Summary form only given. Biological structures are extremely complex at the cellular level. The MCell project has been highly successful in simulating the microphysiology of systems of modest size, but many larger problems require too much storage and computation time to be simulated on a single workstation. MCell-K, a new parallel variant of MCell, has been implemented using the KeLP framework and is running on NPACl's Blue Horizon. MCell-K not only produces validated results consistent with the serial version of MCell but does so with unprecedented scalability. We have thus found a level of description and a way to simulate cellular systems that can approach the complexity of nature on its own terms. At the heart of MCell is a 3D random walk that models diffusion using a Monte Carlo method. We discuss two challenging issues that arose in parallelizing the diffusion process - detecting time-step termination efficiently and performing parallel diffusion of particles in a biophysically accurate way. We explore the scalability limits of the present parallel algorithm and discuss ways to improve upon these limits.

SCALLOP is a highly scalable solver and library for elliptic partial differential equations on regular block-structured domains. SCALLOP avoids high communication overheads algorithmically by taking advantage of the locality properties inherent to solutions to elliptic PDEs. Communication costs are small, on the order of a few percent of the total running time on up to 1024 processors of NPACI's and NERSC's IBM Power-3 SP sytems. SCALLOP trades off numerical overheads against communication. These numerical overheads are independent of the number of processors for a wide range of problem sizes. SCALLOP is implicitly designed for infinite domain (free space) boundary conditions, but the algorithm can be reformulated to accommodate other boundary conditions. The SCALLOP library is built on top of the KeLP programming system and runs on a variety of platforms.