LEARNING EXECUTION THROUGH NEURAL CODE FUSION
Authors: Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As an illustration of this, we apply the new model to challenging dynamic tasks (branch prediction and prefetching) from the SPEC CPU benchmark suite, outperforming the state-of-the-art by 26% and 45% respectively. |
| Researcher Affiliation | Collaboration | Zhan Shi The University of Texas at Austin zshi17@utexas.edu Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi Google Research {kswersky, dtarlow, parthas, miladh}@google.edu |
| Pseudocode | No | The paper does not contain a clearly labeled "Pseudocode" or "Algorithm" block or figure. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use SPECint 2006 to evaluate our proposal. This is a standard benchmark suite commonly used to evaluate hardware and software system performance. (Sta, 2006) |
| Dataset Splits | Yes | We train the model on each benchmark independently. The first 70% of snapshots are used for training, and the last 30% for evaluation. ... These are split into 30 for training, 10 for validation (tuning the linear SVM described below) and 10 for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like gcc, GNU binary utilities, and Pin, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The hyperparameters for all models are given in Table 1. input feature size 64 hidden size 64 propagation steps 5 optimizer adam learning rate 0.01 |