Constraint Programming Techniques for Optimal Instruction Scheduling

Modern processors have multiple pipelined functional units and can issue more than one instruction per clock cycle. This puts great pressure on the instruction scheduling phase in a compiler to expose maximum instruction level parallelism. Basic blocks and superblocks are commonly used regions of code in a program for instruction scheduling. Instruction scheduling coupled with register allocation is also a well studied problem to produce better machine code. Scheduling basic blocks and superblocks optimally with or with out register allocation is NPcomplete, and is done sub-optimally in production compilers using heuristic approaches. In this thesis, I present a constraint programming approach to the superblock and basic block instruction scheduling problems for both idealized and realistic architectures. Basic block scheduling with register allocation with no spilling allowed is also considered. My models for both basic block and superblock scheduling are optimal and fast enough to be incorporated into production compilers. I experimentally evaluated my optimal schedulers on the SPEC 2000 integer and floating point benchmarks. On this benchmark suite, the optimal schedulers were very robust and scaled to the largest basic blocks and superblocks. Depending on the architectural model, between 99.991% to 99.999% of all basic blocks and superblocks were solved to optimality. The schedulers were able to routinely solve the largest blocks, including blocks with up to 2600 instructions. My results compare favorably to the best previous optimal approaches, which are based on integer programming and enumeration. My approach for basic block scheduling without allowing spilling was good enough to solve 97.496% of all basic blocks in the SPEC 2000 benchmark. The approach was able to solve basic blocks as large as 50 instructions for both idealized and realistic architectures within reasonable time limits. Again, my results compare favorably to recent work on optimal integrated code generation, which is based on integer programming.