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- W2562298935 abstract "Tracing and partial evaluation have been proposed as meta- compilation techniques for interpreters to make just-in-time compilation language-independent. They promise that pro- grams executing on simple interpreters can reach perfor- mance of the same order of magnitude as if they would be executed on state-of-the-art virtual machines with highly optimizing just-in-time compilers built for a specific lan- guage. Tracing and partial evaluation approach this meta- compilation from two ends of a spectrum, resulting in dif- ferent sets of tradeoffs. This study investigates both approaches in the context of self-optimizing interpreters, a technique for building fast abstract-syntax-tree interpreters. Based on RPython for trac- ing and Truffle for partial evaluation, we assess the two ap- proaches by comparing the impact of various optimizations on the performance of an interpreter for SOM, an object- oriented dynamically-typed language. The goal is to deter- mine whether either approach yields clear performance or engineering benefits. We find that tracing and partial eval- uation both reach roughly the same level of performance. SOM based on meta-tracing is on average 3x slower than Java, while SOM based on partial evaluation is on average 2:3x slower than Java. With respect to the engineering, trac- ing has however significant benefits, because it requires lan- guage implementers to apply fewer optimizations to reach the same level of performance." @default.
- W2562298935 created "2017-01-06" @default.
- W2562298935 creator A5015543736 @default.
- W2562298935 date "2015-01-01" @default.
- W2562298935 modified "2023-09-26" @default.
- W2562298935 title "Comparing Meta-Compilation Approaches for Self-Optimizing Interpreters" @default.
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