https://github.com/smarr/selfopt-interp-performance
A reporting project on the performance of self-optimizing interpreters
https://github.com/smarr/selfopt-interp-performance
Last synced: about 1 year ago
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A reporting project on the performance of self-optimizing interpreters
- Host: GitHub
- URL: https://github.com/smarr/selfopt-interp-performance
- Owner: smarr
- Created: 2014-04-22T09:46:23.000Z (over 12 years ago)
- Default Branch: master
- Last Pushed: 2015-12-05T16:26:37.000Z (over 10 years ago)
- Last Synced: 2023-03-11T16:55:11.926Z (over 3 years ago)
- Language: Shell
- Size: 1.55 MB
- Stars: 16
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Self-Optimizing Interpreters
============================
This repository contains all the necessary bits and pieces for an evaluation
of different ideas around self-optimizing interpreters.
Currently, the evaluation focusses on the following aspects:
- _partial evaluation vs. meta-tracing_
What are the tradeoffs with respect to engineering effort and performance?
- _optimization techniques and their benefits_
There a number of common patterns used for self-optimizing interpreters,
and we are interested in which concrete impact they have on performance.
- _pure interpretation_
While peek-performance is one relevant criterion, startup time,
and pure interpretation speed are relevant in a number of scenarios as well.
Thus, we are interested in the impact of the various optimizations on
interpretation speed.
- _the future_
Which new and powerful language features could self-optimizing interpreters
facilitate? We think, they provide us with a technique that makes highly dynamic
language features such as _metaobject protocols_ practical. Here, we experiment
with such techniques and investigate the performance potential.
Self-Contained, Complete, and Reproducible Evaluation Setup
-----------------------------------------------------------
This repository contains everything that is necessary to re-execute all
experiments. Well, that excludes obviously the hardware, and operating system
setup.
The following quick start instructions, allow to recreate the basic environment:
```bash
git clone --recursive https://github.com/smarr/selfopt-interp-performance
```
The setup has been used and tested on Ubuntu and OS X.
The following programs are definitely required for execution:
- ReBench (>= 0.5) for benchmark execution
- knitr (>= 1.5) and R (>= 3.0) for report generation
For the various implementations, additional software is required:
- Graal
- C/C++ compiler (GCC, Clang)
- Java 8
- Python
- JRuby
- Maven
-
Licensing
---------
The material in this repository is licensed under the terms of the MIT License.
Please note, the repository links in form of submodules to other repositories
which are licensed under different terms.