Parallel Double Greedy Submodular Maximization
Authors: Xinghao Pan, Stefanie Jegelka, Joseph E Gonzalez, Joseph K. Bradley, Michael I Jordan
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement and evaluate both algorithms on multi-core hardware and billion edge graphs, demonstrating both the scalability and tradeoffs of each approach. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Science, and 2Department of Statistics University of California, Berkeley, Berkeley, CA USA 94720 {xinghao,stefje,jegonzal,josephkb,jordan}@eecs.berkeley.edu |
| Pseudocode | Yes | Algorithm 3: Ser-2g: serial double greedy, Algorithm 4: CF-2g: coord-free double greedy, Algorithm 5: CC-2g: concurrency control, Algorithm 6: CC-2g get Guarantee(e), Algorithm 7: CC-2g propose, Algorithm 8: CC-2g: commit(e, i, ue, result) |
| Open Source Code | No | The paper does not provide any statement or link regarding the public release of its source code. |
| Open Datasets | Yes | Our parallel algorithms were tested on the max graph cut and set cover problems with two synthetic graphs and three real datasets (Table 1). Friendster [21] Arabic-2005 [22, 23, 24] UK-2005 [22, 23, 24] IT-2004 [22, 23, 24]. |
| Dataset Splits | No | The paper applies its algorithms to entire graph datasets and does not mention specific train/validation/test splits for the input data in the traditional machine learning sense. |
| Hardware Specification | Yes | Experiments were conducted on Amazon EC2 using one cc2.8xlarge machine, up to 16 threads, for 10 repetitions. |
| Software Dependencies | No | We implemented the parallel and serial double greedy algorithms in Java / Scala. |
| Experiment Setup | Yes | Experiments were conducted on Amazon EC2 using one cc2.8xlarge machine, up to 16 threads, for 10 repetitions. ... We randomly permuted the ordering of vertices. |