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boinc/lib/coproc.h

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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/>.
// Structures representing coprocessors (e.g. GPUs);
// used in both client and server.
//
// Notes:
//
// 1) The use of "CUDA" is misleading; it really means "NVIDIA GPU".
// 2) The design treats each resource type as a pool of identical devices;
// for example, a scheduler request contains a request
// (#instances, instance-seconds) for CUDA jobs.
// In reality, the instances of a resource type can have different properties:
// In the case of CUDA, "compute capability", driver version, RAM, speed, etc.
// How to resolve this discrepancy?
//
// Prior to 21 Apr 09 we identified the fastest instance
// and pretended that the others were identical to it.
// This approach has a serious flaw:
// suppose that the fastest instance has characteristics
// (version, RAM etc.) that satisfy the project's requirements,
// but other instances to not.
// Then BOINC executes jobs on GPUs that can't handle them,
// the jobs fail, the host is punished, etc.
//
// We could treat each GPU has a separate resource,
// with its own backoffs, etc.
// However, this would imply tying jobs to instances,
// which is undesirable from a scheduling viewpoint.
// It would also be a big code change in both client and server.
//
// Instead, (as of 21 Apr 09) our approach is to identify a
// "most capable" instance, which in the case of CUDA is based on
// a) compute capability
// b) driver version
// c) RAM size
// d) est. FLOPS
// (in decreasing priority).
// We ignore and don't use any instances that are less capable
// on any of these axes.
//
// This design avoids running coprocessor apps on instances
// that are incapable of handling them, and it involves no server changes.
// Its drawback is that, on systems with multiple and differing GPUs,
// it may not use some GPUs that actually could be used.
//
// Modified (as of 23 July 14) to allow coprocessors (OpenCL GPUs and OpenCL
// accelerators) from vendors other than original 3: NVIDIA, AMD and Intel.
// For these original 3 GPU vendors, we still use the above approach, and the
// COPROC::type field contains a standardized vendor name "NVIDIA", "ATI" or
// "intel_gpu". But for other, "new" vendors, we treat each device as a
// separate resource, creating an entry for each instance in the
// COPROCS::coprocs[] array and copying the device name COPROC::opencl_prop.name
// into the COPROC::type field (instead of the vendor name.)
#ifndef _COPROC_
#define _COPROC_
#include <vector>
#include <string>
#ifdef _WIN32
#include "boinc_win.h"
#endif
#ifdef _USING_FCGI_
#include "boinc_fcgi.h"
#endif
//#include "client_types.h"
#include "miofile.h"
#include "error_numbers.h"
#include "parse.h"
#include "cal_boinc.h"
#include "cl_boinc.h"
#include "opencl_boinc.h"
#define MAX_COPROC_INSTANCES 64
#define MAX_RSC 8
// max # of processing resources types
// arguments to proc_type_name() and proc_type_name_xml().
//
#define PROC_TYPE_CPU 0
#define PROC_TYPE_NVIDIA_GPU 1
#define PROC_TYPE_AMD_GPU 2
#define PROC_TYPE_INTEL_GPU 3
#define PROC_TYPE_MINER_ASIC 4
#define NPROC_TYPES 5
extern const char* proc_type_name(int);
// user-readable name
extern const char* proc_type_name_xml(int);
// name used in XML and COPROC::type
extern int coproc_type_name_to_num(const char* name);
// deprecated, but keep for simplicity
#define GPU_TYPE_NVIDIA proc_type_name_xml(PROC_TYPE_NVIDIA_GPU)
#define GPU_TYPE_ATI proc_type_name_xml(PROC_TYPE_AMD_GPU)
#define GPU_TYPE_INTEL proc_type_name_xml(PROC_TYPE_INTEL_GPU)
// represents a requirement for a coproc.
// This is a parsed version of the <coproc> elements in an <app_version>
// (used in client only)
//
struct COPROC_REQ {
char type[256]; // must be unique
double count;
int parse(XML_PARSER&);
};
struct PCI_INFO {
bool present;
int bus_id;
int device_id;
int domain_id;
void write(MIOFILE&);
int parse(XML_PARSER&);
};
// represents a set of identical coprocessors on a particular computer.
// Abstract class;
// objects will always be a derived class (COPROC_CUDA, COPROC_ATI)
// Used in both client and server.
//
struct COPROC {
char type[256]; // must be unique
int count; // how many are present
bool is_gpu;
double peak_flops;
double used; // how many are in use (used by client)
bool have_cuda; // True if this GPU supports CUDA on this computer
bool have_cal; // True if this GPU supports CAL on this computer
bool have_opencl; // True if this GPU supports openCL on this computer
double available_ram;
bool specified_in_config;
// If true, this coproc was listed in cc_config.xml
// rather than being detected by the client.
// the following are used in both client and server for work-fetch info
//
double req_secs;
// how many instance-seconds of work requested
double req_instances;
// client is requesting enough jobs to use this many instances
double estimated_delay;
// resource will be saturated for this long
// temps used in client (enforce_schedule())
// to keep track of what fraction of each instance is in use
// during instance assignment
//
double usage[MAX_COPROC_INSTANCES];
double pending_usage[MAX_COPROC_INSTANCES];
// the device number of each instance
// These are not sequential if we omit instances (see above)
//
int device_nums[MAX_COPROC_INSTANCES];
int device_num; // temp used in scan process
bool instance_has_opencl[MAX_COPROC_INSTANCES];
cl_device_id opencl_device_ids[MAX_COPROC_INSTANCES];
int opencl_device_count;
int opencl_device_indexes[MAX_COPROC_INSTANCES];
PCI_INFO pci_info;
PCI_INFO pci_infos[MAX_COPROC_INSTANCES];
bool running_graphics_app[MAX_COPROC_INSTANCES];
// is this GPU running a graphics app (NVIDIA only)
double last_print_time;
OPENCL_DEVICE_PROP opencl_prop;
#ifndef _USING_FCGI_
void write_xml(MIOFILE&, bool scheduler_rpc=false);
void write_request(MIOFILE&);
#endif
int parse(XML_PARSER&);
inline void clear() {
// can't just memcpy() - trashes vtable
type[0] = 0;
count = 0;
is_gpu = false;
peak_flops = 0;
used = 0;
have_cuda = false;
have_cal = false;
have_opencl = false;
specified_in_config = false;
req_secs = 0;
req_instances = 0;
opencl_device_count = 0;
estimated_delay = 0;
available_ram = 0;
for (int i=0; i<MAX_COPROC_INSTANCES; i++) {
device_nums[i] = 0;
instance_has_opencl[i] = false;
opencl_device_ids[i] = 0;
opencl_device_indexes[i] = 0;
running_graphics_app[i] = true;
}
memset(&opencl_prop, 0, sizeof(opencl_prop));
memset(&pci_info, 0, sizeof(pci_info));
}
inline void clear_usage() {
for (int i=0; i<count; i++) {
usage[i] = 0;
pending_usage[i] = 0;
}
}
COPROC() {
clear();
}
int device_num_index(int n) {
for (int i=0; i<count; i++) {
if (device_nums[i] == n) return i;
}
return -1;
}
void merge_opencl(
std::vector<OPENCL_DEVICE_PROP> &opencls,
std::vector<int>& ignore_dev
);
void find_best_opencls(
bool use_all,
std::vector<OPENCL_DEVICE_PROP> &opencls,
std::vector<int>& ignore_dev
);
};
// Based on cudaDeviceProp from /usr/local/cuda/include/driver_types.h
// doesn't have to match exactly since we get the attributes one at a time.
//
// This is used for 2 purposes:
// - it's exported via GUI RPC for GUIs or other tools
// - it's sent from client to scheduler, for use by app plan functions
// Properties not relevant to either of these can be omitted.
//
struct CUDA_DEVICE_PROP {
char name[256];
double totalGlobalMem;
double sharedMemPerBlock;
int regsPerBlock;
int warpSize;
double memPitch;
int maxThreadsPerBlock;
int maxThreadsDim[3];
int maxGridSize[3];
int clockRate;
double totalConstMem;
int major; // compute capability
int minor;
double textureAlignment;
int deviceOverlap;
int multiProcessorCount;
};
typedef int CUdevice;
struct COPROC_NVIDIA : public COPROC {
int cuda_version; // CUDA runtime version
int display_driver_version;
CUDA_DEVICE_PROP prop;
COPROC_USAGE is_used; // temp used in scan process
#ifndef _USING_FCGI_
void write_xml(MIOFILE&, bool scheduler_rpc);
#endif
COPROC_NVIDIA(): COPROC() {
clear();
is_gpu = true;
}
void get(std::vector<std::string>& warnings);
void correlate(
bool use_all,
std::vector<int>& ignore_devs
);
void description(char* buf, int buflen);
void clear();
int parse(XML_PARSER&);
void set_peak_flops();
void fake(int driver_version, double ram, double avail_ram, int count);
};
// encode a 3-part version as // 10000000*major + 10000*minor + release
// Note: ATI release #s can exceed 1000
//
inline int ati_version_int(int major, int minor, int release) {
return major*10000000 + minor*10000 + release;
}
struct COPROC_ATI : public COPROC {
char name[256];
char version[50];
int version_num;
// CAL version (not driver version) encoded as an int
bool atirt_detected;
bool amdrt_detected;
CALdeviceattribs attribs;
CALdeviceinfo info;
COPROC_USAGE is_used; // temp used in scan process
#ifndef _USING_FCGI_
void write_xml(MIOFILE&, bool scheduler_rpc);
#endif
COPROC_ATI(): COPROC() {
clear();
is_gpu = true;
}
void get(std::vector<std::string>& warnings);
void correlate(
bool use_all,
std::vector<int>& ignore_devs
);
void description(char* buf, int buflen);
void clear();
int parse(XML_PARSER&);
void set_peak_flops();
void fake(double ram, double avail_ram, int);
};
struct COPROC_INTEL : public COPROC {
char name[256];
char version[50];
double global_mem_size;
COPROC_USAGE is_used; // temp used in scan process
#ifndef _USING_FCGI_
void write_xml(MIOFILE&, bool scheduler_rpc);
#endif
COPROC_INTEL(): COPROC() {
clear();
is_gpu = true;
}
void get(std::vector<std::string>& warnings);
void correlate(
bool use_all,
std::vector<int>& ignore_devs
);
void clear();
int parse(XML_PARSER&);
void set_peak_flops();
void fake(double ram, double avail_ram, int);
};
typedef std::vector<int> IGNORE_GPU_INSTANCE[NPROC_TYPES];
struct COPROCS {
int n_rsc;
COPROC coprocs[MAX_RSC];
// array of processor types on this host.
// element 0 always represents the CPU.
// The remaining elements, if any, are GPUs or other coprocessors
// The following contain vendor-specific info about GPUs.
// (These GPUs are also represented by elements in the coprocs array)
//
COPROC_NVIDIA nvidia;
COPROC_ATI ati;
COPROC_INTEL intel_gpu;
void write_xml(MIOFILE& out, bool scheduler_rpc);
void get(
bool use_all,
std::vector<std::string> &descs,
std::vector<std::string> &warnings,
IGNORE_GPU_INSTANCE &ignore_gpu_instance
);
void detect_gpus(std::vector<std::string> &warnings);
int launch_child_process_to_detect_gpus();
void correlate_gpus(
bool use_all,
std::vector<std::string> &descs,
IGNORE_GPU_INSTANCE &ignore_gpu_instance
);
void get_opencl(
std::vector<std::string> &warnings
);
void correlate_opencl(
bool use_all,
IGNORE_GPU_INSTANCE& ignore_gpu_instance
);
cl_int get_opencl_info(
OPENCL_DEVICE_PROP& prop,
cl_uint device_index,
std::vector<std::string>& warnings
);
int parse(XML_PARSER&);
void set_path_to_client(char *path);
int write_coproc_info_file(std::vector<std::string> &warnings);
int read_coproc_info_file(std::vector<std::string> &warnings);
int add_other_coproc_types();
#ifdef __APPLE__
void opencl_get_ati_mem_size_from_opengl(std::vector<std::string> &warnings);
#endif
void summary_string(char* buf, int len);
// Copy a coproc set, possibly setting usage to zero.
// used in round-robin simulator and CPU scheduler,
// to avoid messing w/ master copy
//
void clone(COPROCS& c, bool copy_used) {
n_rsc = c.n_rsc;
for (int i=0; i<n_rsc; i++) {
coprocs[i] = c.coprocs[i];
if (!copy_used) {
coprocs[i].used = 0;
}
}
}
void clear() {
n_rsc = 0;
for (int i=0; i<MAX_RSC; i++) {
coprocs[i].clear();
}
nvidia.clear();
ati.clear();
intel_gpu.clear();
COPROC c;
strcpy(c.type, "CPU");
add(c);
}
inline void clear_usage() {
for (int i=0; i<n_rsc; i++) {
coprocs[i].clear_usage();
}
}
inline bool none() {
return (n_rsc == 1);
}
inline int ndevs() {
int n=0;
for (int i=1; i<n_rsc; i++) {
n += coprocs[i].count;
}
return n;
}
inline bool have_nvidia() {
return (nvidia.count > 0);
}
inline bool have_ati() {
return (ati.count > 0);
}
inline bool have_intel_gpu() {
return (intel_gpu.count > 0);
}
int add(COPROC& c) {
if (n_rsc >= MAX_RSC) return ERR_BUFFER_OVERFLOW;
for (int i=1; i<n_rsc; i++) {
if (!strcmp(c.type, coprocs[i].type)) {
return ERR_DUP_NAME;
}
}
coprocs[n_rsc++] = c;
return 0;
}
void bound_counts();
// make sure instance counts are within legal range
COPROC* lookup_type(const char* t) {
for (int i=1; i<n_rsc; i++) {
if (!strcmp(t, coprocs[i].type)) {
return &coprocs[i];
}
}
return NULL;
}
COPROC* proc_type_to_coproc(int t) {
switch(t) {
case PROC_TYPE_NVIDIA_GPU: return &nvidia;
case PROC_TYPE_AMD_GPU: return &ati;
case PROC_TYPE_INTEL_GPU: return &intel_gpu;
case PROC_TYPE_MINER_ASIC: return lookup_type("miner_asic");
}
return NULL;
}
COPROCS() {
n_rsc = 0;
nvidia.count = 0;
ati.count = 0;
intel_gpu.count = 0;
COPROC c;
strcpy(c.type, "CPU");
add(c);
}
};
#endif
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