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Title: Contextual dispatch for function specialization
In order to generate efficient code, dynamic language compilers often need information, such as dynamic types, not readily available in the program source. Leveraging a mixture of static and dynamic information, these compilers speculate on the missing information. Within one compilation unit, they specialize the generated code to the previously observed behaviors, betting that past is prologue. When speculation fails, the execution must jump back to unoptimized code. In this paper, we propose an approach to further the specialization, by disentangling classes of behaviors into separate optimization units. With contextual dispatch, functions are versioned and each version is compiled under different assumptions. When a function is invoked, the implementation dispatches to a version optimized under assumptions matching the dynamic context of the call. As a proof-of-concept, we describe a compiler for the R language which uses this approach. Our implementation is, on average, 1.7× faster than the GNU R reference implementation. We evaluate contextual dispatch on a set of benchmarks and measure additional speedup, on top of traditional speculation with deoptimization techniques. In this setting contextual dispatch improves the performance of 18 out of 46 programs in our benchmark suite.  more » « less
Award ID(s):
1759736 1925644 1618732
PAR ID:
10601353
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
Association for Computing Machinery (ACM)
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
4
Issue:
OOPSLA
ISSN:
2475-1421
Format(s):
Medium: X Size: p. 1-24
Size(s):
p. 1-24
Sponsoring Org:
National Science Foundation
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