Parallelizing dynamic sequential programs using polyhedral process networks

The Polyhedral Process Network (PPN) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is a very difficult and highly error-prone task. To overcome the associated difficulties, we have developed the pn compiler, which derives PPN specifications from sequential static affine nested loop programs (SANLPs). However, there are many applications that have adaptive and dynamic behavior which cannot be expressed as SANLPs. In order to handle such dynamic applications, in this dissertation we address an important question: whether some of the static restrictions of the SANLPs can be relaxed while keeping the ability to perform compile-time analysis and to derive PPNs in an automated way. Achieving this will significantly extend the range of applications that can be parallelized in an automated way. By studying different dynamic applications we distinguished three relaxations to SANLP programs that would allow one to specify dynamic applications as sequential programs. These relaxations allow dynamic if-conditions, for-loops with dynamic bounds and while-loops in a program. The first relaxation has already been considered. In this dissertation, we consider the other two more difficult relaxations.UBL - phd migration 201