Code Optimization: Speeding-up Analyses

This lecture covers techniques to speed up dataflow analyses, such as work-list algorithms, equation ordering, smaller CFGs, and bit-vectors. It illustrates these techniques using a live variables example and discusses the importance of node ordering and post-order traversal. The lecture also delves into the work-list algorithm in Scala, basic blocks, and the use of bit vectors to represent sets. Additionally, it explores the significance of intermediate representations (IRs) in optimizations, focusing on machine-independent rewriting optimizations in CPS/L3. The session concludes with discussions on dead code elimination, common subexpression elimination, inlining, and constant folding, emphasizing the impact of optimization contexts and heuristics.