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Shared memory parallel machines are relatively easy to program, but it is difficult to scale up their performance by increasing the number of processors because the bandwidth of the shared memory is limited. Although distributed memory parallel computers are more difficult to program, they have the potential to provide very large speedup, since their performance can scale up when the number of processors increases. This research effort evaluated how large scale scientific computational problems can be solved on two different distributed memory parallel systems at Carnegie Mellon - the Warp and Nectar systems. Three applications were chosen. The first problem from the biological sciences and the second application involving chemical process modeling were implemented on Warp. The third application of representing 3-dimensional models was implemented on both Warp and Nectar.