{"url":"/method/flexflow","slug":"flexflow","name":"FlexFlow","full_name":"FlexFlow","full_name_withheld":false,"description_markdown":"**FlexFlow** is a deep learning engine that uses guided randomized search of the SOAP (Sample, Operator, Attribute, and Parameter) space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy’s performance and is three orders of magnitude faster than prior approaches that execute each strategy. \r\n\r\nFlexFlow uses two main components: a fast, incremental execution simulator to evaluate different parallelization strategies, and a Markov Chain Monte Carlo (MCMC) search algorithm that takes advantage of the incremental simulator to rapidly explore the large search space.","description_state":"present","introduced_year":2019,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Auto Parallel Methods","url":"/methods/category/auto-parallel-methods","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Distributed Methods","url":"/methods/category/distributed-methods","pwc_aliases":[]}],"n_papers_tagged":0,"archive_num_papers":0,"papers_newest_first":[],"papers_shown":0,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/flexflow"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}