How to eliminate non-positive circuits in periodic scheduling: a procative strategy based on shortest path equations

International audienceUsual periodic scheduling problems deal with precedence constraints having non-negative latencies. This seems a natural way for modelling scheduling problems, since task delays are generally non-negative quantities. However, in some cases, we need to consider edges latencies that do not only model task latencies, but model other precedence constraints. For instance in register optimisation problems devoted to optimising compilation, a generic machine or processor model can allow considering access delays into/from registers. Edge latencies may be then non-positive leading to a difficult scheduling problem in presence of resources constraints. This research result is related to the problem of periodic scheduling with storage requirement optimisation; its aims is to solve the practical problem of register optimisation in optimising compilation. We show that pre-conditioning a data dependence graph (DDG) to satisfy register constraints before periodic scheduling under resources constraints may create circuits with non-positive distances, resulted from the acceptance of non-positive edge latencies. As a compiler construction strategy, it is forbidden to allow the creation of circuits with non-positive distances during the compilation flow, because such DDG circuits do not guarantee the existence of a valid instruction schedule under resource constraints. We study two solutions to avoid the creation of these problematic circuits. A first solution is reactive, it tolerates the creation of non-positive circuit in a first step, and if detected in a further check step, makes a backtrack to eliminate them. A second solution is proactive, it prevents the creation of non-positive circuits in the DDG during the register optimisation process. It is based on shortest path equations which define a necessary and sufficient condition to free any DDG from these problematic circuits. Then we deduce a linear program accordingly. We have implemented our solutions and we present successful experimental results