{"url":"/method/wavelet-distributed-training","slug":"wavelet-distributed-training","name":"Wavelet Distributed Training","full_name":"Wavelet Distributed Training","full_name_withheld":false,"description_markdown":"**Wavelet** is an asynchronous data parallel approach that interleaves waves of training tasks on the same group of GPUs, such that tasks belong to one wave can leverage on-device memory from tasks in another wave during their memory valley period, thus boost-up the training throughput. As shown in the Figure, Wavelet divides dataparallel training tasks into two waves, namely tick-wave and tock-wave. The task launching offset is achieved by delaying the launch time of tock-wave tasks for half of a whole forward-backward training cycle. Therefore, the tock-wave tasks can directly leverage GPU memory valley period of tick-wave tasks (e.g. 0.4s-0.6s in Figure 2(a)), since backward propagation of tick-wave tasks is compute-heavy but memory is often unused. Similarly, tick-wave tasks can leverage memory valley period of tock-wave tasks in the same way.","description_state":"present","introduced_year":2021,"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":"Asynchronous Data Parallel","url":"/methods/category/asynchronous-data-parallel","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Data Parallel Methods","url":"/methods/category/data-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/wavelet-distributed-training"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}