mirror of https://github.com/BOINC/boinc.git
1135 lines
34 KiB
C++
1135 lines
34 KiB
C++
// 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 "config.h"
|
|
|
|
#include <string>
|
|
|
|
using std::string;
|
|
|
|
#include "str_util.h"
|
|
#include "util.h"
|
|
|
|
#include "sched_check.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"
|
|
#include "plan_class_spec.h"
|
|
|
|
#ifndef ATI_MIN_RAM
|
|
#define ATI_MIN_RAM 256*MEGA
|
|
#endif
|
|
|
|
#ifndef OPENCL_ATI_MIN_RAM
|
|
#define OPENCL_ATI_MIN_RAM 256*MEGA
|
|
#endif
|
|
|
|
#ifndef OPENCL_INTEL_GPU_MIN_RAM
|
|
#define OPENCL_INTEL_GPU_MIN_RAM 256*MEGA
|
|
#endif
|
|
|
|
#ifndef CUDA_MIN_RAM
|
|
#define CUDA_MIN_RAM 256*MEGA
|
|
#endif
|
|
|
|
#ifndef CUDAFERMI_MIN_RAM
|
|
#define CUDAFERMI_MIN_RAM 384*MEGA
|
|
#endif
|
|
|
|
#ifndef CUDA23_MIN_RAM
|
|
#define CUDA23_MIN_RAM 384*MEGA
|
|
#endif
|
|
|
|
#ifndef OPENCL_NVIDIA_MIN_RAM
|
|
#define OPENCL_NVIDIA_MIN_RAM CUDA_MIN_RAM
|
|
#endif
|
|
|
|
GPU_REQUIREMENTS gpu_requirements[NPROC_TYPES];
|
|
|
|
bool wu_is_infeasible_custom(WORKUNIT& wu, APP& app, BEST_APP_VERSION& bav) {
|
|
#if 0
|
|
// example: if WU name contains "_v1", don't use GPU apps.
|
|
// 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.uses_gpu()) {
|
|
return true;
|
|
}
|
|
#endif
|
|
#if 0
|
|
// example: for NVIDIA GPU app,
|
|
// wu.batch is the minimum number of GPU processors.
|
|
// Don't send if #procs is less than this.
|
|
//
|
|
if (!strcmp(app.name, "foobar") && bav.host_usage.proc_type == PROC_TYPE_NVIDIA_GPU) {
|
|
int n = g_request->coprocs.nvidia.prop.multiProcessorCount;
|
|
if (n < wu.batch) {
|
|
return true;
|
|
}
|
|
}
|
|
#endif
|
|
#if defined(SETIATHOME)
|
|
bool infeasible=false;
|
|
static bool send_vlar_to_gpu=false;
|
|
static bool sah_config_checked=false;
|
|
char buff[256];
|
|
|
|
// check the projects app config whether to send vlar wus to gpus
|
|
if (!sah_config_checked) {
|
|
MIOFILE mf;
|
|
XML_PARSER xp(&mf);
|
|
#ifndef _USING_FCGI_
|
|
FILE *f=fopen(config.project_path("sah_config.xml"),"r");
|
|
#else
|
|
FCGI_FILE *f=FCGI::fopen(config.project_path("sah_config.xml"),"r");
|
|
#endif
|
|
if (f) {
|
|
mf.init_file(f);
|
|
if (xp.parse_start("sah") && xp.parse_start("config")) {
|
|
while (!xp.get_tag()) {
|
|
if (!xp.is_tag) continue;
|
|
if (xp.parse_bool("send_vlar_to_gpu",send_vlar_to_gpu)) continue;
|
|
if (xp.match_tag("/config")) break;
|
|
xp.skip_unexpected(false, "wu_is_infeasible_custom");
|
|
}
|
|
}
|
|
fclose(f);
|
|
}
|
|
sah_config_checked=true;
|
|
}
|
|
// example: if CUDA app and WU name contains ".vlar", don't send
|
|
// to NVIDIA, INTEL or older ATI cards
|
|
//
|
|
if (bav.host_usage.uses_gpu() && strstr(wu.name, ".vlar")) {
|
|
if (send_vlar_to_gpu) {
|
|
if (bav.host_usage.proc_type == PROC_TYPE_AMD_GPU) {
|
|
// ATI GPUs older than HD7870
|
|
COPROC_ATI &cp = g_request->coprocs.ati;
|
|
if (cp.count && (cp.attribs.target < 15)) {
|
|
infeasible=true;
|
|
}
|
|
} else if (bav.host_usage.proc_type == PROC_TYPE_NVIDIA_GPU) {
|
|
COPROC_NVIDIA &cp = g_request->coprocs.nvidia;
|
|
if (cp.count) {
|
|
int v = (cp.prop.major)*100 + cp.prop.minor;
|
|
if (v < 300) {
|
|
infeasible=true;
|
|
}
|
|
}
|
|
} else {
|
|
// all other GPUS
|
|
infeasible=true;
|
|
}
|
|
} else {
|
|
infeasible=true;
|
|
}
|
|
}
|
|
if (infeasible && config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [setiathome] VLAR workunit is infeasible on this GPU\n"
|
|
);
|
|
}
|
|
return infeasible;
|
|
#endif
|
|
return false;
|
|
}
|
|
|
|
#ifndef isnum
|
|
#define isnum(x) (((x)>='0') && ((x)<='9'))
|
|
#endif
|
|
|
|
#ifndef isnumorx
|
|
#define isnumorx(x) (isnum(x) || ((x=='X') || (x=='x')))
|
|
#endif
|
|
|
|
// 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;
|
|
}
|
|
|
|
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,
|
|
int min_hd_model=0
|
|
) {
|
|
if (c.version_num) {
|
|
gpu_requirements[PROC_TYPE_AMD_GPU].update(min_driver_version, min_ram);
|
|
}
|
|
|
|
if (min_hd_model) {
|
|
char *p=strcasestr(c.name,"hd");
|
|
if (p) {
|
|
p+=2;
|
|
while (p && !isnum(*p)) p++;
|
|
char modelnum[64];
|
|
int i=0;
|
|
while ((i<63) && p[i] && isnumorx(p[i])) {
|
|
modelnum[i]=p[i];
|
|
if ((modelnum[i]=='x') || (modelnum[i]=='X')) {
|
|
modelnum[i]='0';
|
|
}
|
|
i++;
|
|
}
|
|
modelnum[i]=0;
|
|
i=atoi(modelnum);
|
|
if (i<min_hd_model) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Requires ATI HD%4d+. Found HD%4d\n",
|
|
min_hd_model, i
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
if (need_amd_libs) {
|
|
if (!c.amdrt_detected) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] AMD run time libraries not found\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
} else {
|
|
if (!c.atirt_detected) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] ATI run time libraries not found\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
}
|
|
if (c.version_num < min_driver_version) {
|
|
if (config.debug_version_select) {
|
|
int app_major=min_driver_version/10000000;
|
|
int app_minor=(min_driver_version%10000000)/10000;
|
|
int app_rev=(min_driver_version%10000);
|
|
int dev_major=c.version_num/10000000;
|
|
int dev_minor=(c.version_num%10000000)/10000;
|
|
int dev_rev=(c.version_num%10000);
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Bad display driver revision %d.%d.%d<%d.%d.%d.\n",
|
|
dev_major,dev_minor,dev_rev,app_major,app_minor,app_rev
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
if (c.available_ram < min_ram) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Insufficient GPU RAM %f>%f.\n",
|
|
min_ram, c.available_ram
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
hu.gpu_ram = min_ram;
|
|
hu.proc_type = PROC_TYPE_AMD_GPU;
|
|
hu.gpu_usage = ndevs;
|
|
|
|
coproc_perf(
|
|
capped_host_fpops(),
|
|
flops_scale * hu.gpu_usage*c.peak_flops,
|
|
cpu_frac,
|
|
hu.projected_flops,
|
|
hu.avg_ncpus
|
|
);
|
|
hu.peak_flops = hu.gpu_usage*c.peak_flops + hu.avg_ncpus*capped_host_fpops();
|
|
hu.max_ncpus = hu.avg_ncpus;
|
|
return true;
|
|
}
|
|
|
|
static inline bool app_plan_ati(
|
|
SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
|
|
) {
|
|
COPROC_ATI& c = sreq.coprocs.ati;
|
|
if (!c.count) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,"[version] Host has no ATI GPUs\n");
|
|
}
|
|
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;
|
|
}
|
|
}
|
|
|
|
#ifdef SETIATHOME
|
|
// ati_opencl_<ver> plan classes are for running
|
|
// opencl ati apps on pre-v7 boinc core clients
|
|
if (!strcmp(plan_class, "ati_opencl_100")) {
|
|
if (!ati_check(c, hu,
|
|
ati_version_int(1, 4, 1386),
|
|
false,
|
|
OPENCL_ATI_MIN_RAM,
|
|
1, .01,
|
|
.14,
|
|
4600
|
|
)) {
|
|
return false;
|
|
}
|
|
}
|
|
#endif
|
|
|
|
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;
|
|
}
|
|
|
|
// Change values for these parameters in shed_customize.h!
|
|
#ifndef CUDA_MIN_DRIVER_VERSION
|
|
#define CUDA_MIN_DRIVER_VERSION 17700
|
|
#endif
|
|
|
|
#ifndef CUDA23_MIN_CUDA_VERSION
|
|
#define CUDA23_MIN_CUDA_VERSION 2030
|
|
#endif
|
|
|
|
#ifndef CUDA23_MIN_DRIVER_VERSION
|
|
#define CUDA23_MIN_DRIVER_VERSION 19038
|
|
#endif
|
|
|
|
#ifndef CUDA3_MIN_CUDA_VERSION
|
|
#define CUDA3_MIN_CUDA_VERSION 3000
|
|
#endif
|
|
|
|
#ifndef CUDA3_MIN_DRIVER_VERSION
|
|
#define CUDA3_MIN_DRIVER_VERSION 19500
|
|
#endif
|
|
|
|
#ifndef CUDA_OPENCL_MIN_DRIVER_VERSION
|
|
#define CUDA_OPENCL_MIN_DRIVER_VERSION 19713
|
|
#endif
|
|
|
|
#ifndef CUDA_OPENCL_101_MIN_DRIVER_VERSION
|
|
#define CUDA_OPENCL_101_MIN_DRIVER_VERSION 28013
|
|
#endif
|
|
|
|
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 (min_cc && (cc < min_cc)) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] App requires compute capability > %d.%d (has %d.%d).\n",
|
|
min_cc/100,min_cc%100,
|
|
c.prop.major,c.prop.minor
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
if (max_cc && cc >= max_cc) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] App requires compute capability <= %d.%d (has %d.%d).\n",
|
|
max_cc/100,max_cc%100,
|
|
c.prop.major,c.prop.minor
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
if (c.display_driver_version) {
|
|
gpu_requirements[PROC_TYPE_NVIDIA_GPU].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) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Client did not provide cuda or driver version.\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
if (c.cuda_version) {
|
|
if (min_cuda_version && (c.cuda_version < min_cuda_version)) {
|
|
if (config.debug_version_select) {
|
|
double app_version=(double)(min_cuda_version/1000)+(double)(min_cuda_version%100)/100.0;
|
|
double client_version=(double)(c.cuda_version/1000)+(double)(c.cuda_version%100)/100.0;
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Bad CUDA version %f>%f.\n",
|
|
app_version, client_version
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
}
|
|
if (c.display_driver_version) {
|
|
if (min_driver_version && (c.display_driver_version < min_driver_version)) {
|
|
if (config.debug_version_select) {
|
|
double app_version=(double)(min_driver_version)/100.0;
|
|
double client_version=(double)(c.display_driver_version)/100.0;
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Bad display driver revision %f>%f.\n",
|
|
app_version, client_version
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
}
|
|
if (c.available_ram < min_ram) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Insufficient GPU RAM %f>%f.\n",
|
|
min_ram, c.available_ram
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
hu.gpu_ram = min_ram;
|
|
hu.proc_type = PROC_TYPE_NVIDIA_GPU;
|
|
hu.gpu_usage = ndevs;
|
|
|
|
coproc_perf(
|
|
capped_host_fpops(),
|
|
flops_scale * hu.gpu_usage*c.peak_flops,
|
|
cpu_frac,
|
|
hu.projected_flops,
|
|
hu.avg_ncpus
|
|
);
|
|
hu.peak_flops = hu.gpu_usage*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_nvidia(
|
|
SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu
|
|
) {
|
|
COPROC_NVIDIA& c = sreq.coprocs.nvidia;
|
|
if (!c.count) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Host has no NVIDIA GPUs.\n");
|
|
}
|
|
return false;
|
|
}
|
|
|
|
// Macs require 6.10.28
|
|
//
|
|
if (strstr(sreq.host.os_name, "Darwin") && (sreq.core_client_version < 61028)) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] CUDA on MacOS requires BOINC 6.10.28 or higher.\n");
|
|
}
|
|
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
|
|
#ifdef SETIATHOME
|
|
// cuda_opencl_<ver> plan classes are for running opencl apps on
|
|
// pre-boinc-v7 core clients. May be useful for other projects
|
|
//
|
|
if (!strcmp(plan_class, "cuda_opencl_100")) {
|
|
if (!cuda_check(c, hu,
|
|
100, 0,
|
|
0,CUDA_OPENCL_MIN_DRIVER_VERSION,
|
|
CUDA_MIN_RAM,
|
|
1,
|
|
.01,
|
|
0.14
|
|
)) {
|
|
return false;
|
|
}
|
|
} else if (!strcmp(plan_class, "cuda_opencl_101")) {
|
|
if (!cuda_check(c, hu,
|
|
200, 0,
|
|
0,CUDA_OPENCL_101_MIN_DRIVER_VERSION,
|
|
CUDA_MIN_RAM,
|
|
1,
|
|
.01,
|
|
0.14
|
|
)) {
|
|
return false;
|
|
}
|
|
} else
|
|
#endif // SETIATHOME
|
|
if (!strcmp(plan_class, "cuda_fermi")) {
|
|
if (!cuda_check(c, hu,
|
|
200, 0,
|
|
CUDA3_MIN_CUDA_VERSION, CUDA3_MIN_DRIVER_VERSION,
|
|
CUDAFERMI_MIN_RAM,
|
|
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,
|
|
CUDA23_MIN_RAM,
|
|
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,
|
|
CUDA_MIN_RAM,
|
|
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) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [opencl_check] App requires OpenCL verion >= %d (has %d).\n",
|
|
min_opencl_device_version,
|
|
cp.opencl_prop.opencl_device_version_int
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
#ifdef SETIATHOME
|
|
// fix for ATI drivers that report zero or negative global memory size
|
|
// on some cards. Probably no longer necessary.
|
|
if (cp.opencl_prop.global_mem_size < cp.opencl_prop.local_mem_size) {
|
|
cp.opencl_prop.global_mem_size=cp.opencl_prop.local_mem_size;
|
|
}
|
|
#endif
|
|
|
|
if (cp.opencl_prop.global_mem_size && (cp.opencl_prop.global_mem_size < min_global_mem_size)) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [opencl_check] Insufficient GPU RAM %f>%ld.\n",
|
|
min_global_mem_size, cp.opencl_prop.global_mem_size
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
hu.gpu_ram = min_global_mem_size;
|
|
if (!strcmp(cp.type, proc_type_name_xml(PROC_TYPE_NVIDIA_GPU))) {
|
|
hu.proc_type = PROC_TYPE_NVIDIA_GPU;
|
|
hu.gpu_usage = ndevs;
|
|
} else if (!strcmp(cp.type, proc_type_name_xml(PROC_TYPE_AMD_GPU))) {
|
|
hu.proc_type = PROC_TYPE_AMD_GPU;
|
|
hu.gpu_usage = ndevs;
|
|
} else if (!strcmp(cp.type, proc_type_name_xml(PROC_TYPE_INTEL_GPU))) {
|
|
hu.proc_type = PROC_TYPE_INTEL_GPU;
|
|
hu.gpu_usage = 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
|
|
) {
|
|
// opencl_*_<ver> plan classes check for a trailing integer which is
|
|
// used as the opencl version number. This is compatible with the old
|
|
// opencl_nvidia_101 and opencl_ati_101 plan classes, but doens't require
|
|
// modifications if someone wants a opencl_nvidia_102 plan class.
|
|
const char *p=plan_class+strlen(plan_class);
|
|
while (isnum(p[-1])) {
|
|
p--;
|
|
}
|
|
int ver=atoi(p);
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] plan_class %s uses OpenCl version %d\n",
|
|
plan_class,
|
|
ver
|
|
);
|
|
}
|
|
if (strstr(plan_class, "nvidia")) {
|
|
COPROC_NVIDIA& c = sreq.coprocs.nvidia;
|
|
if (!c.count) return false;
|
|
if (!c.have_opencl) return false;
|
|
if (strstr(plan_class,"opencl_nvidia") == plan_class) {
|
|
return opencl_check(
|
|
c, hu,
|
|
ver,
|
|
OPENCL_NVIDIA_MIN_RAM,
|
|
1,
|
|
.01,
|
|
.14
|
|
);
|
|
} 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) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [opencl] HOST has no ATI/AMD GPUs\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
if (!c.have_opencl) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [opencl] GPU/Driver/BOINC revision doesn not support OpenCL\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
if (strstr(plan_class,"opencl_ati") == plan_class) {
|
|
return opencl_check(
|
|
c, hu,
|
|
ver,
|
|
OPENCL_ATI_MIN_RAM,
|
|
1,
|
|
.01,
|
|
.14
|
|
);
|
|
} else {
|
|
log_messages.printf(MSG_CRITICAL,
|
|
"[version] [opencl] Unknown plan class: %s\n", plan_class
|
|
);
|
|
return false;
|
|
}
|
|
|
|
} else if (strstr(plan_class, "intel_gpu")) {
|
|
COPROC_INTEL& c = sreq.coprocs.intel_gpu;
|
|
if (!c.count) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [opencl] HOST has no INTEL GPUs\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
if (!c.have_opencl) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] [opencl] GPU/Driver/BOINC revision doesn not support OpenCL\n"
|
|
);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
|
|
if (strstr(plan_class,"opencl_intel_gpu") == plan_class) {
|
|
return opencl_check(
|
|
c, hu,
|
|
ver,
|
|
OPENCL_INTEL_GPU_MIN_RAM,
|
|
1,
|
|
.1,
|
|
.2
|
|
);
|
|
} else {
|
|
log_messages.printf(MSG_CRITICAL,
|
|
"[version] [opencl] Unknown plan class: %s\n", plan_class
|
|
);
|
|
return false;
|
|
}
|
|
|
|
// maybe add a clause for multicore CPU
|
|
|
|
} else {
|
|
log_messages.printf(MSG_CRITICAL,
|
|
"[version] [opencl] 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 run 7.0+ client
|
|
//
|
|
if (sreq.core_client_major_version < 7) {
|
|
add_no_work_message("BOINC client 7.0+ required for Virtualbox jobs");
|
|
return false;
|
|
}
|
|
|
|
// host must have VirtualBox 3.2 or later
|
|
//
|
|
if (strlen(sreq.host.virtualbox_version) == 0) {
|
|
add_no_work_message("VirtualBox is not installed");
|
|
return false;
|
|
}
|
|
int n, maj, min, rel;
|
|
n = sscanf(sreq.host.virtualbox_version, "%d.%d.%d", &maj, &min, &rel);
|
|
if ((n != 3) || (maj < 3) || (maj == 3 and min < 2)) {
|
|
add_no_work_message("VirtualBox version 3.2 or later is required");
|
|
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;
|
|
}
|
|
|
|
PLAN_CLASS_SPECS plan_class_specs;
|
|
|
|
// app planning function.
|
|
// See http://boinc.berkeley.edu/trac/wiki/AppPlan
|
|
//
|
|
bool app_plan(SCHEDULER_REQUEST& sreq, char* plan_class, HOST_USAGE& hu) {
|
|
char buf[256];
|
|
static bool check_plan_class_spec = true;
|
|
static bool have_plan_class_spec = false;
|
|
static bool bad_plan_class_spec = false;
|
|
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Checking plan class '%s'\n", plan_class
|
|
);
|
|
}
|
|
|
|
if (check_plan_class_spec) {
|
|
check_plan_class_spec = false;
|
|
safe_strcpy(buf, config.project_dir);
|
|
safe_strcat(buf, "/plan_class_spec.xml");
|
|
int retval = plan_class_specs.parse_file(buf);
|
|
if (retval == ERR_FOPEN) {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] Couldn't open plan class spec file '%s'\n", buf
|
|
);
|
|
}
|
|
have_plan_class_spec = false;
|
|
} else if (retval) {
|
|
log_messages.printf(MSG_CRITICAL,
|
|
"Error parsing plan class spec file '%s'\n", buf
|
|
);
|
|
bad_plan_class_spec = true;
|
|
} else {
|
|
if (config.debug_version_select) {
|
|
log_messages.printf(MSG_NORMAL,
|
|
"[version] reading plan classes from file '%s'\n", buf
|
|
);
|
|
}
|
|
have_plan_class_spec = true;
|
|
}
|
|
}
|
|
if (bad_plan_class_spec) {
|
|
return false;
|
|
}
|
|
if (have_plan_class_spec) {
|
|
return plan_class_specs.check(sreq, plan_class, hu);
|
|
}
|
|
|
|
if (!strcmp(plan_class, "mt")) {
|
|
return app_plan_mt(sreq, hu);
|
|
} else if (strstr(plan_class, "opencl") == plan_class) {
|
|
return app_plan_opencl(sreq, plan_class, hu);
|
|
} else if (strstr(plan_class, "ati") == plan_class) {
|
|
return app_plan_ati(sreq, plan_class, hu);
|
|
} else if (strstr(plan_class, "cuda")) {
|
|
return app_plan_nvidia(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;
|
|
}
|
|
|
|
#ifndef NEW_SCORE
|
|
// 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);
|
|
if (app->non_cpu_intensive) return false;
|
|
|
|
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;
|
|
}
|
|
#endif
|
|
|
|
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));
|
|
}
|
|
}
|