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Optimizing Sideways Composition: Fast Context-oriented Programming in ContextPyPy
| Content Provider | ACM Digital Library |
|---|---|
| Author | Pape, Tobias Felgentreff, Tim Hirschfeld, Robert |
| Abstract | The prevalent way of code sharing in many current object systems is static and/or single inheritance; both are limiting in situations that call for multi-dimensional decomposition. Sideways composition provides a technique to reduce their limitations. Context-oriented programming (COP) notably applies sideways composition to achieve better modularity. However, most COP implementations have a substantial performance overhead. This is partly because weaving and execution of layered methods violate assumptions that common language implementations hold about lookup. Meta-tracing just-in-time (JIT) compilers have unique characteristics that can alleviate the performance overhead, as they can treat lookup differently. We show that meta-tracing JIT compilers are good at optimizing sideways composition and give initial, supporting results. Furthermore, we suggest that explicit communication with the JIT compiler in a COP implementation can improve performance further. |
| Starting Page | 13 |
| Ending Page | 20 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450344401 |
| DOI | 10.1145/2951965.2951967 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Pypy Context-oriented programming Virtual machines Meta-tracing jit compilers Optimization |
| Content Type | Text |
| Resource Type | Article |
