MSc(Engg) Thesis, Department of Computer Science and Automation,
Indian Institute of Science, Bangalore, India, January 2008.
Instruction scheduling and Software pipelining are important compilation techniques which reorder instructions in a program to exploit instruction level parallelism. They are essential for enhancing instruction level parallelism in architectures such as Very Long Instruction Word and tiled processors. This thesis addresses two important problems in the context of these instruction reordering techniques. The first problem is for general purpose applications and architectures, while the second is for media and graphics applications for tiled and multi-core architectures. The first problem deals with software pipelining which is an instruction scheduling technique that overlaps instructions from multiple iterations. Software pipelining increases the register pressure and hence it may be required to introduce spill instructions.
In this thesis, we model the problem of register allocation with optimal spill code generation and scheduling in software pipelined loops as a 0-1 integer linear program. By minimizing the amount of spill code produced, the formulation ensures that the initiation interval (II) between successive iterations of the loop is not increased unnecessarily. Experimental results show that our formulation performs better than the existing heuristics by preventing an increase in the II and also generating less spill code on average among loops extracted from Perfect Club and SPEC benchmarks.
The second major contribution of the thesis deals with the code size aware scheduling of stream programs. Large scale synchronous dataflow graphs (SDF's) and StreamIt have emerged as powerful programming models for high performance streaming applications. In these models, a program is represented as a dataflow graph where each node represents an autonomous filter and the edges represent the channels through which the nodes communicate. In constructing static schedules for programs in these models, it is important to optimize the execution time buffer requirements of the data channel and the space required to store the encoded schedule. Earlier approaches have either given priority to one of the requirements or proposed ad-hoc methods for generating schedules with good trade-offs. In this thesis, we propose a genetic algorithm framework based on non- dominated sorting for generating serial schedules which have good trade-off between code size and buffer requirement. We extend the framework to generate software pipelined schedules for tiled architectures. From our experiments, we observe that the genetic algorithm framework generates schedules with good trade-off and performs better than the earlier approaches.