{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/t-sne-cuda-gpu-accelerated-t-sne-and-its","title":"t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data","arxiv_id":"1807.11824","date":"2018-07-31","proceeding":null,"authors":["David M. Chan","Roshan Rao","Forrest Huang","John F. Canny"],"abstract":"Modern datasets and models are notoriously difficult to explore and analyze\ndue to their inherent high dimensionality and massive numbers of samples.\nExisting visualization methods which employ dimensionality reduction to two or\nthree dimensions are often inefficient and/or ineffective for these datasets.\nThis paper introduces t-SNE-CUDA, a GPU-accelerated implementation of\nt-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and\nmodels. t-SNE-CUDA significantly outperforms current implementations with\n50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for\nthe first time, visualization of the neural network activations on the entire\nImageNet dataset - a feat that was previously computationally intractable. We\nalso demonstrate visualization performance in the NLP domain by visualizing the\nGloVe embedding vectors. From these visualizations, we can draw interesting\nconclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is\npublicly available athttps://github.com/CannyLab/tsne-cuda","url_abs":"http://arxiv.org/abs/1807.11824v1","url_pdf":"http://arxiv.org/pdf/1807.11824v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"t-sne-cuda-gpu-accelerated-t-sne-and-its","repo_url":"https://github.com/CannyLab/tsne-cuda","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.11824","atlas_url":"https://app.syntology.ai/?focus=1807.11824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11824"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/CannyLab/tsne-cuda","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f4af514a2dfaea36","entry":"ord_string","repo":"CannyLab/tsne-cuda","repo_kind":"official","path":"src/python/tsnecuda/TSNE.py","file_url":"https://github.com/CannyLab/tsne-cuda/blob/HEAD/src/python/tsnecuda/TSNE.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"f4af514a2dfaea36"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}