50b9195882
First, we switch from a `BTreeSet` to a `HashSet` because clearing a `BTreeSet` will deallocate the btree's nodes but clearing a `HashSet` will not deallocate the backing hash table, saving the space to reuse for future insertions. Then, we reuse the same set (and therefore the same allocation) across every call to `can_optimize_var_lookup`. This results in a 1.22x to 1.32x speed up on various Sightglass benchmarks: ``` compilation :: nanoseconds :: benchmarks/pulldown-cmark/benchmark.wasm Δ = 39478181.76 ± 3441880.32 (confidence = 99%) main.so is 0.75x to 0.79x faster than reuse-set.so! reuse-set.so is 1.27x to 1.32x faster than main.so! [160128343 172174751.09 213325968] main.so [115055695 132696569.33 200782128] reuse-set.so compilation :: nanoseconds :: benchmarks/bz2/benchmark.wasm Δ = 22576954.88 ± 1830771.68 (confidence = 99%) main.so is 0.77x to 0.81x faster than reuse-set.so! reuse-set.so is 1.25x to 1.29x faster than main.so! [100449245 106820149.65 118628066] main.so [77039172 84243194.77 128168647] reuse-set.so compilation :: nanoseconds :: benchmarks/spidermonkey/benchmark.wasm Δ = 664533554.97 ± 22109170.05 (confidence = 99%) main.so is 0.81x to 0.82x faster than reuse-set.so! reuse-set.so is 1.22x to 1.23x faster than main.so! [3549762523 3640587103.35 3798662501] main.so [2793335181 2976053548.38 3192950484] reuse-set.so ```
This crate provides a straightforward way to create a
Cranelift IR function and fill it with
instructions translated from another language. It contains an SSA construction
module that provides convenient methods for translating non-SSA variables into
SSA Cranelift IR values via use_var and def_var calls.