Embedded multicore processors for hard real-time applications like automobile engine control require the usage of local memory on each processor core to precisely meet the real-time deadline constraints, since cache memory cannot satisfy the deadline requirements due to cache misses. To utilize local memory, programmers or compilers need to explicitly manage data movement and data replacement for local memory considering the limited size. However, such management is extremely difficult and time consuming for programmers. This paper proposes an automatic local memory management method by compilers through (i) multi-dimensional data decomposition techniques to fit working sets onto limited size local memory (ii) suitable block management structures, called Adjustable Blocks, to create application specific fixed size data transfer blocks (iii) multi-dimensional templates to preserve the original multi-dimensional representations of the decomposed multi-dimensional data that are mapped onto one-dimensional Adjustable Blocks (iv) block replacement policies from liveness analysis of the decomposed data, and (v) code size reduction schemes to generate shorter codes. The proposed local memory management method is implemented on the OSCAR multi-grain and multi-platform compiler and evaluated on the Renesas RP2 8 core embedded homogeneous multicore processor equipped with local and shared memory. Evaluations on 5 programs including multimedia and scientific applications show promising results. For instance, speedups on 8 cores compared to single core execution using off-chip shared memory on an AAC encoder program, a MPEG2 encoder program, Tomcatv, and Swim are improved from 7.14 to 20.12, 1.97 to 7.59, 5.73 to 7.38, and 7.40 to 11.30, respectively, when using local memory with the proposed method. These evaluations indicate the usefulness and the validity of the proposed local memory management method on real embedded multicore processors.