{"action":"created","issue":{"url":"https://api.github.com/repos/ratt-ru/bullseye/issues/41","labels_url":"https://api.github.com/repos/ratt-ru/bullseye/issues/41/labels{/name}","comments_url":"https://api.github.com/repos/ratt-ru/bullseye/issues/41/comments","events_url":"https://api.github.com/repos/ratt-ru/bullseye/issues/41/events","html_url":"https://github.com/ratt-ru/bullseye/issues/41","id":56008531,"number":41,"title":"Coplanar w-faceting","user":{"login":"bennahugo","id":7934586,"avatar_url":"https://avatars.githubusercontent.com/u/7934586?v=3","gravatar_id":"","url":"https://api.github.com/users/bennahugo","html_url":"https://github.com/bennahugo","followers_url":"https://api.github.com/users/bennahugo/followers","following_url":"https://api.github.com/users/bennahugo/following{/other_user}","gists_url":"https://api.github.com/users/bennahugo/gists{/gist_id}","starred_url":"https://api.github.com/users/bennahugo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/bennahugo/subscriptions","organizations_url":"https://api.github.com/users/bennahugo/orgs","repos_url":"https://api.github.com/users/bennahugo/repos","events_url":"https://api.github.com/users/bennahugo/events{/privacy}","received_events_url":"https://api.github.com/users/bennahugo/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":{"login":"bennahugo","id":7934586,"avatar_url":"https://avatars.githubusercontent.com/u/7934586?v=3","gravatar_id":"","url":"https://api.github.com/users/bennahugo","html_url":"https://github.com/bennahugo","followers_url":"https://api.github.com/users/bennahugo/followers","following_url":"https://api.github.com/users/bennahugo/following{/other_user}","gists_url":"https://api.github.com/users/bennahugo/gists{/gist_id}","starred_url":"https://api.github.com/users/bennahugo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/bennahugo/subscriptions","organizations_url":"https://api.github.com/users/bennahugo/orgs","repos_url":"https://api.github.com/users/bennahugo/repos","events_url":"https://api.github.com/users/bennahugo/events{/privacy}","received_events_url":"https://api.github.com/users/bennahugo/received_events","type":"User","site_admin":false},"milestone":null,"comments":4,"created_at":"2015-01-30T08:50:58Z","updated_at":"2015-07-07T10:28:26Z","closed_at":null,"body":"Once the templating is in place this will be added"},"comment":{"url":"https://api.github.com/repos/ratt-ru/bullseye/issues/comments/119163144","html_url":"https://github.com/ratt-ru/bullseye/issues/41#issuecomment-119163144","issue_url":"https://api.github.com/repos/ratt-ru/bullseye/issues/41","id":119163144,"user":{"login":"bennahugo","id":7934586,"avatar_url":"https://avatars.githubusercontent.com/u/7934586?v=3","gravatar_id":"","url":"https://api.github.com/users/bennahugo","html_url":"https://github.com/bennahugo","followers_url":"https://api.github.com/users/bennahugo/followers","following_url":"https://api.github.com/users/bennahugo/following{/other_user}","gists_url":"https://api.github.com/users/bennahugo/gists{/gist_id}","starred_url":"https://api.github.com/users/bennahugo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/bennahugo/subscriptions","organizations_url":"https://api.github.com/users/bennahugo/orgs","repos_url":"https://api.github.com/users/bennahugo/repos","events_url":"https://api.github.com/users/bennahugo/events{/privacy}","received_events_url":"https://api.github.com/users/bennahugo/received_events","type":"User","site_admin":false},"created_at":"2015-07-07T10:28:26Z","updated_at":"2015-07-07T10:28:26Z","body":"@jgain @cyriltasse Our idea to use 1D separable filters panned out well. Pushing the filter lookups through texture memory (through a surface load call) gives a 20% improvement when running on a 1.5 GiB EVLA dataset with the convolution functions at a full support of 31 pixels and 128 w planes. I decided to keep on supporting compute capability >= 2.0 devices (using surface memory instead of the read only data cache on compute 3.5 cards) since I want to compare the K40m performance to the GT770 I have in my lab machine.  I also added an option to compile without the caching if the user ever needs filters that exceeds the device limitations. See commits ab3cbf949 and 57a3351e for details."}}