{"action":"created","issue":{"url":"https://api.github.com/repos/nygenome/lancet/issues/26","repository_url":"https://api.github.com/repos/nygenome/lancet","labels_url":"https://api.github.com/repos/nygenome/lancet/issues/26/labels{/name}","comments_url":"https://api.github.com/repos/nygenome/lancet/issues/26/comments","events_url":"https://api.github.com/repos/nygenome/lancet/issues/26/events","html_url":"https://github.com/nygenome/lancet/issues/26","id":346815551,"node_id":"MDU6SXNzdWUzNDY4MTU1NTE=","number":26,"title":"Memory and Runtime","user":{"login":"ahwanpandey","id":8450532,"node_id":"MDQ6VXNlcjg0NTA1MzI=","avatar_url":"https://avatars2.githubusercontent.com/u/8450532?v=4","gravatar_id":"","url":"https://api.github.com/users/ahwanpandey","html_url":"https://github.com/ahwanpandey","followers_url":"https://api.github.com/users/ahwanpandey/followers","following_url":"https://api.github.com/users/ahwanpandey/following{/other_user}","gists_url":"https://api.github.com/users/ahwanpandey/gists{/gist_id}","starred_url":"https://api.github.com/users/ahwanpandey/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ahwanpandey/subscriptions","organizations_url":"https://api.github.com/users/ahwanpandey/orgs","repos_url":"https://api.github.com/users/ahwanpandey/repos","events_url":"https://api.github.com/users/ahwanpandey/events{/privacy}","received_events_url":"https://api.github.com/users/ahwanpandey/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-08-02T00:23:28Z","updated_at":"2018-08-02T02:44:23Z","closed_at":null,"author_association":"NONE","body":"First off thanks for this awesome tool!\r\n\r\nI just discovered Lancet a couple of days ago and have been testing it on some WGS data. I have 48 Tumor/Normal pairs to run, with a lot more data incoming soon. Before I invested too much time on it I was going to do some benchmarking with some results from other tools we have already run. Also wanted to get an idea of how realistic it would be to run Lancet on such a large cohort given the computing resources I have. By default a user is capped at 90 CPUs at one time. Also priority comes into effect and sometimes when the cluster is full I only see about 5 to 10 cores free per user! Memory limits are no issue as long as they are realistic. I decided to split every chromosome into 3 regions. Sure I could split the larger chromosomes into more regions and smaller into lesser, but just wanted to keep it simple in this fist test.\r\n\r\nThe command is:\r\n`lancet --tumor tumor.recal.bam --normal normal.recal.bam --ref human_g1k_v37.fasta --bed regA.chrB.bed --num-threads 8`\r\n\r\nwhere (regA) goes from (1-72) and chrB goes from (1 to 22, X, MT). So a total of 72 jobs per sample with each job requesting 30GB and 8 threads.\r\n\r\nThe average coverage for the Normals is 40x and the Tumors is 80x\r\n\r\nI have attached run-times of thread 1 for each of the jobs as I figured the run-times are similar between threads. Keep in mind the best case scenario for jobs running concurrently is 11 jobs requesting 8 threads each (i.e. 88 cores being used).\r\n\r\n```\r\nREGION_ID\tCHROMOSOME\tElapsed time thread1 (hrs)\r\nreg1\t1\t7.289777778\r\nreg2\t1\t8.385305556\r\nreg3\t1\t6.514277778\r\nreg4\t2\t6.756916667\r\nreg5\t2\t8.549111111\r\nreg6\t2\t6.455666667\r\nreg7\t3\t7.225888889\r\nreg8\t3\t7.361805556\r\nreg9\t3\t5.463055556\r\nreg10\t4\t5.154444444\r\nreg11\t4\t4.030166667\r\nreg12\t4\t4.335027778\r\nreg13\t5\t6.024444444\r\nreg14\t5\t5.115027778\r\nreg15\t5\t3.2065\r\nreg16\t6\t3.953527778\r\nreg17\t6\t4.191166667\r\nreg18\t6\t2.476222222\r\nreg19\t7\t3.918833333\r\nreg20\t7\t6.621472222\r\nreg21\t7\t4.082888889\r\nreg22\t8\t2.602305556\r\nreg23\t8\t4.359805556\r\nreg24\t8\t6.478555556\r\nreg25\t9\t4.088416667\r\nreg26\t9\t3.244722222\r\nreg27\t9\t4.872333333\r\nreg28\t10\t4.01575\r\nreg29\t10\t4.222555556\r\nreg30\t10\t3.842694444\r\nreg31\t11\t3.968222222\r\nreg32\t11\t5.392555556\r\nreg33\t11\t3.807027778\r\nreg34\t12\t3.341333333\r\nreg35\t12\t3.666388889\r\nreg36\t12\t2.968\r\nreg37\t13\t1.375472222\r\nreg38\t13\t1.923166667\r\nreg39\t13\t3.390722222\r\nreg40\t14\t0.786972222\r\nreg42\t14\t3.648527778\r\nreg41\t14\t2.286166667\r\nreg43\t15\t1.382638889\r\nreg45\t15\t4.857083333\r\nreg44\t15\t3.673861111\r\nreg46\t16\t2.563305556\r\nreg47\t16\tJob 2073292 exceeded memory limit (33390852 > 31457280), being killed\r\nreg48\t16\t2.042722222\r\nreg49\t17\t2.158833333\r\nreg50\t17\t1.934472222\r\nreg53\t18\t1.70975\r\nreg51\t17\t4.275583333\r\nreg52\t18\t2.026027778\r\nreg54\t18\t1.588333333\r\nreg55\t19\t2.4165\r\nreg56\t19\t2.266583333\r\nreg57\t19\t1.99225\r\nreg58\t20\t2.809472222\r\nreg59\t20\t3.324027778\r\nreg60\t20\t3.095194444\r\nreg61\t21\t1.828555556\r\nreg62\t21\t1.537527778\r\nreg63\t21\t1.732916667\r\nreg64\t22\t0.128027778\r\nreg65\t22\t1.549305556\r\nreg66\t22\t1.066\r\nreg67\tX\t3.484638889\r\nreg68\tX\t3.24825\r\nreg69\tX\t2.181777778\r\nreg70\tMT\t0.022638889\r\nreg71\tMT\t0.022611111\r\nreg72\tMT\t0.02425\r\n```\r\n\r\n**Question1** -> do these run-times look realistic to you? Can I improve on them without straying too far from the optimized variant calling defaults of lancet?\r\n\r\n**Question2** -> One of the regions (reg47) failed and said `[Job 2073292 exceeded memory limit (33390852 > 31457280), being killed]`. So it seems I would have to use more than 30GB per job just to be on the safe side. I am just arbitrarily testing memory required because on my first run I was only requesting 10GB and most jobs failed. So decided to try 30GB. How should I approach the memory requirements?\r\n\r\nReally appreciate your feedback here.\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/nygenome/lancet/issues/comments/409787888","html_url":"https://github.com/nygenome/lancet/issues/26#issuecomment-409787888","issue_url":"https://api.github.com/repos/nygenome/lancet/issues/26","id":409787888,"node_id":"MDEyOklzc3VlQ29tbWVudDQwOTc4Nzg4OA==","user":{"login":"gnarzisi","id":7513490,"node_id":"MDQ6VXNlcjc1MTM0OTA=","avatar_url":"https://avatars3.githubusercontent.com/u/7513490?v=4","gravatar_id":"","url":"https://api.github.com/users/gnarzisi","html_url":"https://github.com/gnarzisi","followers_url":"https://api.github.com/users/gnarzisi/followers","following_url":"https://api.github.com/users/gnarzisi/following{/other_user}","gists_url":"https://api.github.com/users/gnarzisi/gists{/gist_id}","starred_url":"https://api.github.com/users/gnarzisi/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gnarzisi/subscriptions","organizations_url":"https://api.github.com/users/gnarzisi/orgs","repos_url":"https://api.github.com/users/gnarzisi/repos","events_url":"https://api.github.com/users/gnarzisi/events{/privacy}","received_events_url":"https://api.github.com/users/gnarzisi/received_events","type":"User","site_admin":false},"created_at":"2018-08-02T02:44:23Z","updated_at":"2018-08-02T02:44:23Z","author_association":"COLLABORATOR","body":"Thank you for the kind words! \r\nHere are my answers to your questions:\r\n\r\n1. The running times that you listed are realistic for the 80x/30x tumor/normal coverage of your data. I am afraid you cannot improve them further without additional splitting of the regions or optimization of Lancet parameters. We plan to optimize the computational requirements in the future releases, but there is not timeline for it yet.\r\n\r\n2. To be safe, you may want to allocate more memory to each job. In my testings I have rarely seen more than 60GB of usage for each job though.  \r\n\r\nThank your for sharing your benchmarking results.  Let me know if you have more questions."}}
{"actor":{"display_login":"gnarzisi"}}