An Optimization-Driven Incremental Inline Substitution Algorithm for Just-in-Time Compilers
CGO 2019
Abstract
Inlining is one of the most important compiler optimizations. It reduces call overheads and widens the scope of other optimizations. But, inlining is somewhat of a black art of an optimizing compiler, and was characterized as a computationally intractable problem. Intricate heuristics, tuned during countless hours of compiler engineering, are often at the core of an inliner implementation. And despite decades of research, well-established inlining heuristics are still missing.
In this paper, we describe a novel inlining algorithm for JIT compilers that incrementally explores a program’s call graph, and alternates between inlining and optimizations. We devise three novel heuristics that guide our inliner: adaptive decision thresholds, callsite clustering, and deep inlining trials. We implement the algorithm inside Graal, a dynamic JIT compiler for the HotSpot JVM. We evaluate our algorithm on a set of industry-standard benchmarks, including Java DaCapo, Scalabench, Spark-Perf, STMBench7 and other benchmarks, and we conclude that it significantly improves performance, surpassing state-of-the-art inlining approaches with speedups ranging from 5% up to 3×.
Links
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