Chunked extendible dense arrays for scientific data storage

• The implementation of a chunked computed access scheme for dense extendible array.
• A method of computing inverse functions for extendible arrays.
• Use of a tree-indexing structure for the axial-vector of extendible array.
• Analysis of the complexity of the access functions and the storage overhead.
• Experimental performance comparison of our approach with conventional arrays.

Several meetings of the Extremely Large Databases Community for large scale scientific applications advocate the use of multidimensional arrays as the appropriate model for representing scientific databases. Scientific databases gradually grow to massive sizes of the order of terabytes and petabytes. As such, the storage of such databases require efficient dynamic storage schemes where the array is allowed to arbitrarily extend the bounds of the dimensions. Conventional multidimensional array representations cannot extend or shrink their bounds without relocating elements of the data-set. In general, extendibility of the bounds of the dimensions, is limited to only one dimension. This paper presents a technique for storing dense multidimensional arrays by chunks such that the array can be extended along any dimension without compromising the access time for an element. This is done with a computed access mapping function, that maps the k-dimensional index onto a linear index of the storage locations. This concept forms the basis for the implementation of an array file of any number of dimensions, where the bounds of the array dimension can be extended arbitrarily. Such a feature currently exists in the Hierarchical Data Format version 5 (HDF5). However, extending the bound of a dimension in the HDF5 array file can be unusually expensive in time. Such extensions, in our storage scheme for dense array files, can still be performed while still accessing elements of the array at orders of magnitude faster than in HDF5 or conventional arrays-files. We also present theoretical and experimental analysis of our scheme with respect to access time and storage overhead. Such mapping scheme can be readily integrated into existing PGAS models for parallel processing in a cluster networked computing environment.