SCAN: A Heuristic for Near-Optimal Software Pipelining

Software pipelining is a classic compiler optimization that improves the performances of inner loops on instruction-level parallel processors. In the context of embedded computing, applications are compiled prior to manufacturing the system, so it is possible to invest large amounts of time for compiler optimizations. Traditionally, software pipelining is performed by heuristics such as iterative modulo scheduling. Optimal software pipelining can be formulated as integer linear programs, however these formulations can take exponential time to solve. As a result, the size of loops that can be optimally software pipelined is quite limited. In this article, we present the SCAN heuristic, which enables to benefit from the integer linear programming formulations of software pipelining even on loops of significant size. The principle of the SCAN heuristic is to iteratively constrain the software pipelining problem until the integer linear programming formulation is solvable in reasonable time. We applied the SCAN heuristic to a multimedia benchmark for the ST200 VLIW processor. We show that it almost always compute an optimal solution for loops that are intractable by classic integer linear programming approaches. This improves performances by up to 33.3% over the heuristic modulo scheduling of the production ST200 compiler.