Tensors, or multidimensional arrays, have many use in data analysis applications. The additional dimensions over matrices (or two dimensional arrays) enable gleaning information that is otherwise unreachable. A remarkable example comes from the Netflix Challenge. The aim of the challenge was to improve the company's algorithm for predicting user ratings on movies using a dataset containing a set of ratings of users on movies. The winning algorithm, when the challenge was concluded, had to use the time dimension on top of user x movie rating, during the analysis. Tensors from many applications, such as the mentioned one, are sparse, which means that not all entries of the tensor are relevant or known. The PeachTree project investigates the building blocks of numerical parallel tensor computation algorithms on high end systems, and designs a set of scheduling and combinatorial tools for achieving efficiency.ROMA and TDAlab of Georgia Institute of Technology (an agreement is being drafted to be signed).