{"action":"created","issue":{"url":"https://api.github.com/repos/cdcseacave/openMVS/issues/377","repository_url":"https://api.github.com/repos/cdcseacave/openMVS","labels_url":"https://api.github.com/repos/cdcseacave/openMVS/issues/377/labels{/name}","comments_url":"https://api.github.com/repos/cdcseacave/openMVS/issues/377/comments","events_url":"https://api.github.com/repos/cdcseacave/openMVS/issues/377/events","html_url":"https://github.com/cdcseacave/openMVS/issues/377","id":372233052,"node_id":"MDU6SXNzdWUzNzIyMzMwNTI=","number":377,"title":"CUDA NVLink","user":{"login":"williamlai3a","id":642314,"node_id":"MDQ6VXNlcjY0MjMxNA==","avatar_url":"https://avatars1.githubusercontent.com/u/642314?v=4","gravatar_id":"","url":"https://api.github.com/users/williamlai3a","html_url":"https://github.com/williamlai3a","followers_url":"https://api.github.com/users/williamlai3a/followers","following_url":"https://api.github.com/users/williamlai3a/following{/other_user}","gists_url":"https://api.github.com/users/williamlai3a/gists{/gist_id}","starred_url":"https://api.github.com/users/williamlai3a/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/williamlai3a/subscriptions","organizations_url":"https://api.github.com/users/williamlai3a/orgs","repos_url":"https://api.github.com/users/williamlai3a/repos","events_url":"https://api.github.com/users/williamlai3a/events{/privacy}","received_events_url":"https://api.github.com/users/williamlai3a/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2018-10-20T17:16:15Z","updated_at":"2019-10-14T11:43:30Z","closed_at":"2018-11-13T04:48:39Z","author_association":"NONE","body":"Hi,\r\n\r\nI am trying to reconstruct a large set of High-Res photo using the MvgMvs pipeline in AWS p3.8xlarge instances with 4 Tesla V100 GPUs NVLinked.\r\nHowever, it seems that DensifyPointCloud does not seem to see the NVLinked GPUs as one single logical unit. It neither uses all GPUs, instead it only initialized device 0.\r\nBelow is the App output:\r\n```\r\n#8. Densify point cloud\r\n['/home/ubuntu/openMVS_build/bin/DensifyPointCloud', 'scene.mvs', '-w', '/media/                                                                                                                                                             data/oldhole_result/mvs']\r\n17:03:56 [App     ] Build date: Oct 18 2018, 04:17:06\r\n17:03:56 [App     ] CPU: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz\r\n17:03:56 [App     ] RAM: 240.09GB Physical Memory 0B Virtual Memory\r\n17:03:56 [App     ] OS: Linux 4.4.0-1069-aws (x86_64)\r\n17:03:56 [App     ] SSE & AVX compatible CPU & OS detected\r\n17:03:56 [App     ] Command line: scene.mvs -w /media/data/oldhole_result/mvs\r\n17:03:56 [App     ] MapSMtoCores for SM 7.0 is undefined; default to use 128 cores/SM\r\n17:03:56 [App     ] MapSMtoCores for SM 7.0 is undefined; default to use 128 cores/SM\r\n17:03:56 [App     ] MapSMtoCores for SM 7.0 is undefined; default to use 128 cores/SM\r\n17:03:56 [App     ] MapSMtoCores for SM 7.0 is undefined; default to use 128 cores/SM\r\n17:03:56 [App     ] CUDA device 0 initialized: Tesla V100-SXM2-16GB (compute capability 7.0; memory 15.78GB)\r\n17:03:57 [App     ] Camera model loaded: platform 0; camera  0; f 0.965x0.965; poses 340\r\n17:03:57 [App     ] Camera model loaded: platform 1; camera  0; f 0.786x0.786; poses 99\r\n```\r\n\r\nMay I have any advises or pointer to what might go wrong?\r\nThanks.\r\nWilliam\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/cdcseacave/openMVS/issues/comments/541628807","html_url":"https://github.com/cdcseacave/openMVS/issues/377#issuecomment-541628807","issue_url":"https://api.github.com/repos/cdcseacave/openMVS/issues/377","id":541628807,"node_id":"MDEyOklzc3VlQ29tbWVudDU0MTYyODgwNw==","user":{"login":"jiaerping123","id":48344473,"node_id":"MDQ6VXNlcjQ4MzQ0NDcz","avatar_url":"https://avatars0.githubusercontent.com/u/48344473?v=4","gravatar_id":"","url":"https://api.github.com/users/jiaerping123","html_url":"https://github.com/jiaerping123","followers_url":"https://api.github.com/users/jiaerping123/followers","following_url":"https://api.github.com/users/jiaerping123/following{/other_user}","gists_url":"https://api.github.com/users/jiaerping123/gists{/gist_id}","starred_url":"https://api.github.com/users/jiaerping123/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jiaerping123/subscriptions","organizations_url":"https://api.github.com/users/jiaerping123/orgs","repos_url":"https://api.github.com/users/jiaerping123/repos","events_url":"https://api.github.com/users/jiaerping123/events{/privacy}","received_events_url":"https://api.github.com/users/jiaerping123/received_events","type":"User","site_admin":false},"created_at":"2019-10-14T11:43:30Z","updated_at":"2019-10-14T11:43:30Z","author_association":"NONE","body":"> no, it is not\r\nSorry， I may not have made it clear. I have the following questions:\r\n1.  I mean that If I use CUDA to calculate depth maps, can the accuracy be consistent with the CPU version?  \r\nIn my opinion， I don't think I'll get a better depth value than the CPU version through Spatial propagation, because these depths of the surrounding neighborhoods may not be updated yet.\r\n\r\n2. Will you consider to provide depth map calculation for CUDA version in the future?\r\n\r\n"}}
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