Alex Pothen — Page 2

arXiv2014

Immunophenotypes of Acute Myeloid Leukemia From Flow Cytometry Data Using Templates

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 prognosis and treatment for AML depends on the subtype. Results: We apply flowMatch, an algorithmic pipeline for flow cytometry data created in earlier work, to compute templates succinctly summarizing classes of AML and healthy samples. We develop a scoring function that accounts for features of the AML data such as heterogeneity to identify immunophenotypes corresponding to various AML subtypes, including APL. All of the AML samples in the test set are classified correctly with high confidence. Availability: flowMatch is available at www.bioconductor.org/packages/devel/bioc/html/flowMatch.html; programs specific to immunophenotyping AML are at www.cs.purdue.edu/homes/aazad/software.html.

q-bio.QM↗

arXiv2012

Graph Coloring Algorithms for Muti-core and Massively Multithreaded Architectures

We explore the interplay between architectures and algorithm design in the context of shared-memory platforms and a specific graph problem of central importance in scientific and high-performance computing, distance-1 graph coloring. We introduce two different kinds of multithreaded heuristic algorithms for the stated, NP-hard, problem. The first algorithm relies on speculation and iteration, and is suitable for any shared-memory system. The second algorithm uses dataflow principles, and is targeted at the non-conventional, massively multithreaded Cray XMT system. We study the performance of the algorithms on the Cray XMT and two multi-core systems, Sun Niagara 2 and Intel Nehalem. Together, the three systems represent a spectrum of multithreading capabilities and memory structure. As testbed, we use synthetically generated large-scale graphs carefully chosen to cover a wide range of input types. The results show that the algorithms have scalable runtime performance and use nearly the same number of colors as the underlying serial algorithm, which in turn is effective in practice. The study provides insight into the design of high performance algorithms for irregular problems on many-core architectures.

cs.DC↗