# Exploring Parallel Efficiency and Synergy for Max-P Region Problem Using Python ``` release_rev_7add13b6-7af4-4d9a-a2aa-b134e0b9df5f ```

by Viney Sindhu

### Entity Metadata (schema)

 `abstracts[]` ``` {'sha1': '4475482937bb830f45647bb58a5b8685151cef4f', 'content': 'Given a set of n areas spatially covering a geographical zone such as a province, forming contiguous regions from homogeneous neighboring areas satisfying a minimum threshold criterion over each region is an interesting NP-hard problem that has applications in various domains such as political science and GIS. We focus on a specific case, called Max-p regions problem, in which the main objective is to maximize the number of regions while keeping heterogeneity in each region as small as possible. The solution is broken into two phases: Construction phase and Optimization phase. We present a parallel implementation of the Max-p problem using Python multiprocessing library. By exploiting an intuitive data structure based on multi-locks, we achieve up 12-fold and 19-fold speeds up over the best sequential algorithm for the construction and optimization phases respectively. We provide extensive experimental results to verify our algorithm.', 'mimetype': 'text/plain', 'lang': 'en'} ``` `container` `container_id` `contribs[]` ``` {'index': 0, 'creator_id': None, 'creator': None, 'raw_name': 'Viney Sindhu', 'given_name': 'Viney', 'surname': 'Sindhu', 'role': 'author', 'raw_affiliation': None, 'extra': None} ``` `ext_ids` `{'doi': '10.57709/12040187', 'wikidata_qid': None, 'isbn13': None, 'pmid': None, 'pmcid': None, 'core': None, 'arxiv': None, 'jstor': None, 'ark': None, 'mag': None, 'doaj': None, 'dblp': None, 'oai': None, 'hdl': None}` `files` `filesets` `issue` `language` `license_slug` `number` `original_title` `pages` `publisher` `Georgia State University` `refs` `[]` `release_date` `release_stage` `published` `release_type` `article-journal` `release_year` `2018` `subtitle` `title` `Exploring Parallel Efficiency and Synergy for Max-P Region Problem Using Python` `version` `volume` `webcaptures` `withdrawn_date` `withdrawn_status` `withdrawn_year` `work_id` `sl4l6mxcqvgrfn4ngcflkqkrza`

### Extra Metadata (raw JSON)

 `datacite.resourceType` `thesis` `datacite.resourceTypeGeneral` `Text`