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We design, implement, and evaluate algorithms for computing a matching of maximum cardinality in a bipartite graph on multicore and massively multithreaded computers. As computers with larger numbers of slower cores dominate the commodity processor market, the design of multithreaded algorithms to solve large matching problems becomes a necessity. Recent(More)
Triangle counting and enumeration are important kernels that are used to characterize graphs. They are also used to compute important statistics such as clustering coefficients. We provide a simple exact algorithm that is based on operations on sparse adjacency matrices. By parallelizing the individual sparse matrix operations, we achieve a parallel(More)
We describe an algorithm to dynamically classify flow cytometry data samples into several classes based on their immunophenotypes. Flow cytometry data consists of fluorescence measurements of several proteins that characterize different cell types in blood or cultured cell lines. Each sample is initially clustered to identify the cell populations present in(More)
Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. The scaling of existing parallel implementations of SpGEMM is heavily bound by communication. Even though 3D (or 2.5D) algorithms have been proposed and theoretically analyzed in the(More)
When flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past(More)
Multi-channel, high throughput experimental methodologies for flow cytometry are transforming clinical immunology and hematology, and require the development of algorithms to analyze the highdimensional, large-scale data. We describe the development of two combinatorial algorithms to identify rare cell populations in data from mice with acute promyelocytic(More)
We study parallel algorithms for computing matchings in graphs and apply them to solve population registration problem from bio-imaging data. We have developed several classes of multithreaded algorithms for maximum cardinality matching and achieved good speedups on three shared memory machines on a representative set of large real-world and synthetic(More)
Motivation: We investigate whether a template-based classification pipeline could be used to identify immunophenotypes in (and thereby classify) a heterogeneous disease with many subtypes. The disease we consider here is Acute Myeloid Leukemia, which is heterogeneous at the morphologic, cytogenetic and molecular levels, with several known subtypes. The(More)
. Phylogenomics, even more so than traditional phylogenetics, needs to represent the uncertainty in evolutionary trees due to systematic error. Here we illustrate the analysis of genome-scale alignments of yeast, using robust measures of the additivity of the fit of distances to tree when using flexi Weighted Least Squares. A variety of DNA and protein(More)