{"action":"created","issue":{"url":"https://api.github.com/repos/gprMax/gprMax/issues/94","repository_url":"https://api.github.com/repos/gprMax/gprMax","labels_url":"https://api.github.com/repos/gprMax/gprMax/issues/94/labels{/name}","comments_url":"https://api.github.com/repos/gprMax/gprMax/issues/94/comments","events_url":"https://api.github.com/repos/gprMax/gprMax/issues/94/events","html_url":"https://github.com/gprMax/gprMax/issues/94","id":209792776,"number":94,"title":"OpenMP - core utilisation on multiple CPU","user":{"login":"craig-warren","id":11279495,"avatar_url":"https://avatars3.githubusercontent.com/u/11279495?v=4","gravatar_id":"","url":"https://api.github.com/users/craig-warren","html_url":"https://github.com/craig-warren","followers_url":"https://api.github.com/users/craig-warren/followers","following_url":"https://api.github.com/users/craig-warren/following{/other_user}","gists_url":"https://api.github.com/users/craig-warren/gists{/gist_id}","starred_url":"https://api.github.com/users/craig-warren/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/craig-warren/subscriptions","organizations_url":"https://api.github.com/users/craig-warren/orgs","repos_url":"https://api.github.com/users/craig-warren/repos","events_url":"https://api.github.com/users/craig-warren/events{/privacy}","received_events_url":"https://api.github.com/users/craig-warren/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":21,"created_at":"2017-02-23T15:23:38Z","updated_at":"2018-03-08T18:00:20Z","closed_at":null,"author_association":"OWNER","body":"From @Mark-Dunscomb:\r\n\r\ngprMax doesn't seem to utilize cores on a second processor when it runs. I.e. I have a computer with duel Xeon E5 8-core processors. When I run a model, all 8 cores on one of the processors are fully utilized but the other 8 on the other processor aren't touched. I've looked into possible configuration related issues with the processors but think I have them set to be incorporated together. Could the coding be involved here? It seems to me that both processors should be considered together as a single cluster and therefore no need to run additional MPI scripts for an HPC environment but... maybe they do?"},"comment":{"url":"https://api.github.com/repos/gprMax/gprMax/issues/comments/371570504","html_url":"https://github.com/gprMax/gprMax/issues/94#issuecomment-371570504","issue_url":"https://api.github.com/repos/gprMax/gprMax/issues/94","id":371570504,"user":{"login":"craig-warren","id":11279495,"avatar_url":"https://avatars3.githubusercontent.com/u/11279495?v=4","gravatar_id":"","url":"https://api.github.com/users/craig-warren","html_url":"https://github.com/craig-warren","followers_url":"https://api.github.com/users/craig-warren/followers","following_url":"https://api.github.com/users/craig-warren/following{/other_user}","gists_url":"https://api.github.com/users/craig-warren/gists{/gist_id}","starred_url":"https://api.github.com/users/craig-warren/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/craig-warren/subscriptions","organizations_url":"https://api.github.com/users/craig-warren/orgs","repos_url":"https://api.github.com/users/craig-warren/repos","events_url":"https://api.github.com/users/craig-warren/events{/privacy}","received_events_url":"https://api.github.com/users/craig-warren/received_events","type":"User","site_admin":false},"created_at":"2018-03-08T18:00:20Z","updated_at":"2018-03-08T18:00:20Z","author_association":"OWNER","body":"@zhoufeng617 the CPU-based solver which is parallelised using OpenMP can use all available CPU cores, but the speedup is not linear. Take a look at some of the plots in the performance section of the docs for examples on desktop, server, and HPC - http://docs.gprmax.com/en/latest/benchmarking.html\r\n\r\nThe use of MPI with GPUs is the same as for CPU, i.e. for task farming - distributing models to different GPUs or nodes in the case of CPU. Multiple GPUs are not treated as shared memory, i.e. you cannot distribute a large model among multiple GPUs. There are ways to mitigate this somewhat using things like NVLink - http://www.nvidia.com/object/nvlink.html"}}
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