Datasets used in scientific and engineering applications are often modeled as dense multi-dimensional arrays. For very large datasets, the corresponding array models are typically stored out-of-core as array files. The array elements are mapped onto linear consecutive locations that correspond to the linear ordering of the multi-dimensional indices. Two conventional mappings used are the row-major order and the column-major order of multi-dimensional arrays. Such conventional mappings of dense array files highly limit the performance of applications and the extendibility of the dataset. Firstly, an array file that is organized in say row-major order causes applications that subsequently access the data in column-major order, to have abysmal performance. Secondly, any subsequent expansion of the array file is limited to only one dimension. Expansions of such out-of-core conventional arrays along arbitrary dimensions, require storage reorganization that can be very expensive. We present a solution for storing out-of-core dense extendible arrays that resolve the two limitations. The method uses a mapping function F{sub}*(), together with information maintained in axial vectors, to compute the linear address of an extendible array element when passed its k-dimensional index. We also give the inverse function, (F{sub}*){sup}(-1)() for deriving the k-dimensional index when given the linear address. We show how the mapping function, in combination with MPI-IO and a parallel file system, allows for the growth of the extendible array without reorganization and no significant performance degradation of applications accessing elements in any desired order. We give methods for reading and writing sub-arrays into and out of parallel applications that run on a cluster of workstations, The axial-vectors are replicated and maintained in each node that accesses sub-array elements.
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