The imperative. When should you create a custom data type, as opposed to reusing pre-existing data types such as Either, Maybe or tuples? Here are some reasons you should reuse a generic type:
- It saves typing (both in declaration and in pattern matching), making it good for one-off affairs,
- It gives you a library of predefined functions that work with that type,
- Other developers have expectations about what the type does that make understanding quicker.
On the flip side of the coin:
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The essence of dataflow optimization is analysis and transformation, and it should come as no surprise that once you’ve defined your intermediate representation, the majority of your work with Hoopl will involve defining analysis and transformations on your graph of basic blocks. Analysis itself can be further divided into the specification of the dataflow facts that we are computing, and how we derive these dataflow facts during analysis. In part 2 of this series on Hoopl, we look at the fundamental structure backing analysis: the dataflow lattice. We discuss the theoretical reasons behind using a lattice and give examples of lattices you may define for optimizations such as constant propagation and liveness analysis.
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Hoopl is a higher-order optimization library. We think it’s pretty cool! This series of blog post is meant to give a tutorial-like introduction to this library, supplementing the papers and the source code. I hope this series will also have something for people who aren’t interested in writing optimization passes with Hoopl, but are interested in the design of higher-order APIs in Haskell. By the end of this tutorial, you will be able to understand references in code to names such as analyzeAndRewriteFwd and DataflowLattice, and make decode such type signatures as:
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