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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 the app version's resource usage and/or FLOPS rate estimate
// (by assigning to bav.host_usage)
// - modify command-line args
// (by assigning to bav.host_usage.cmdline)
// - set the job's FLOPS count
// (by assigning to wu.rsc_fpops_est)
//
// 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 <string>
using std::string;
#include "str_util.h"
#include "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 ".vlar", don't send
//
if (bav.host_usage.ncudas) {
if (strstr(wu.name, ".vlar")) {
return true;
}
}
#endif
return false;
}
// Suppose we have a computation that uses two devices alternately.
// The devices have speeds s1 and s2.
// The fraction of work done on device 1 is frac.
//
// This function returns:
// 1) the overall speed
// 2) the utilization of device 1, which is always in (0, 1).
//
static inline void coproc_perf(
double s1, double s2, double frac,
double& speed, double& u1
) {
double y = (frac*s2 + (1-frac)*s1);
speed = s1*s2/y;
// do the math
u1 = frac*s2/y;
}
// the following is for an app that can use anywhere from 1 to 64 threads
//
static inline bool app_plan_mt(SCHEDULER_REQUEST&, HOST_USAGE& hu) {
double ncpus = g_wreq->effective_ncpus;
// number of usable CPUs, taking user prefs into account
if (ncpus < 2) return false;
int nthreads = (int)ncpus;
if (nthreads > 64) nthreads = 64;
hu.avg_ncpus = nthreads;
hu.max_ncpus = nthreads;
sprintf(hu.cmdline, "--nthreads %d", nthreads);
hu.projected_flops = capped_host_fpops()*hu.avg_ncpus*.99;
// the .99 ensures that on uniprocessors a sequential app
// will be used in preferences to this
hu.peak_flops = capped_host_fpops()*hu.avg_ncpus;
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] Multi-thread app projected %.2fGS\n",
hu.projected_flops/1e9
);
}
return true;
}
GPU_REQUIREMENTS ati_requirements;
static bool ati_check(COPROC_ATI& c, HOST_USAGE& hu,
int min_driver_version,
bool need_amd_libs,
double min_ram,
double ndevs, // # of GPUs used; can be fractional
double cpu_frac, // fraction of FLOPS performed by CPU
double flops_scale
) {
ati_requirements.update(min_driver_version, min_ram);
if (need_amd_libs) {
if (!c.amdrt_detected) {
return false;
}
} else {
if (!c.atirt_detected) {
return false;
}
}
if (c.version_num < min_driver_version) {
return false;
}
if (c.available_ram < min_ram) {
return false;
}
hu.gpu_ram = min_ram;
hu.natis = ndevs;
coproc_perf(
capped_host_fpops(),
flops_scale * hu.natis*c.peak_flops,
cpu_frac,
hu.projected_flops,
hu.avg_ncpus
);
hu.peak_flops = hu.natis*c.peak_flops + hu.avg_ncpus*capped_host_fpops();
hu.max_ncpus = hu.avg_ncpus;
return true;
}
#define ATI_MIN_RAM 250*MEGA
static inline bool app_plan_ati(
SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
) {
COPROC_ATI& c = sreq.coprocs.ati;
if (!c.count) {
return false;
}
if (!strcmp(plan_class, "ati")) {
if (!ati_check(c, hu,
ati_version_int(1, 0, 0),
true,
ATI_MIN_RAM,
1,
.01,
.20
)) {
return false;
}
}
if (!strcmp(plan_class, "ati13amd")) {
if (!ati_check(c, hu,
ati_version_int(1, 3, 0),
true,
ATI_MIN_RAM,
1, .01,
.21
)) {
return false;
}
}
if (!strcmp(plan_class, "ati13ati")) {
if (!ati_check(c, hu,
ati_version_int(1, 3, 186),
false,
ATI_MIN_RAM,
1, .01,
.22
)) {
return false;
}
}
if (!strcmp(plan_class, "ati14")) {
if (!ati_check(c, hu,
ati_version_int(1, 4, 0),
false,
ATI_MIN_RAM,
1, .01,
.23
)) {
return false;
}
}
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] %s ATI app projected %.2fG peak %.2fG %.3f CPUs\n",
plan_class,
hu.projected_flops/1e9,
hu.peak_flops/1e9,
hu.avg_ncpus
);
}
return true;
}
GPU_REQUIREMENTS cuda_requirements;
#define CUDA_MIN_DRIVER_VERSION 17700
#define CUDA23_MIN_CUDA_VERSION 2030
#define CUDA23_MIN_DRIVER_VERSION 19038
#define CUDA3_MIN_CUDA_VERSION 3000
#define CUDA3_MIN_DRIVER_VERSION 19500
#define CUDA_OPENCL_MIN_DRIVER_VERSION 19713
static bool cuda_check(COPROC_NVIDIA& c, HOST_USAGE& hu,
int min_cc, int max_cc,
int min_cuda_version, int min_driver_version,
double min_ram,
double ndevs, // # of GPUs used; can be fractional
double cpu_frac, // fraction of FLOPS performed by CPU
double flops_scale
) {
int cc = c.prop.major*100 + c.prop.minor;
if (cc < min_cc) return false;
if (max_cc && cc >= max_cc) return false;
cuda_requirements.update(min_driver_version, min_ram);
// Old BOINC clients report display driver version;
// newer ones report CUDA RT version.
// Some Linux doesn't return either.
//
if (!c.cuda_version && !c.display_driver_version) {
return false;
}
if (c.cuda_version) {
if (min_cuda_version && (c.cuda_version < min_cuda_version)) {
return false;
}
}
if (c.display_driver_version) {
if (min_driver_version && (c.display_driver_version < min_driver_version)) {
return false;
}
}
if (c.available_ram < min_ram) {
return false;
}
hu.gpu_ram = min_ram;
hu.ncudas = ndevs;
coproc_perf(
capped_host_fpops(),
flops_scale * hu.ncudas*c.peak_flops,
cpu_frac,
hu.projected_flops,
hu.avg_ncpus
);
hu.peak_flops = hu.ncudas*c.peak_flops + hu.avg_ncpus*capped_host_fpops();
hu.max_ncpus = hu.avg_ncpus;
return true;
}
// the following is for an app that uses an NVIDIA GPU
//
static inline bool app_plan_cuda(
SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
) {
COPROC_NVIDIA& c = sreq.coprocs.nvidia;
if (!c.count) {
return false;
}
// Macs require 6.10.28
//
if (strstr(sreq.host.os_name, "Darwin") && (sreq.core_client_version < 61028)) {
return false;
}
// 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")) {
if (!cuda_check(c, hu,
200, 0,
CUDA3_MIN_CUDA_VERSION, CUDA3_MIN_DRIVER_VERSION,
384*MEGA,
1,
.01,
.22
)) {
return false;
}
} else if (!strcmp(plan_class, "cuda23")) {
if (!cuda_check(c, hu,
100,
200, // change to zero if app is compiled to byte code
CUDA23_MIN_CUDA_VERSION, CUDA23_MIN_DRIVER_VERSION,
384*MEGA,
1,
.01,
.21
)) {
return false;
}
} else if (!strcmp(plan_class, "cuda")) {
if (!cuda_check(c, hu,
100,
200, // change to zero if app is compiled to byte code
0, CUDA_MIN_DRIVER_VERSION,
254*MEGA,
1,
.01,
.20
)) {
return false;
}
} else {
log_messages.printf(MSG_CRITICAL,
"UNKNOWN PLAN CLASS %s\n", plan_class
);
return false;
}
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] %s app projected %.2fG peak %.2fG %.3f CPUs\n",
plan_class,
hu.projected_flops/1e9,
hu.peak_flops/1e9,
hu.avg_ncpus
);
}
return true;
}
// 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
//
static inline bool app_plan_nci(SCHEDULER_REQUEST&, HOST_USAGE& hu) {
hu.avg_ncpus = .01;
hu.max_ncpus = .01;
hu.projected_flops = capped_host_fpops()*1.01;
// The *1.01 is needed to ensure that we'll send this app
// version rather than a non-plan-class one
hu.peak_flops = capped_host_fpops()*.01;
return true;
}
// the following is for an app version that requires a processor with SSE3,
// and will run 10% faster than the non-SSE3 version
//
static inline bool app_plan_sse3(
SCHEDULER_REQUEST& sreq, HOST_USAGE& hu
) {
downcase_string(sreq.host.p_features);
if (!strstr(sreq.host.p_features, "sse3")) {
// Pre-6.x clients report CPU features in p_model
//
if (!strstr(sreq.host.p_model, "sse3")) {
//add_no_work_message("Your CPU lacks SSE3");
return false;
}
}
hu.avg_ncpus = 1;
hu.max_ncpus = 1;
hu.projected_flops = 1.1*capped_host_fpops();
hu.peak_flops = capped_host_fpops();
return true;
}
static inline bool opencl_check(
COPROC& cp, HOST_USAGE& hu,
int min_opencl_device_version,
double min_global_mem_size,
double ndevs,
double cpu_frac,
double flops_scale
) {
if (cp.opencl_prop.opencl_device_version_int < min_opencl_device_version) {
return false;
}
if (cp.opencl_prop.global_mem_size < min_global_mem_size) {
return false;
}
hu.gpu_ram = min_global_mem_size;
if (!strcmp(cp.type, "NVIDIA")) {
hu.ncudas = ndevs;
} else if (!strcmp(cp.type, "ATI")) {
hu.natis = ndevs;
}
coproc_perf(
capped_host_fpops(),
flops_scale * ndevs * cp.peak_flops,
cpu_frac,
hu.projected_flops,
hu.avg_ncpus
);
hu.peak_flops = ndevs*cp.peak_flops + hu.avg_ncpus*capped_host_fpops();
hu.max_ncpus = hu.avg_ncpus;
return true;
}
static inline bool app_plan_opencl(
SCHEDULER_REQUEST& sreq, const char* plan_class, HOST_USAGE& hu
) {
if (strstr(plan_class, "nvidia")) {
COPROC_NVIDIA& c = sreq.coprocs.nvidia;
if (!c.count) return false;
if (!c.have_opencl) return false;
if (!strcmp(plan_class, "opencl_nvidia_101")) {
return opencl_check(
c, hu,
101,
256*MEGA,
1,
.1,
.2
);
} else {
log_messages.printf(MSG_CRITICAL,
"Unknown plan class: %s\n", plan_class
);
return false;
}
} else if (strstr(plan_class, "ati")) {
COPROC_ATI& c = sreq.coprocs.ati;
if (!c.count) return false;
if (!c.have_opencl) return false;
if (!strcmp(plan_class, "opencl_ati_101")) {
return opencl_check(
c, hu,
101,
256*MEGA,
1,
.1,
.2
);
} else {
log_messages.printf(MSG_CRITICAL,
"Unknown plan class: %s\n", plan_class
);
return false;
}
// maybe add a clause for multicore CPU
} else {
log_messages.printf(MSG_CRITICAL,
"Unknown plan class: %s\n", plan_class
);
return false;
}
}
// handles vbox_[32|64][_mt]
// "mt" is tailored to the needs of CERN:
// use 1 or 2 CPUs
static inline bool app_plan_vbox(
SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
) {
bool can_use_multicore = true;
// host must have VirtualBox 3.2 or later
//
if (strlen(sreq.host.virtualbox_version) == 0) return false;
int n, maj, min, rel;
n = sscanf(sreq.host.virtualbox_version, "%d.%d.%d", &maj, &min, &rel);
if (n != 3) return false;
if (maj < 3) return false;
if (maj == 3 and min < 2) return false;
// host must have VM acceleration in order to run multi-core jobs
//
if (strstr(plan_class, "mt")) {
if ((!strstr(sreq.host.p_features, "vmx") && !strstr(sreq.host.p_features, "svm"))
|| sreq.host.p_vm_extensions_disabled
) {
can_use_multicore = false;
}
}
// only send the version for host's primary platform.
// A Win64 host can't run a 32-bit VM app:
// it will look in the 32-bit half of the registry and fail
//
PLATFORM* p = g_request->platforms.list[0];
if (is_64b_platform(p->name)) {
if (!strstr(plan_class, "64")) return false;
} else {
if (strstr(plan_class, "64")) return false;
}
double flops_scale = 1;
hu.avg_ncpus = 1;
hu.max_ncpus = 1;
if (strstr(plan_class, "mt")) {
if (can_use_multicore) {
// Use number of usable CPUs, taking user prefs into account
double ncpus = g_wreq->effective_ncpus;
// CernVM on average uses between 25%-50% of a second core
// Total on a dual-core machine is between 65%-75%
if (ncpus > 1.5) ncpus = 1.5;
hu.avg_ncpus = ncpus;
hu.max_ncpus = 2.0;
sprintf(hu.cmdline, "--nthreads %f", ncpus);
}
// use the non-mt version rather than the mt version with 1 CPU
//
flops_scale = .99;
}
hu.projected_flops = flops_scale * capped_host_fpops()*hu.avg_ncpus;
hu.peak_flops = capped_host_fpops()*hu.max_ncpus;
if (config.debug_version_select) {
log_messages.printf(MSG_NORMAL,
"[version] %s app projected %.2fG\n",
plan_class, hu.projected_flops/1e9
);
}
return true;
}
// app planning function.
// See http://boinc.berkeley.edu/trac/wiki/AppPlan
//
bool app_plan(SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu) {
if (!strcmp(plan_class, "mt")) {
return app_plan_mt(sreq, hu);
} else if (strstr(plan_class, "opencl")) {
return app_plan_opencl(sreq, plan_class, hu);
} else if (strstr(plan_class, "ati")) {
return app_plan_ati(sreq, plan_class, hu);
} else if (strstr(plan_class, "cuda")) {
return app_plan_cuda(sreq, plan_class, hu);
} else if (!strcmp(plan_class, "nci")) {
return app_plan_nci(sreq, hu);
} else if (!strcmp(plan_class, "sse3")) {
return app_plan_sse3(sreq, hu);
} else if (strstr(plan_class, "vbox")) {
return app_plan_vbox(sreq, plan_class, hu);
}
log_messages.printf(MSG_CRITICAL,
"Unknown plan class: %s\n", plan_class
);
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, 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 = (capped_host_fpops() - ssp->perf_info.host_fpops_mean)/ ssp->perf_info.host_fpops_stddev;
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;
}
void handle_file_xfer_results() {
for (unsigned int i=0; i<g_request->file_xfer_results.size(); i++) {
RESULT& r = g_request->file_xfer_results[i];
log_messages.printf(MSG_NORMAL,
"completed file xfer %s\n", r.name
);
g_reply->result_acks.push_back(string(r.name));
}
}
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