= Work fetch and GPUs = == Problems with the current work fetch policy == The current work-fetch policy is essentially: * Do a weighted round-robin simulation, computing the CPU shortfall (i.e., the idle CPU time we expect during the work-buffering period). * If there's a CPU shortfall, request work from the project with highest long-term debt (LTD). The scheduler request has a scalar "work_req_seconds" indicating the total duration of jobs being requested. This policy has some problems. First: * There's no way for the client to say "I have N idle CPUs; send me enough jobs to use them all". And various problems related to GPUs: * If there is no CPU shortfall, no work will be fetched even if GPUs are idle. * If a GPU is idle, we should get work from a project that potentially has jobs for it. * If a project has both CPU and GPU jobs, the client should be able to tell it to send only GPU (or only CPU) jobs. * LTD is computed solely on the basis of CPU time used, so it doesn't provide a meaningful comparison between projects that use only GPUs, or between a GPU and CPU projects. This document proposes a modification to the work-fetch system that solves these problems. For simplicity, the design assumes that there is only one GPU type (CUDA). It is straightforward to extend the design to handle additional GPU types. == Terminology == A job sent to a client is associated with an app version, which uses some number (possibly fractional) of CPUs and CUDA devices. * A '''CPU job''' is one that uses only CPU. * A '''CUDA job''' is one that uses CUDA (and may use CPU as well). == Scheduler request == New fields in the scheduler request message: '''double cpu_req_seconds''': number of CPU seconds requested '''double cuda_req_seconds''': number of CUDA seconds requested '''double ninstances_cpu''': send enough jobs to occupy this many CPUs '''double ninstances_cuda''': send enough jobs to occupy this many CUDA devs For compatibility with old servers, the message still has '''work_req_seconds'''; this is the max of (cpu,cuda)_req_seconds. == Client == New abstraction: '''processing resource''' or PRSC. There are two processing resource types: CPU and CUDA. === Per-resource-type backoff === We need to handle the situation where there's a GPU shortfall but no projects are supplying GPU work (for either permanent or transient reasons). We don't want an overall work-fetch backoff from those projects. Instead, we maintain a separate backoff timer per (project, PRSC). This is doubled whenever we ask for only work of that type and don't get any; it's cleared whenever we get a job of that type. === Work-fetch state === Each PRSC has its own set of data related to work fetch. This is stored in an object of class PRSC_WORK_FETCH. Data members of PRSC_WORK_FETCH '''ninstances''' Used/set by rr_simulation()): '''double shortfall''': shortfall for this resource '''double nidle''': number of currently idle instances Member functions of PRSC_WORK_FETCH: '''rr_init()''': called at the start of RR simulation. Compute share of each project for this PRSC, and clear shortfall. '''set_nidle()''': called by RR sim after initial job assignment. Set nidle to # of idle instances. '''accumulate_shortfall(dt)''': called by RR sim for each time interval during work buf period. {{{ nidle_now = ninstances - instances in use shortfall += dt*(nidle_now) for each project p not backed off for this PRSC p->PRSC_PROJECT_DATA.accumulate_shortfall(dt) }}} '''select_project()''': select the best project to request this type of work from. It's the project not backed off for this PRSC, and for which LTD + this->shortfall is largest '''accumulate_debt(dt)''': for each project p: {{{ x = insts of this device used by P's running jobs y = P's share of this device update P's LTD }}} Each PRSC also needs to have some per-project data. This is stored in an object of class PRSC_PROJECT_DATA. It has the following "persistent" members (i.e., saved in state file): '''double long_term_debt*''' '''backoff timer'''*: how long to wait until ask project for work specifically for this PRSC; double this any time we ask for work for this rsc and get none (maximum 24 hours). Clear it when we ask for work for this PRSC and get some job. And the following transient members (used by rr_simulation()): '''double share''': # of instances this project should get based on resource share relative to the set of projects not backed off for this PRSC. '''instances_used''': # of instances currently being used '''double shortfall''' '''accumulate_shortfall(dt)''' {{{ shortfall += dt*(share - instances_used) }}} === debt accounting === {{{ for each resource type R.accumulate_debt(dt) }}} === RR simulation === {{{ cpu_work_fetch.rr_init() cuda_work_fetch.rr_init() compute initial assignment of jobs cpu_work_fetch.set_nidle(); cuda_work_fetch.set_nidle(); do simulation as current on completion of an interval dt cpu_work_fetch.accumulate_shortfall(dt) cuda_work_fetch.accumulate_shortfall(dt) }}} === Work fetch === {{{ if cuda_work_fetch.nidle cpu_work_fetch.shortfall = 0 p = cuda_work_fetch.select_project() if p send_req(p) return if cpu_work_fetch.nidle cuda_work_fetch.shortfall = 0 p = cpu_work_fetch.select_project() if p send_req(p) return if cuda_work_fetch.shortfall p = cuda_work_fetch.select_project() if p send_req(p) return if cpu_work_fetch.shortfall p = cpu_work_fetch.select_project() if p send_req(p) return void send_req(p) req.cpu_req_seconds = cpu_work_fetch.shortfall req.cpu_req_ninstances = cpu_work_fetch.nidle req.cuda_req_seconds = cuda_work_fetch.shortfall req.cuda_req_ninstances = cuda_work_fetch.nidle req.work_req_seconds = max(req.cpu_req_seconds, req.cuda_req_seconds) }}} === Handling scheduler reply === if no jobs returned double backoff for each requested PRSC == Scheduler changes == {{{ global vars have_cpu_app_versions have_cuda_app_versions per-req vars bool coproc_request ncpu_jobs_sending ncuda_jobs_sending ncpu_seconds_to_fill ncuda_seconds_to_fill seconds_to_fill (backwards compat; used if !coproc_request) overall startup scan app versions, set have_x vars req startup if send_only_cpu and no CPU app versions, don't send work if send_only_cuda and no CUDA app versions, don't send work work_needed() need_more_cpu_jobs = n_cpu_jobs_sending < ninstances_cpu or cpu_seconds_to_fill > 0 same for cuda return false if don't need more CPU or more CUDA get_app_version if send_only_cpu, ignore CUDA versions if send_only_cuda, ignore CPU versions when commit a job update n*_jobs_sending, n*_seconds_to_fill, seconds_to_fill }}}