Learning to superoptimize programs
Authors: Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H.S. Torr, Pushmeet Kohli
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmarks comprising of automatically generated as well as existing ( Hacker s Delight ) programs show that the proposed method is able to significantly outperform state of the art approaches for code super-optimization. |
| Researcher Affiliation | Collaboration | Rudy Bunel1, Alban Desmaison1, M. Pawan Kumar1,2 & Philip H.S. Torr1 1Department of Engineering Science University of Oxford 2Alan Turing Institute Oxford, UK {rudy,alban,pawan}@robots.ox.ac.uk, philip.torr@eng.ox.ac.uk Pushmeet Kohli Microsoft Research Redmond, WA 98052, USA pkohli@microsoft.com |
| Pseudocode | Yes | Figure 5: Generative Model of a Transformation. The figure presents a detailed pseudocode block describing the `proposal` function and different move types. |
| Open Source Code | No | The paper states that its system is built on top of the Stoke super-optimizer and uses the Torch framework, but it does not provide any link or explicit statement about making its own source code available for the methodology described. |
| Open Datasets | Yes | The first is based on the Hacker s delight (Warren, 2002) corpus, a collection of twenty five bit-manipulation programs, used as benchmark in program synthesis (Gulwani et al., 2011; Jha et al., 2010; Schkufza et al., 2013). |
| Dataset Splits | No | The paper describes training and test sets by dividing the Hacker's Delight dataset into even-numbered tasks for training and odd-numbered tasks for evaluation, and also states, "We generate 600 of these programs, 300 that we use as a training set for the optimizer to learn over and 300 that we keep as a test set." However, it does not explicitly mention a distinct validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running its experiments. |
| Software Dependencies | No | The paper mentions that the implementation uses the Torch framework (Collobert et al., 2011) and the Adam optimizer (Kingma & Ba, 2015), but it does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | The optimization is performed by stochastic gradient descent, using the Adam (Kingma & Ba, 2015) optimizer. For each estimate of the gradient, we draw 100 samples for our estimator. The values of the hyperparameters used are given in Appendix A. |