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boinc/sched/sched_customize.cpp

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// This file is part of BOINC.
// http://boinc.berkeley.edu
// Copyright (C) 2008 University of California
//
// BOINC is free software; you can redistribute it and/or modify it
// under the terms of the GNU Lesser General Public License
// as published by the Free Software Foundation,
// either version 3 of the License, or (at your option) any later version.
//
// BOINC is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
// See the GNU Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public License
// along with BOINC. If not, see <http://www.gnu.org/licenses/>.
//
// This file contains functions that can be customized to
// implement project-specific scheduling policies.
// The functions are:
//
// wu_is_infeasible_custom()
// Decide whether host can run a job using a particular app version.
// In addition it can:
// - set its resource usage and/or FLOPS estimate
// (by assigning to bav.host_usage)
// app_plan()
// Decide whether host can use an app version,
// and if so what resources it will use
// app_plan_uses_gpu():
// Which plan classes use GPUs
// JOB::get_score():
// Determine the value of sending a particular job to host;
// (used only by "matchmaker" scheduling)
//
// WARNING: if you modify this file, you must prevent it from
// being overwritten the next time you update BOINC source code.
// You can either:
// 1) write-protect this file, or
// 2) put this in a differently-named file and change the Makefile.am
// (and write-protect that)
// In either case, put your version under source-code control, e.g. SVN
#include "str_util.h"
#include "sched_config.h"
#include "sched_main.h"
#include "sched_msgs.h"
#include "sched_send.h"
#include "sched_score.h"
#include "sched_shmem.h"
#include "sched_version.h"
#include "sched_customize.h"
bool wu_is_infeasible_custom(WORKUNIT& wu, APP& app, BEST_APP_VERSION& bav) {
#if 0
// example: if WU name contains "_v1", don't use CUDA app
// Note: this is slightly suboptimal.
// If the host is able to accept both GPU and CPU jobs,
// we'll skip this job rather than send it for the CPU.
// Fixing this would require a big architectural change.
//
if (strstr(wu.name, "_v1") && bav.host_usage.ncudas) {
return true;
}
#endif
#if 0
// example: for CUDA app, wu.batch is the minimum number of processors.
// Don't send if #procs is less than this.
//
if (!strcmp(app.name, "foobar") && bav.host_usage.ncudas) {
int n = g_request->coproc_cuda->prop.multiProcessorCount;
if (n < wu.batch) {
return true;
}
}
#endif
#if 0
// example: if CUDA app and WU name contains "slow",
// cut performance estimate in half
//
if (bav.host_usage.ncudas) {
if (!strstr(wu.name, "slow")) {
bav.host_usage.flops = g_request->coproc_cuda->peak_flops()/10;
} else {
bav.host_usage.flops = g_request->coproc_cuda->peak_flops()/5;
}
}
#endif
return false;
}
#define PLAN_CUDA_MIN_DRIVER_VERSION 17700
#define PLAN_CUDA23_MIN_DRIVER_VERSION 19038
#define PLAN_CUDA_MIN_RAM (254.*1024*1024)
#define PLAN_CUDA23_MIN_RAM (384.*1024*1024)
bool app_plan(SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu) {
char buf[256];
if (!strcmp(plan_class, "mt")) {
// the following is for an app that:
// - can use from 1 to 64 threads, and can control this exactly
// - if it uses N threads, will use .65N cores on average
// (hence on a uniprocessor we'll use a sequential app
// if one is available)
//
double ncpus = g_wreq->effective_ncpus;
// number of usable CPUs, taking user prefs into account
int nthreads = (int)(ncpus/.65);
if (!nthreads) {
add_no_work_message("Your computer has too few CPUs");
return false;
}
if (nthreads > 64) nthreads = 64;
hu.avg_ncpus = nthreads*.65;
hu.max_ncpus = nthreads;
sprintf(hu.cmdline, "--nthreads %d", nthreads);
hu.flops = sreq.host.p_fpops*hu.avg_ncpus;
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] Multi-thread app estimate %.2f GFLOPS\n",
hu.flops/1e9
);
}
return true;
} else if (strstr(plan_class, "ati")) {
COPROC_ATI* cp = (COPROC_ATI*)sreq.coprocs.lookup("ATI");
if (!cp) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] Host lacks ATI GPU for plan class ati\n"
);
}
add_no_work_message("Your computer has no ATI GPU");
return false;
}
int major, minor, release;
sscanf(cp->version, "%d.%d.%d", &major, &minor, &release);
int vers = major*1000000 + minor*1000 + release;
double min_ram = 250*MEGA;
if (!strcmp(plan_class, "ati")) {
if (vers < 1000000) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] host has CAL version %s, need 1.0+\n",
cp->version
);
}
add_no_work_message("ATI Catalyst 8.12+ needed to use GPU");
return false;
}
if (!cp->amdrt_detected) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] ati libs found, need amd\n"
);
}
add_no_work_message("Need libraries named amd* to use ATI GPU");
return false;
}
}
if (!strcmp(plan_class, "ati13amd")) {
if (vers < 1003000) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] host has CAL version %s, need 1.3.0 to 1.3.186\n",
cp->version
);
}
add_no_work_message("ATI Catalyst 9.1+ needed to use GPU");
return false;
}
if (!cp->amdrt_detected) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] ati libs found, need amd\n"
);
}
add_no_work_message("Need libraries named amd* to use ATI GPU");
return false;
}
}
if (!strcmp(plan_class, "ati13ati")) {
if (vers < 1003186) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] host has CAL version %s, need 1.3.186+\n",
cp->version
);
}
add_no_work_message("ATI Catalyst 9.2+ needed to use GPU");
return false;
}
if (!cp->atirt_detected) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] amd libs found, need ati\n"
);
}
add_no_work_message("Need libraries named ati* to use ATI GPU");
return false;
}
}
if (!strcmp(plan_class, "ati14")) {
if (vers < 1004000) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] host has CAL version %s, need 1.4+\n",
cp->version
);
}
add_no_work_message("ATI Catalyst 9.7+ needed to use GPU");
return false;
}
if (!cp->atirt_detected) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] amd libs found, need ati\n"
);
}
add_no_work_message("Need libraries named ati* to use ATI GPU");
return false;
}
}
if (cp->attribs.localRAM*MEGA < min_ram) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] ATI mem %dMB < %d\n",
cp->attribs.localRAM, (int)(min_ram/MEGA)
);
}
sprintf(buf,
"Your ATI GPU has insufficient memory (need %.0fMB)",
min_ram/MEGA
);
add_no_work_message(buf);
return false;
}
hu.gpu_ram = 200*MEGA;
double cpu_frac; // the fraction of the app's FLOPS that are
// performed by the CPU
// (GPU is assumed to be idle then)
double gpu_effic; // when the app is using the GPU,
// fraction of GPU's peak FLOPS it gets
#if 1
// the following for an app that runs 99% on the GPU
cpu_frac = .01;
gpu_effic = .25;
#endif
#if 0
// the following for SETI@home Astropulse
cpu_frac = .75;
gpu_effic = .25;
#endif
double p = sreq.host.p_fpops;
double g = cp->peak_flops()/5;
hu.flops = p*g/(cpu_frac*g + (1-cpu_frac)*p);
double x = (cpu_frac*g)/(cpu_frac*g + (1-cpu_frac)*p);
hu.avg_ncpus = x;
hu.max_ncpus = x;
hu.natis = 1;
//hu.natis = .5; // you can use a fractional GPU if you want
// determine priority among variants of ATI
// 1. ati14
// 2. ati13ati
// 3. ati13amd
// 4. ati
if (!strcmp(plan_class, "ati13amd")) {
hu.flops *= 1.01;
}
if (!strcmp(plan_class, "ati13ati")) {
hu.flops *= 1.02;
}
if (!strcmp(plan_class, "ati14")) {
hu.flops *= 1.03;
}
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] ATI app estimated %.2f GFLOPS\n", hu.flops/1e9
);
}
return true;
} else if (strstr(plan_class, "cuda")) {
// the following is for an app that uses an NVIDIA GPU
//
COPROC_CUDA* cp = (COPROC_CUDA*)sreq.coprocs.lookup("CUDA");
if (!cp) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] Host lacks CUDA coprocessor for plan class cuda\n"
);
}
add_no_work_message("Your computer has no NVIDIA GPU");
return false;
}
// Macs require 6.10.28
//
if (strstr(sreq.host.os_name, "Darwin")) {
if (sreq.core_client_version < 61028) {
add_no_work_message(
"CUDA apps require BOINC version 6.10.28 or greater on Mac"
);
return false;
}
}
// check compute capability
//
int v = (cp->prop.major)*100 + cp->prop.minor;
if (v < 100) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] Compute capability %d < 1.0\n", v
);
}
add_no_work_message(
"Your NVIDIA GPU lacks the needed compute capability"
);
return false;
}
double min_ram;
// for CUDA 2.3, we need to check the CUDA RT version.
// Old BOINC clients report display driver version;
// newer ones report CUDA RT version
//
if (!strcmp(plan_class, "cuda_fermi")) {
int compute_capability = cp->prop.major*100 + cp->prop.minor;
if (compute_capability < 200) {
add_no_work_message("Fermi-class GPU needed");
return false;
}
if (cp->cuda_version < 3000) {
add_no_work_message("CUDA version 2.3 needed");
return false;
}
min_ram = PLAN_CUDA23_MIN_RAM;
} else if (!strcmp(plan_class, "cuda23")) {
if (cp->cuda_version) {
if (cp->cuda_version < 2030) {
add_no_work_message("CUDA version 2.3 needed");
return false;
}
} else if (cp->display_driver_version) {
if (cp->display_driver_version < PLAN_CUDA23_MIN_DRIVER_VERSION) {
sprintf(buf, "NVIDIA display driver %d or later needed",
PLAN_CUDA23_MIN_DRIVER_VERSION
);
add_no_work_message(buf);
return false;
}
} else {
// pre-6.10 Linux clients report neither CUDA nor driver
// version; they'll end up here
//
add_no_work_message("CUDA version 2.3 needed");
return false;
}
min_ram = PLAN_CUDA23_MIN_RAM;
} else {
if (cp->display_driver_version && cp->display_driver_version < PLAN_CUDA_MIN_DRIVER_VERSION) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] NVIDIA driver version %d < PLAN_CUDA_MIN_DRIVER_VERSION\n",
cp->display_driver_version
);
}
sprintf(buf, "NVIDIA driver version %d or later needed",
PLAN_CUDA_MIN_DRIVER_VERSION
);
add_no_work_message(buf);
return false;
}
min_ram = PLAN_CUDA_MIN_RAM;
}
if (cp->prop.dtotalGlobalMem < min_ram) {
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] CUDA mem %d < %d\n",
cp->prop.dtotalGlobalMem, min_ram
);
}
sprintf(buf,
"Your NVIDIA GPU has insufficient memory (need %.0fMB)",
min_ram/MEGA
);
add_no_work_message(buf);
return false;
}
hu.gpu_ram = min_ram - 16*MEGA;
double cpu_frac; // the fraction of the app's FLOPS that are
// performed by the CPU
// (GPU is assumed to be idle then)
double gpu_effic; // when the app is using the GPU,
// fraction of GPU's peak FLOPS it gets
#if 1
// the following for an app that runs 99% on the GPU
cpu_frac = .01;
gpu_effic = .25;
#endif
#if 0
cpu_frac = .75;
gpu_effic = .25;
#endif
double p = sreq.host.p_fpops;
double g = cp->peak_flops()/5;
hu.flops = p*g/(cpu_frac*g + (1-cpu_frac)*p);
double x = (cpu_frac*g)/(cpu_frac*g + (1-cpu_frac)*p);
hu.avg_ncpus = x;
hu.max_ncpus = x;
hu.ncudas = 1;
//hu.ncudas = .5; // you can use a fractional GPU if you want
if (!strcmp(plan_class, "cuda23")) {
hu.flops *= 1.01;
}
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] CUDA app estimated %.2f GFLOPS (clock %d count %d)\n",
hu.flops/1e9, cp->prop.clockRate,
cp->prop.multiProcessorCount
);
}
return true;
} else if (!strcmp(plan_class, "nci")) {
// The following is for a non-CPU-intensive application.
// Say that we'll use 1% of a CPU.
// This will cause the client (6.7+) to run it at non-idle priority
//
hu.avg_ncpus = .01;
hu.max_ncpus = .01;
hu.flops = sreq.host.p_fpops*1.01;
// The *1.01 is needed to ensure that we'll send this app
// version rather than a non-plan-class one
return true;
} else if (!strcmp(plan_class, "sse3")) {
// the following is for an app that requires a processor with SSE3,
// and will run 10% faster if so
//
downcase_string(sreq.host.p_features);
if (!strstr(sreq.host.p_features, "sse3")) {
add_no_work_message("Your CPU lacks SSE3");
return false;
}
hu.avg_ncpus = 1;
hu.max_ncpus = 1;
hu.flops = 1.1*sreq.host.p_fpops;
return true;
}
log_messages.printf(MSG_CRITICAL,
"Unknown plan class: %s\n", plan_class
);
return false;
}
// the following is used to enforce limits on in-progress jobs
// for GPUs and CPUs (see handle_request.cpp)
//
bool app_plan_uses_gpu(const char* plan_class) {
if (strstr(plan_class, "cuda")) {
return true;
}
if (strstr(plan_class, "ati")) {
return true;
}
return false;
}
// 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);
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 (g_wreq->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.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;
}
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