Graph Traverse Software Pipelining

Software pipelining is becoming widely used as a loop execution model for microprocessors supporting a high instruction level parallelism. In this paper we describe a heuristic method for software pipelining, named Graph Traverse Software Pipelining (GTSP), that divides the scheduling problem in two phases: scheduling on a graph and code generation. The first phase encapsulates the NP-complete part of the problem. The scheduling is represented on the data dependence graph (DDG) through a set of scheduling recurrences composed of scheduling edges, representing a partial ordering between statements in the each recurrence. A heuristic is given to include such edges in order to generate efficient schedules. It improves previous software pipelining techniques achieving the maximum throughput for 98% of a benchmark set composed of more than 500 innermost loops from the SpecFP95. The second phase is a fast and automatic algorithm that transforms the scheduling generated on the DDG into VLIW-like instructions. Among the advantages of GTSP we remark the abstraction on the graph that benefits from the large developed graph theory, the global knowledge of the heuristic during the scheduling phase, and the possibility of reaching maximum throughput schedules without unrolling the DDG.