Learning Instruction Scheduling Heuristics from Optimal Data.
MMath thesis, University of Waterloo, School of Computer Science, 2006.
The development of modern pipelined and multiple functional unit processors has increased the available instruction level parallelism. In order to fully utilize these resources, compiler writers spend large amounts of time developing complex scheduling heuristics for each new architecture. In order to reduce the time spent on this process, automated machine learning techniques have been proposed to generate scheduling heuristics. We present two case studies using these techniques to generate instruction scheduling heuristics for basic blocks and super blocks. A basic block is a block of code with a single flow of control and a super block is a collection of basic blocks with a single entry point but multiple exit points. We improve previous techniques for automated generation of basic block scheduling heuristics by increasing the quality of the training data and increasing the number of features considered, including several novel features that have useful effects on scheduling instructions. Our case study into super block scheduling heuristics is a novel contribution as previous approaches were only applied to basic blocks. We show through experimentation that we can produce efficient heuristics that perform better than current heuristic methods for basic block and super block scheduling. We show that we can reduce the number of non-optimally scheduled blocks by up to 55\% for basic blocks and 38\% for super blocks. We also show that we can produce better schedules 7.8 times more often than the next best heuristic for basic blocks and 4.4 times more often for super blocks.