Autobahn 2.0: minimizing bangs while maintaining performance (system demonstration)

Abstract

Lazy evaluation has many advantages, but it can cause bad performance. Consequently, Haskell allows users to force eager evaluation at certain program points by inserting strictness annotations, known and written as bangs (!). Unfortunately, manual bang placement is difficult. Autobahn 1.0 uses a genetic algorithm to infer bang annotations that improve performance. However, Autobahn 1.0 often generates large numbers of superfluous bangs, which is problematic because users must inspect each such bang to determine whether it is safe. We introduce Autobahn 2.0, which uses GHC profiling information to reduce the number of superfluous bangs. When evaluated on the NoFib benchmark suite, Autobahn 2.0 reduced the number of inferred bangs by 90.2% on average, while only degrading program performance by 15.7% compared with the performance produced by Autobahn 1.0. In a case study on a garbage collection simulator, Autobahn 2.0 eliminated 81.8% of the recommended bangs, with the same 15.7% optimization degradation.