mirror of https://github.com/BOINC/boinc.git
603 lines
17 KiB
C++
603 lines
17 KiB
C++
// This file is part of BOINC.
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// http://boinc.berkeley.edu
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// Copyright (C) 2008 University of California
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//
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// BOINC is free software; you can redistribute it and/or modify it
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// under the terms of the GNU Lesser General Public License
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// as published by the Free Software Foundation,
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// either version 3 of the License, or (at your option) any later version.
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//
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// BOINC is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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// See the GNU Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public License
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// along with BOINC. If not, see <http://www.gnu.org/licenses/>.
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//
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// This file contains functions that can be customized to
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// implement project-specific scheduling policies.
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// The functions are:
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//
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// wu_is_infeasible_custom()
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// Decide whether host can run a job using a particular app version.
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// In addition it can:
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// - set the app version's resource usage and/or FLOPS rate estimate
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// (by assigning to bav.host_usage)
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// - modify command-line args
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// (by assigning to bav.host_usage.cmdline)
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// - set the job's FLOPS count
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// (by assigning to wu.rsc_fpops_est)
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//
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// app_plan()
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// Decide whether host can use an app version,
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// and if so what resources it will use
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//
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// app_plan_uses_gpu():
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// Which plan classes use GPUs
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//
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// JOB::get_score():
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// Determine the value of sending a particular job to host;
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// (used only by "matchmaker" scheduling)
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//
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// WARNING: if you modify this file, you must prevent it from
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// being overwritten the next time you update BOINC source code.
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// You can either:
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// 1) write-protect this file, or
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// 2) put this in a differently-named file and change the Makefile.am
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// (and write-protect that)
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// In either case, put your version under source-code control, e.g. SVN
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#include "str_util.h"
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#include "util.h"
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#include "sched_config.h"
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#include "sched_main.h"
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#include "sched_msgs.h"
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#include "sched_send.h"
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#include "sched_score.h"
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#include "sched_shmem.h"
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#include "sched_version.h"
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#include "sched_customize.h"
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bool wu_is_infeasible_custom(WORKUNIT& wu, APP& app, BEST_APP_VERSION& bav) {
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#if 0
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// example: if WU name contains "_v1", don't use CUDA app
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// Note: this is slightly suboptimal.
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// If the host is able to accept both GPU and CPU jobs,
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// we'll skip this job rather than send it for the CPU.
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// Fixing this would require a big architectural change.
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//
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if (strstr(wu.name, "_v1") && bav.host_usage.ncudas) {
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return true;
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}
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#endif
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#if 0
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// example: for CUDA app, wu.batch is the minimum number of processors.
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// Don't send if #procs is less than this.
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//
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if (!strcmp(app.name, "foobar") && bav.host_usage.ncudas) {
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int n = g_request->coproc_cuda->prop.multiProcessorCount;
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if (n < wu.batch) {
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return true;
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}
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}
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#endif
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#if 0
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// example: if CUDA app and WU name contains ".vlar", don't send
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//
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if (bav.host_usage.ncudas) {
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if (strstr(wu.name, ".vlar")) {
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return true;
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}
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}
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#endif
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return false;
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}
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// Suppose we have a computation that uses two devices alternately.
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// The devices have speeds s1 and s2.
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// The fraction of work done on device 1 is frac.
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//
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// This function returns:
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// 1) the overall speed
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// 2) the utilization of device 1, which is always in (0, 1).
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//
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static inline void coproc_perf(
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double s1, double s2, double frac,
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double& speed, double& u1
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) {
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double y = (frac*s2 + (1-frac)*s1);
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speed = s1*s2/y;
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// do the math
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u1 = frac*s2/y;
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}
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// the following is for an app that can use anywhere from 1 to 64 threads
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//
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static inline bool app_plan_mt(
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SCHEDULER_REQUEST& sreq, HOST_USAGE& hu
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) {
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double ncpus = g_wreq->effective_ncpus;
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// number of usable CPUs, taking user prefs into account
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int nthreads = (int)ncpus;
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if (nthreads > 64) nthreads = 64;
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hu.avg_ncpus = nthreads;
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hu.max_ncpus = nthreads;
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sprintf(hu.cmdline, "--nthreads %d", nthreads);
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hu.projected_flops = sreq.host.p_fpops*hu.avg_ncpus*.99;
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// the .99 ensures that on uniprocessors a sequential app
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// will be used in preferences to this
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hu.peak_flops = sreq.host.p_fpops*hu.avg_ncpus;
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if (config.debug_version_select) {
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log_messages.printf(MSG_NORMAL,
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"[version] Multi-thread app projected %.2fGS\n",
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hu.projected_flops/1e9
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);
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}
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return true;
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}
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GPU_REQUIREMENTS ati_requirements;
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static bool ati_check(COPROC_ATI& c, HOST_USAGE& hu,
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int min_driver_version,
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bool need_amd_libs,
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double min_ram,
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double ndevs, // # of GPUs used; can be fractional
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double cpu_frac, // fraction of FLOPS performed by CPU
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double flops_scale
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) {
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ati_requirements.update(min_driver_version, min_ram);
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if (need_amd_libs) {
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if (!c.amdrt_detected) {
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return false;
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}
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} else {
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if (!c.atirt_detected) {
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return false;
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}
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}
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if (c.version_num < min_driver_version) {
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return false;
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}
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if (c.attribs.localRAM*MEGA < min_ram) {
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return false;
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}
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hu.gpu_ram = min_ram;
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hu.natis = ndevs;
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coproc_perf(
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g_request->host.p_fpops,
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hu.natis*c.peak_flops,
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cpu_frac,
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hu.projected_flops,
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hu.avg_ncpus
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);
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hu.peak_flops = hu.natis*c.peak_flops + hu.avg_ncpus*g_request->host.p_fpops;
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hu.max_ncpus = hu.avg_ncpus;
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hu.projected_flops *= flops_scale;
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return true;
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}
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#define ATI_MIN_RAM 250*MEGA
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static inline bool app_plan_ati(
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SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
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) {
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COPROC_ATI& c = sreq.coprocs.ati;
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if (!c.count) {
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return false;
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}
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if (!strcmp(plan_class, "ati")) {
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if (!ati_check(c, hu,
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1000000,
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true,
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ATI_MIN_RAM,
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1, .01,
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1
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)) {
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return false;
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}
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}
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if (!strcmp(plan_class, "ati13amd")) {
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if (!ati_check(c, hu,
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1003000,
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true,
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ATI_MIN_RAM,
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1, .01,
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1.01
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)) {
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return false;
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}
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}
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if (!strcmp(plan_class, "ati13ati")) {
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if (!ati_check(c, hu,
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1003186,
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false,
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ATI_MIN_RAM,
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1, .01,
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1.02
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)) {
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return false;
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}
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}
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if (!strcmp(plan_class, "ati14")) {
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if (!ati_check(c, hu,
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1004000,
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false,
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ATI_MIN_RAM,
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1, .01,
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1.03
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)) {
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return false;
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}
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}
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if (config.debug_version_select) {
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log_messages.printf(MSG_NORMAL,
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"[version] %s ATI app projected %.2fG peak %.2fG %.3f CPUs\n",
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plan_class,
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hu.projected_flops/1e9,
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hu.peak_flops/1e9,
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hu.avg_ncpus
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);
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}
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return true;
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}
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GPU_REQUIREMENTS cuda_requirements;
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#define CUDA_MIN_DRIVER_VERSION 17700
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#define CUDA23_MIN_CUDA_VERSION 2030
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#define CUDA23_MIN_DRIVER_VERSION 19038
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#define CUDA3_MIN_CUDA_VERSION 3000
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#define CUDA3_MIN_DRIVER_VERSION 19500
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#define CUDA_OPENCL_MIN_DRIVER_VERSION 19713
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static bool cuda_check(COPROC_NVIDIA& c, HOST_USAGE& hu,
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int min_cc, int max_cc,
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int min_cuda_version, int min_driver_version,
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double min_ram,
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double ndevs, // # of GPUs used; can be fractional
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double cpu_frac, // fraction of FLOPS performed by CPU
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double flops_scale
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) {
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int cc = c.prop.major*100 + c.prop.minor;
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if (cc < min_cc) return false;
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if (max_cc && cc >= max_cc) return false;
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cuda_requirements.update(min_driver_version, min_ram);
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// Old BOINC clients report display driver version;
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// newer ones report CUDA RT version.
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// Some Linux doesn't return either.
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//
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if (!c.cuda_version && !c.display_driver_version) {
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return false;
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}
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if (c.cuda_version) {
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if (min_cuda_version && (c.cuda_version < min_cuda_version)) {
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return false;
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}
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}
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if (c.display_driver_version) {
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if (min_driver_version && (c.display_driver_version < min_driver_version)) {
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return false;
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}
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}
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if (c.prop.dtotalGlobalMem < min_ram) {
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return false;
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}
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hu.gpu_ram = min_ram;
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hu.ncudas = ndevs;
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coproc_perf(
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g_request->host.p_fpops,
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hu.ncudas*c.peak_flops,
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cpu_frac,
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hu.projected_flops,
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hu.avg_ncpus
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);
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hu.peak_flops = hu.ncudas*c.peak_flops + hu.avg_ncpus*g_request->host.p_fpops;
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hu.max_ncpus = hu.avg_ncpus;
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hu.projected_flops *= flops_scale;
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return true;
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}
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// the following is for an app that uses an NVIDIA GPU
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//
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static inline bool app_plan_cuda(
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SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
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) {
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COPROC_NVIDIA& c = sreq.coprocs.nvidia;
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if (!c.count) {
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return false;
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}
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// Macs require 6.10.28
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//
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if (strstr(sreq.host.os_name, "Darwin") && (sreq.core_client_version < 61028)) {
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return false;
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}
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// for CUDA 2.3, we need to check the CUDA RT version.
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// Old BOINC clients report display driver version;
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// newer ones report CUDA RT version
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//
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if (!strcmp(plan_class, "cuda_fermi")) {
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if (!cuda_check(c, hu,
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200, 0,
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CUDA3_MIN_CUDA_VERSION, CUDA3_MIN_DRIVER_VERSION,
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384*MEGA,
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1, .01, 1.02
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)) {
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return false;
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}
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} else if (!strcmp(plan_class, "cuda23")) {
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if (!cuda_check(c, hu,
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100,
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200, // change to zero if app is compiled to byte code
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CUDA23_MIN_CUDA_VERSION, CUDA23_MIN_DRIVER_VERSION,
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384*MEGA,
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1, .01, 1.01
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)) {
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return false;
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}
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} else if (!strcmp(plan_class, "cuda_opencl")) {
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if (!cuda_check(c, hu,
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100, 0,
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0, CUDA_OPENCL_MIN_DRIVER_VERSION,
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384*MEGA,
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1, .01, 1
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)) {
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return false;
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}
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} else if (!strcmp(plan_class, "cuda")) {
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if (!cuda_check(c, hu,
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100,
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200, // change to zero if app is compiled to byte code
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0, CUDA_MIN_DRIVER_VERSION,
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254*MEGA,
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1, .01, 1
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)) {
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return false;
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}
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} else {
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log_messages.printf(MSG_CRITICAL,
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"UNKNOWN PLAN CLASS %s\n", plan_class
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);
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return false;
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}
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if (config.debug_version_select) {
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log_messages.printf(MSG_NORMAL,
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"[version] %s app projected %.2fG peak %.2fG %.3f CPUs\n",
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plan_class,
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hu.projected_flops/1e9,
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hu.peak_flops/1e9,
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hu.avg_ncpus
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);
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}
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return true;
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}
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// The following is for a non-CPU-intensive application.
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// Say that we'll use 1% of a CPU.
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// This will cause the client (6.7+) to run it at non-idle priority
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//
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static inline bool app_plan_nci(
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SCHEDULER_REQUEST& sreq, HOST_USAGE& hu
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) {
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hu.avg_ncpus = .01;
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hu.max_ncpus = .01;
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hu.projected_flops = sreq.host.p_fpops*1.01;
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// The *1.01 is needed to ensure that we'll send this app
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// version rather than a non-plan-class one
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hu.peak_flops = sreq.host.p_fpops*.01;
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return true;
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}
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// the following is for an app version that requires a processor with SSE3,
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// and will run 10% faster than the non-SSE3 version
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//
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static inline bool app_plan_sse3(
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SCHEDULER_REQUEST& sreq, HOST_USAGE& hu
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) {
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downcase_string(sreq.host.p_features);
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if (!strstr(sreq.host.p_features, "sse3")) {
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// Pre-6.x clients report CPU features in p_model
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//
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if (!strstr(sreq.host.p_model, "sse3")) {
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//add_no_work_message("Your CPU lacks SSE3");
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return false;
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}
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}
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hu.avg_ncpus = 1;
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hu.max_ncpus = 1;
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hu.projected_flops = 1.1*sreq.host.p_fpops;
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hu.peak_flops = sreq.host.p_fpops;
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return true;
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}
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static inline bool app_plan_opencl_nvidia(
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SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
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) {
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return false;
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}
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static inline bool app_plan_opencl_ati(
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SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
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) {
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return false;
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}
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static inline bool app_plan_vbox32(SCHEDULER_REQUEST& sreq, HOST_USAGE& hu) {
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if (strlen(sreq.host.virtualbox_version) == 0) return false;
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int n, maj, min, rel;
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n = sscanf(sreq.host.virtualbox_version, "%d.%d.%d", &maj, &min, &rel);
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if (n != 3) return false;
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if (maj < 3) return false;
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if (maj == 3 and min < 2) return false;
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return true;
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}
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static inline bool app_plan_vbox64(SCHEDULER_REQUEST& sreq, HOST_USAGE& hu) {
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if (strlen(sreq.host.virtualbox_version) == 0) return false;
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if (!is_64b_platform(sreq.platform.name)) return false;
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int n, maj, min, rel;
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n = sscanf(sreq.host.virtualbox_version, "%d.%d.%d", &maj, &min, &rel);
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if (n != 3) return false;
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if (maj < 3) return false;
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if (maj == 3 and min < 2) return false;
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return true;
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}
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// app planning function.
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// See http://boinc.berkeley.edu/trac/wiki/AppPlan
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//
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bool app_plan(SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu) {
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if (!strcmp(plan_class, "mt")) {
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return app_plan_mt(sreq, hu);
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} else if (strstr(plan_class, "ati")) {
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return app_plan_ati(sreq, plan_class, hu);
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} else if (strstr(plan_class, "cuda")) {
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return app_plan_cuda(sreq, plan_class, hu);
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} else if (!strcmp(plan_class, "nci")) {
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return app_plan_nci(sreq, hu);
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} else if (!strcmp(plan_class, "sse3")) {
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return app_plan_sse3(sreq, hu);
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} else if (!strcmp(plan_class, "vbox32")) {
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return app_plan_vbox32(sreq, hu);
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} else if (!strcmp(plan_class, "vbox64")) {
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return app_plan_vbox64(sreq, hu);
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}
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log_messages.printf(MSG_CRITICAL,
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"Unknown plan class: %s\n", plan_class
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);
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return false;
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}
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// the following is used to enforce limits on in-progress jobs
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// for GPUs and CPUs (see handle_request.cpp)
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//
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bool app_plan_uses_gpu(const char* plan_class) {
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if (strstr(plan_class, "cuda")) {
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return true;
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}
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if (strstr(plan_class, "ati")) {
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return true;
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}
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return false;
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}
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// compute a "score" for sending this job to this host.
|
|
// Return false if the WU is infeasible.
|
|
// Otherwise set est_time and disk_usage.
|
|
//
|
|
bool JOB::get_score() {
|
|
WORKUNIT wu;
|
|
int retval;
|
|
|
|
WU_RESULT& wu_result = ssp->wu_results[index];
|
|
wu = wu_result.workunit;
|
|
app = ssp->lookup_app(wu.appid);
|
|
|
|
score = 0;
|
|
|
|
// Find the best app version to use.
|
|
//
|
|
bavp = get_app_version(wu, true, false);
|
|
if (!bavp) return false;
|
|
|
|
retval = wu_is_infeasible_fast(
|
|
wu, wu_result.res_server_state, wu_result.res_priority,
|
|
wu_result.res_report_deadline,
|
|
*app, *bavp
|
|
);
|
|
if (retval) {
|
|
if (config.debug_send) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[send] [HOST#%d] [WU#%d %s] WU is infeasible: %s\n",
|
|
g_reply->host.id, wu.id, wu.name, infeasible_string(retval)
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
score = 1;
|
|
|
|
#if 0
|
|
// example: for CUDA app, wu.batch is the minimum number of processors.
|
|
// add min/actual to score
|
|
// (this favors sending jobs that need lots of procs to GPUs that have them)
|
|
// IF YOU USE THIS, USE THE PART IN wu_is_infeasible_custom() ALSO
|
|
//
|
|
if (!strcmp(app->name, "foobar") && bavp->host_usage.ncudas) {
|
|
int n = g_request->coproc_cuda->prop.multiProcessorCount;
|
|
score += ((double)wu.batch)/n;
|
|
}
|
|
#endif
|
|
|
|
// check if user has selected apps,
|
|
// and send beta work to beta users
|
|
//
|
|
if (app->beta && !config.distinct_beta_apps) {
|
|
if (g_wreq->allow_beta_work) {
|
|
score += 1;
|
|
} else {
|
|
return false;
|
|
}
|
|
} else {
|
|
if (app_not_selected(wu)) {
|
|
if (!g_wreq->allow_non_preferred_apps) {
|
|
return false;
|
|
} else {
|
|
// Allow work to be sent, but it will not get a bump in its score
|
|
}
|
|
} else {
|
|
score += 1;
|
|
}
|
|
}
|
|
|
|
// if job needs to get done fast, send to fast/reliable host
|
|
//
|
|
if (bavp->reliable && (wu_result.need_reliable)) {
|
|
score += 1;
|
|
}
|
|
|
|
// if job already committed to an HR class,
|
|
// try to send to host in that class
|
|
//
|
|
if (wu_result.infeasible_count) {
|
|
score += 1;
|
|
}
|
|
|
|
// Favor jobs that will run fast
|
|
//
|
|
score += bavp->host_usage.projected_flops/1e9;
|
|
|
|
// match large jobs to fast hosts
|
|
//
|
|
if (config.job_size_matching) {
|
|
double host_stdev = (g_reply->host.p_fpops - ssp->perf_info.host_fpops_mean)/ ssp->perf_info.host_fpops_stdev;
|
|
double diff = host_stdev - wu_result.fpops_size;
|
|
score -= diff*diff;
|
|
}
|
|
|
|
// TODO: If user has selected some apps but will accept jobs from others,
|
|
// try to send them jobs from the selected apps
|
|
//
|
|
|
|
est_time = estimate_duration(wu, *bavp);
|
|
disk_usage = wu.rsc_disk_bound;
|
|
return true;
|
|
}
|