Parallel Algorithms for Operations on Multi-Valued Decision Diagrams
Authors: Guillaume Perez, Jean-Charles Régin
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we give some experimental results for different sizes of MDDs and involving different number of cores, and we conclude. Experimental results show that they accelerate the sequential algorithms by a linear factor according to the number of involved workers. |
| Researcher Affiliation | Academia | Guillaume Perez Dept. of Computer Science Cornell University, Ithaca, NY 14850 USA gdp35@cornell.edu Jean-Charles R egin Universit Nice Sophia Antipolis, CNRS, I3S UMR 7271, 06900 Sophia Antipolis, France jcregin@gmail.com |
| Pseudocode | Yes | Algorithm 1 parallel reduce of an MDD. PARAREDUCE(mdd, W) Algorithm 2 Parallel Apply. APPLY(mdd1, mdd2, op, W): MDD |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper refers to “Real instances from (Perez and R egin 2015; 2017)” and “Random instances generated by fixing a lower and upper bound”, but it does not provide concrete access information (link, DOI, specific repository, or explicit statement of public availability) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., train/validation/test percentages, sample counts, or cross-validation setup). |
| Hardware Specification | Yes | All the experiments have been made on a Dell machine having four E74870 Intel processors, each having 10 cores with 256 GB of memory, 16 memory channels and running under Scientific Linux. On a simple Macbook pro 2013 using four cores, we observe the following speed-up: |
| Software Dependencies | No | The paper mentions “running under Scientific Linux” but does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | No | The paper describes the general algorithmic approach and high-level data generation characteristics (e.g., “fixing a lower and upper bound on the number of nodes on each layer and then building the outgoing arcs with respect to some probabilities”), but it does not provide concrete, reproducible details for the experimental setup such as specific hyperparameter values, iteration counts, or system-level training settings. |