An efficient scheme for fine-grain software pipelining

Abstract

Dataflow software pipelining was proposed as a means of structuring fine-grain parallelism and has been studied mostly under an idealized dataflow architecture model with infinite resources [7]. In this paper, we address some issues of software pipelining under a realistic architecture model with finite resources. A general framework for fine-grain code scheduling in pipelined machines is developed which simultaneously addresses both time and space efficiency issues for loops typically found in general-purpose scientific computations. This scheduling method exploits fine-grain parallelism through a loop optimization technique which limitedly balances 1 the program graph at compile time, while the instruction-level scheduling is done dynamically at runtime in a data-driven manner.