The trends in advanced integrated circuit technologies require us to look for new ways to utilize large numbers of gates and reduce the effects of high interconnect delays. One promising research direction is chip multiprocessors that integrate multiple processors on the same die. Among the components of a chip multiprocessor, its memory subsystem is maybe the most critical one, since it shapes both power and performance characteristics of the resulting design. Motivated by this observation, this paper addresses the problem of decomposing (partitioning) onchip memory space across parallel processors and allocating data across memory components in an integrated manner. In the most general case, the resulting memory architecture is a hybrid one, where some memory components are accessed privately, whereas the others are shared by two or more processors. The proposed approach for achieving this has two complementary components: an optimizing compiler and an ILP (integer linear programming) solver. The role of the compiler in this approach is to analyze the application code and detect the interprocessor data sharing patterns, given the loop parallelization information. The job of the ILP solver, on the other hand, is to determine the sizes of the on-chip memory components, how these memory components are shared across multiple processors in the system, and what data each component holds. In other words, we address the problem of integrated memory space partitioning and data allocation for chip multiprocessors.