Biases are eliminated with a premultiplication by the inverse of the analytically derived error matrix. The technique is demonstrated with a numerical example using statist...