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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 graphs. The parallel machines include processors that employ multithreading and cache (Intel Nehalem and AMD Opteron) and massively multithreading and flat memory model (Cray XMT). The bio-imaging application involves registering different cell populations across samples using flow cytometry data. The population registration problem is solved by a generalized edge cover, computed from a weighted matching. We have used this approach to differentiate leukemic cells from healthy ones, and to identify phosphorylation shifts in T cells due to stimulation with an antibody. In current work, we are adapting the concept of consistency used in multiple sequence alignments to the population registration problem for large sample sets.