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lammps/lib/gpu/lal_device.cpp
Michael Brown 45c782308c Fixing issue from recent GPU package update with OMP_NUM_THREADS env being overridden in GPU library. 
Fixing race condition with OpenMP for GPU styles using torque (missed in regression tests due to the first fix)
Documenting GPU package option for setting the number of threads (consistent with USER-INTEL and USER-OMP).
2021-02-18 21:08:18 -08:00

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/***************************************************************************
device.cpp
-------------------
W. Michael Brown (ORNL)
Class for management of the device where the computations are performed
__________________________________________________________________________
This file is part of the LAMMPS Accelerator Library (LAMMPS_AL)
__________________________________________________________________________
begin :
email : brownw@ornl.gov
***************************************************************************/
#include "lal_device.h"
#include "lal_precision.h"
#include <map>
#include <cmath>
#include <cstdlib>
#if (LAL_USE_OMP == 1)
#include <omp.h>
#endif
#if defined(USE_OPENCL)
#include "device_cl.h"
#ifdef LAL_OCL_EXTRA_ARGS
#define LAL_DM_STRINGIFY(x) #x
#define LAL_PRE_STRINGIFY(x) LAL_DM_STRINGIFY(x)
#endif
#elif defined(USE_CUDART)
const char *device=0;
#else
#include "device_cubin.h"
#endif
namespace LAMMPS_AL {
#define DeviceT Device<numtyp, acctyp>
template <class numtyp, class acctyp>
DeviceT::Device() : _init_count(0), _device_init(false),
_gpu_mode(GPU_FORCE), _first_device(0),
_last_device(0), _platform_id(-1), _compiled(false) {
}
template <class numtyp, class acctyp>
DeviceT::~Device() {
clear_device();
}
template <class numtyp, class acctyp>
int DeviceT::init_device(MPI_Comm world, MPI_Comm replica, const int ngpu,
const int first_gpu_id, const int gpu_mode,
const double p_split, const int t_per_atom,
const double user_cell_size, char *ocl_args,
const int ocl_platform, char *device_type_flags,
const int block_pair) {
_threads_per_atom=t_per_atom;
_threads_per_charge=t_per_atom;
_threads_per_three=t_per_atom;
if (_device_init)
return 0;
_device_init=true;
_comm_world=replica; //world;
_comm_replica=replica;
int ndevices=ngpu;
_first_device=first_gpu_id;
_gpu_mode=gpu_mode;
_particle_split=p_split;
_user_cell_size=user_cell_size;
_block_pair=block_pair;
// support selecting OpenCL platform id with "package platform" keyword
if (ocl_platform >= 0)
_platform_id = ocl_platform;
gpu=new UCL_Device();
// ---------------------- OpenCL Compiler Args -------------------------
std::string extra_args="";
if (ocl_args) extra_args+=":"+std::string(ocl_args);
#ifdef LAL_OCL_EXTRA_ARGS
extra_args+=":" LAL_PRE_STRINGIFY(LAL_OCL_EXTRA_ARGS);
#endif
for (int i=0; i<extra_args.length(); i++)
if (extra_args[i]==':') extra_args[i]=' ';
// --------------------------- MPI setup -------------------------------
// Get the rank/size within the world
MPI_Comm_rank(_comm_world,&_world_me);
MPI_Comm_size(_comm_world,&_world_size);
// Get the rank/size within the replica
MPI_Comm_rank(_comm_replica,&_replica_me);
MPI_Comm_size(_comm_replica,&_replica_size);
// Get the names of all nodes
int name_length;
char node_name[MPI_MAX_PROCESSOR_NAME];
char *node_names = new char[MPI_MAX_PROCESSOR_NAME*_world_size];
MPI_Get_processor_name(node_name,&name_length);
MPI_Allgather(&node_name,MPI_MAX_PROCESSOR_NAME,MPI_CHAR,&node_names[0],
MPI_MAX_PROCESSOR_NAME,MPI_CHAR,_comm_world);
std::string node_string=std::string(node_name);
// Get the number of procs per node
std::map<std::string,int> name_map;
std::map<std::string,int>::iterator np;
for (int i=0; i<_world_size; i++) {
std::string i_string=std::string(&node_names[i*MPI_MAX_PROCESSOR_NAME]);
np=name_map.find(i_string);
if (np==name_map.end())
name_map[i_string]=1;
else
np->second++;
}
int procs_per_node=name_map.begin()->second;
// Assign a unique id to each node
int split_num=0, split_id=0;
for (np=name_map.begin(); np!=name_map.end(); ++np) {
if (np->first==node_string)
split_id=split_num;
split_num++;
}
delete[] node_names;
// Set up a per node communicator and find rank within
MPI_Comm node_comm;
MPI_Comm_split(_comm_world, split_id, 0, &node_comm);
int node_rank;
MPI_Comm_rank(node_comm,&node_rank);
// ------------------- Device selection parameters----------------------
if (ndevices > procs_per_node)
ndevices = procs_per_node;
// --------------------- OCL Platform Selection -----------------------
// Setup OpenCL platform and parameters based on platform
// and device type specifications
std::string ocl_vstring="";
if (device_type_flags != nullptr) ocl_vstring=device_type_flags;
// Setup the OpenCL platform
// If multiple platforms and no user platform specified,
// try to match platform from config matching any user specified
// device type. Give preference to platforms with GPUs.
// Priority under these conditions to platform with device with
// highest compute unit count.
int pres;
enum UCL_DEVICE_TYPE type=UCL_GPU;
#ifndef USE_OPENCL
pres=gpu->set_platform(0);
#else
if (_platform_id>=0)
pres=gpu->set_platform(_platform_id);
else {
std::string vendor="";
if (device_type_flags!=nullptr) {
if (ocl_vstring=="intelgpu")
vendor="intel";
else if (ocl_vstring=="intelcpu") {
vendor="intel";
type=UCL_CPU;
} else if (ocl_vstring=="nvidiagpu")
vendor="nvidia";
else if (ocl_vstring=="amdgpu")
vendor="amd";
else if (ocl_vstring=="applegpu")
vendor="apple";
}
pres=gpu->auto_set_platform(type,vendor,ndevices,_first_device);
}
#endif
if (pres != UCL_SUCCESS)
return -12;
// ------------------------ Device Selection ---------------------------
if (_first_device > -1 && _first_device >= gpu->num_devices())
return -2;
if (ndevices > gpu->num_devices())
return -2;
if (_first_device + ndevices > gpu->num_devices())
return -2;
if (gpu->num_devices()==0)
return -2;
// Fully specified deviceIDs
if (_first_device > -1 && ndevices > 0)
_last_device = _first_device + ndevices - 1;
// Find deviceID with most CUs (priority given to the accelerator type)
if (_first_device < 0) {
int best_device = 0;
int best_cus = gpu->cus(0);
bool type_match = (gpu->device_type(0) == type);
for (int i = 1; i < gpu->num_devices(); i++) {
if (type_match==true && gpu->device_type(i)!=type)
continue;
if (type_match == false && gpu->device_type(i) == type) {
type_match = true;
best_cus = gpu->cus(i);
best_device = i;
}
if (gpu->cus(i) > best_cus) {
best_cus = gpu->cus(i);
best_device = i;
}
}
_first_device = _last_device = best_device;
type = gpu->device_type(_first_device);
if (ndevices > 0) {
// Expand range to meet specified number of devices
while (_last_device - _first_device < ndevices - 1) {
if (_last_device + 1 == gpu->num_devices())
_first_device--;
else if (_first_device == 0)
_last_device++;
else {
if (gpu->device_type(_last_device+1)==type &&
gpu->device_type(_first_device-1)!=type)
_last_device++;
else if (gpu->device_type(_last_device+1)!=type &&
gpu->device_type(_first_device-1)==type)
_first_device--;
else if (gpu->cus(_last_device+1) > gpu->cus(_first_device-1))
_last_device++;
else
_first_device--;
}
}
}
}
// If ngpus not specified, expand range to include matching devices
if (ndevices == 0) {
for (int i = _first_device; i < gpu->num_devices(); i++) {
if (gpu->device_type(i)==gpu->device_type(_first_device) &&
gpu->cus(i)==gpu->cus(_first_device))
_last_device = i;
else
break;
}
ndevices = _last_device - _first_device + 1;
if (ndevices > procs_per_node) {
ndevices = procs_per_node;
_last_device=_first_device + ndevices - 1;
}
}
// ------------------------ MPI Device ID Setup -----------------------
// set the device ID
_procs_per_gpu=static_cast<int>(ceil(static_cast<double>(procs_per_node)/
ndevices));
int my_gpu=node_rank/_procs_per_gpu+_first_device;
// Time on the device only if 1 proc per gpu
_time_device=true;
#if 0
// XXX: the following setting triggers a memory leak with OpenCL and MPI
// setting _time_device=true for all processes doesn't seem to be a
// problem with either (no segfault, no (large) memory leak.
// thus keeping this disabled for now. may need to review later.
// 2018-07-23 <akohlmey@gmail.com>
if (_procs_per_gpu>1)
_time_device=false;
#endif
// Set up a per device communicator
MPI_Comm_split(node_comm,my_gpu,0,&_comm_gpu);
MPI_Comm_rank(_comm_gpu,&_gpu_rank);
#if !defined(CUDA_PROXY) && !defined(CUDA_MPS_SUPPORT)
if (_procs_per_gpu>1 && gpu->sharing_supported(my_gpu)==false)
return -7;
#endif
// --------------- Device Configuration and Setup -------------------------
if (gpu->set(my_gpu)!=UCL_SUCCESS)
return -6;
#if !defined(USE_OPENCL) && !defined(USE_HIP)
if (gpu->arch()<7.0) {
gpu->push_command_queue();
gpu->set_command_queue(1);
}
#endif
_long_range_precompute=0;
// If OpenCL parameters not specified by user, try to auto detect
// best option from the platform config
#ifdef USE_OPENCL
if (device_type_flags==nullptr) {
std::string pname = gpu->platform_name();
for (int i=0; i<pname.length(); i++)
if (pname[i]<='z' && pname[i]>='a')
pname[i]=toupper(pname[i]);
if (pname.find("NVIDIA")!=std::string::npos)
ocl_vstring="nvidiagpu";
else if (pname.find("INTEL")!=std::string::npos) {
if (gpu->device_type()==UCL_GPU)
ocl_vstring="intelgpu";
else if (gpu->device_type()==UCL_CPU)
ocl_vstring="intelcpu";
} else if (pname.find("AMD")!=std::string::npos) {
if (gpu->device_type()==UCL_GPU)
ocl_vstring="amdgpu";
} else if (pname.find("APPLE")!=std::string::npos) {
if (gpu->device_type()==UCL_GPU)
ocl_vstring="applegpu";
}
}
#endif
if (set_ocl_params(ocl_vstring, extra_args)!=0)
return -11;
int flag=0;
for (int i=0; i<_procs_per_gpu; i++) {
if (_gpu_rank==i)
flag=compile_kernels();
gpu_barrier();
}
// Setup auto bin size calculation for calls from atom::sort
// - This is repeated in neighbor init with additional info
if (_user_cell_size<0.0) {
#ifndef LAL_USE_OLD_NEIGHBOR
_neighbor_shared.setup_auto_cell_size(true,0,_simd_size);
#else
_neighbor_shared.setup_auto_cell_size(false,0,_simd_size);
#endif
} else
_neighbor_shared.setup_auto_cell_size(false,_user_cell_size,_simd_size);
return flag;
}
template <class numtyp, class acctyp>
int DeviceT::set_ocl_params(std::string s_config, std::string extra_args) {
#ifdef USE_OPENCL
#include "lal_pre_ocl_config.h"
if (s_config=="" || s_config=="none")
s_config="generic";
int config_index=-1;
for (int i=0; i<nconfigs; i++)
if (s_config==std::string(ocl_config_names[i]))
config_index=i;
if (config_index != -1)
s_config=ocl_config_strings[config_index];
_ocl_config_name="CUSTOM";
int token_count=0;
std::string params[18];
char ocl_config[2048];
strcpy(ocl_config,s_config.c_str());
char *pch = strtok(ocl_config,",");
_ocl_config_name=pch;
pch = strtok(nullptr,",");
if (pch == nullptr) return -11;
while (pch != nullptr) {
if (token_count==18)
return -11;
params[token_count]=pch;
token_count++;
pch = strtok(nullptr,",");
}
_ocl_compile_string="-cl-mad-enable ";
if (params[4]!="0") _ocl_compile_string+="-cl-fast-relaxed-math ";
_ocl_compile_string+=std::string(OCL_INT_TYPE)+" "+
std::string(OCL_PRECISION_COMPILE);
if (gpu->has_subgroup_support())
_ocl_compile_string+=" -DUSE_OPENCL_SUBGROUPS";
#ifdef LAL_USE_OLD_NEIGHBOR
_ocl_compile_string+=" -DLAL_USE_OLD_NEIGHBOR";
#endif
_ocl_compile_string += " -DCONFIG_ID="+params[0]+
" -DSIMD_SIZE="+params[1]+
" -DMEM_THREADS="+params[2];
if (gpu->has_shuffle_support()==false)
_ocl_compile_string+=" -DSHUFFLE_AVAIL=0";
else
_ocl_compile_string+=" -DSHUFFLE_AVAIL="+params[3];
_ocl_compile_string += " -DFAST_MATH="+params[4]+
" -DTHREADS_PER_ATOM="+params[5]+
" -DTHREADS_PER_CHARGE="+params[6]+
" -DTHREADS_PER_THREE="+params[7]+
" -DBLOCK_PAIR="+params[8]+
" -DBLOCK_BIO_PAIR="+params[9]+
" -DBLOCK_ELLIPSE="+params[10]+
" -DPPPM_BLOCK_1D="+params[11]+
" -DBLOCK_NBOR_BUILD="+params[12]+
" -DBLOCK_CELL_2D="+params[13]+
" -DBLOCK_CELL_ID="+params[14]+
" -DMAX_SHARED_TYPES="+params[15]+
" -DMAX_BIO_SHARED_TYPES="+params[16]+
" -DPPPM_MAX_SPLINE="+params[17];
_ocl_compile_string += extra_args;
#endif
return 0;
}
template <class numtyp, class acctyp>
int DeviceT::init(Answer<numtyp,acctyp> &ans, const bool charge,
const bool rot, const int nlocal,
const int nall, const int maxspecial,
const bool vel) {
if (!_device_init)
return -1;
if (sizeof(acctyp)==sizeof(double) && gpu->double_precision()==false)
return -5;
// Counts of data transfers for timing overhead estimates
_data_in_estimate=0;
_data_out_estimate=1;
// Initial number of local particles
int ef_nlocal=nlocal;
if (_particle_split<1.0 && _particle_split>0.0)
ef_nlocal=static_cast<int>(_particle_split*nlocal);
int gpu_nbor=0;
if (_gpu_mode==Device<numtyp,acctyp>::GPU_NEIGH)
gpu_nbor=1;
else if (_gpu_mode==Device<numtyp,acctyp>::GPU_HYB_NEIGH)
gpu_nbor=2;
#if !defined(USE_CUDPP) && !defined(USE_HIP_DEVICE_SORT)
if (gpu_nbor==1) gpu_nbor=2;
#endif
#ifndef LAL_USE_OLD_NEIGHBOR
if (gpu_nbor==1) gpu_nbor=2;
#endif
if (_init_count==0) {
// Initialize atom and nbor data
if (!atom.init(nall,charge,rot,*gpu,gpu_nbor,gpu_nbor>0 && maxspecial>0,vel))
return -3;
_data_in_estimate++;
if (charge)
_data_in_estimate++;
if (rot)
_data_in_estimate++;
if (vel)
_data_in_estimate++;
} else {
if (atom.charge()==false && charge)
_data_in_estimate++;
if (atom.quaternion()==false && rot)
_data_in_estimate++;
if (atom.velocity()==false && vel)
_data_in_estimate++;
if (!atom.add_fields(charge,rot,gpu_nbor,gpu_nbor>0 && maxspecial,vel))
return -3;
}
if (!ans.init(ef_nlocal,charge,rot,*gpu))
return -3;
_init_count++;
return 0;
}
template <class numtyp, class acctyp>
int DeviceT::init(Answer<numtyp,acctyp> &ans, const int nlocal,
const int nall) {
if (!_device_init)
return -1;
if (sizeof(acctyp)==sizeof(double) && gpu->double_precision()==false)
return -5;
if (_init_count==0) {
// Initialize atom and nbor data
if (!atom.init(nall,true,false,*gpu,false,false))
return -3;
} else
if (!atom.add_fields(true,false,false,false))
return -3;
if (!ans.init(nlocal,true,false,*gpu))
return -3;
_init_count++;
return 0;
}
template <class numtyp, class acctyp>
int DeviceT::init_nbor(Neighbor *nbor, const int nlocal,
const int host_nlocal, const int nall,
const int maxspecial, const int gpu_host,
const int max_nbors, const double cutoff,
const bool pre_cut, const int threads_per_atom,
const bool ilist_map) {
int ef_nlocal=nlocal;
if (_particle_split<1.0 && _particle_split>0.0)
ef_nlocal=static_cast<int>(_particle_split*nlocal);
int gpu_nbor=0;
if (_gpu_mode==Device<numtyp,acctyp>::GPU_NEIGH)
gpu_nbor=1;
else if (_gpu_mode==Device<numtyp,acctyp>::GPU_HYB_NEIGH)
gpu_nbor=2;
#if !defined(USE_CUDPP) && !defined(USE_HIP_DEVICE_SORT)
if (gpu_nbor==1)
gpu_nbor=2;
#endif
#ifndef LAL_USE_OLD_NEIGHBOR
if (gpu_nbor==1)
gpu_nbor=2;
#endif
if (!nbor->init(&_neighbor_shared,ef_nlocal,host_nlocal,max_nbors,maxspecial,
*gpu,gpu_nbor,gpu_host,pre_cut,_block_cell_2d,
_block_cell_id, _block_nbor_build, threads_per_atom,
_simd_size, _time_device, compile_string(), ilist_map))
return -3;
if (_user_cell_size<0.0) {
#ifndef LAL_USE_OLD_NEIGHBOR
_neighbor_shared.setup_auto_cell_size(true,cutoff,nbor->simd_size());
#else
_neighbor_shared.setup_auto_cell_size(false,cutoff,nbor->simd_size());
#endif
} else
_neighbor_shared.setup_auto_cell_size(false,_user_cell_size,
nbor->simd_size());
nbor->set_cutoff(cutoff);
return 0;
}
template <class numtyp, class acctyp>
void DeviceT::set_single_precompute
(PPPM<numtyp,acctyp,float,_lgpu_float4> *pppm) {
_long_range_precompute=1;
pppm_single=pppm;
}
template <class numtyp, class acctyp>
void DeviceT::set_double_precompute
(PPPM<numtyp,acctyp,double,_lgpu_double4> *pppm) {
_long_range_precompute=2;
pppm_double=pppm;
}
template <class numtyp, class acctyp>
void DeviceT::init_message(FILE *screen, const char *name,
const int first_gpu, const int last_gpu) {
#if defined(USE_OPENCL)
std::string fs="";
#elif defined(USE_CUDART)
std::string fs="";
#else
std::string fs=toa(gpu->free_gigabytes())+"/";
#endif
if (_replica_me == 0 && screen) {
fprintf(screen,"\n-------------------------------------");
fprintf(screen,"-------------------------------------\n");
fprintf(screen,"- Using acceleration for %s:\n",name);
fprintf(screen,"- with %d proc(s) per device.\n",_procs_per_gpu);
#if (LAL_USE_OMP == 1)
fprintf(screen,"- with %d thread(s) per proc.\n", omp_get_max_threads());
#endif
#ifdef USE_OPENCL
fprintf(screen,"- with OpenCL Parameters for: %s (%d)\n",
_ocl_config_name.c_str(),_config_id);
#endif
if (shuffle_avail())
fprintf(screen,"- Horizontal vector operations: ENABLED\n");
else
fprintf(screen,"- Horizontal vector operations: DISABLED\n");
if (gpu->shared_memory(first_gpu))
fprintf(screen,"- Shared memory system: Yes\n");
else
fprintf(screen,"- Shared memory system: No\n");
fprintf(screen,"-------------------------------------");
fprintf(screen,"-------------------------------------\n");
int last=last_gpu+1;
if (last>gpu->num_devices())
last=gpu->num_devices();
for (int i=first_gpu; i<last; i++) {
std::string sname;
if (i==first_gpu)
sname=gpu->name(i)+", "+toa(gpu->cus(i))+" CUs, "+fs+
toa(gpu->gigabytes(i))+" GB, "+toa(gpu->clock_rate(i))+" GHZ (";
else
sname=gpu->name(i)+", "+toa(gpu->cus(i))+" CUs, "+
toa(gpu->clock_rate(i))+" GHZ (";
if (sizeof(PRECISION)==4) {
if (sizeof(ACC_PRECISION)==4)
sname+="Single Precision)";
else
sname+="Mixed Precision)";
} else
sname+="Double Precision)";
fprintf(screen,"Device %d: %s\n",i,sname.c_str());
}
fprintf(screen,"-------------------------------------");
fprintf(screen,"-------------------------------------\n\n");
}
}
template <class numtyp, class acctyp>
void DeviceT::estimate_gpu_overhead(const int kernel_calls,
double &gpu_overhead,
double &gpu_driver_overhead) {
UCL_H_Vec<int> *host_data_in=nullptr, *host_data_out=nullptr;
UCL_D_Vec<int> *dev_data_in=nullptr, *dev_data_out=nullptr,
*kernel_data=nullptr;
UCL_Timer *timers_in=nullptr, *timers_out=nullptr, *timers_kernel=nullptr;
UCL_Timer over_timer(*gpu);
if (_data_in_estimate>0) {
host_data_in=new UCL_H_Vec<int>[_data_in_estimate];
dev_data_in=new UCL_D_Vec<int>[_data_in_estimate];
timers_in=new UCL_Timer[_data_in_estimate];
}
if (_data_out_estimate>0) {
host_data_out=new UCL_H_Vec<int>[_data_out_estimate];
dev_data_out=new UCL_D_Vec<int>[_data_out_estimate];
timers_out=new UCL_Timer[_data_out_estimate];
}
if (kernel_calls>0) {
kernel_data=new UCL_D_Vec<int>[kernel_calls];
timers_kernel=new UCL_Timer[kernel_calls];
}
for (int i=0; i<_data_in_estimate; i++) {
host_data_in[i].alloc(1,*gpu);
dev_data_in[i].alloc(1,*gpu);
timers_in[i].init(*gpu);
}
for (int i=0; i<_data_out_estimate; i++) {
host_data_out[i].alloc(1,*gpu);
dev_data_out[i].alloc(1,*gpu);
timers_out[i].init(*gpu);
}
for (int i=0; i<kernel_calls; i++) {
kernel_data[i].alloc(1,*gpu);
timers_kernel[i].init(*gpu);
}
gpu_overhead=0.0;
gpu_driver_overhead=0.0;
for (int z=0; z<11; z++) {
gpu->sync();
gpu_barrier();
over_timer.start();
gpu->sync();
gpu_barrier();
double driver_time=MPI_Wtime();
for (int i=0; i<_data_in_estimate; i++) {
timers_in[i].start();
ucl_copy(dev_data_in[i],host_data_in[i],true);
timers_in[i].stop();
}
const int numel=1;
for (int i=0; i<kernel_calls; i++) {
timers_kernel[i].start();
k_zero.set_size(1,_block_pair);
k_zero.run(&(kernel_data[i]),&numel);
timers_kernel[i].stop();
}
for (int i=0; i<_data_out_estimate; i++) {
timers_out[i].start();
ucl_copy(host_data_out[i],dev_data_out[i],true);
timers_out[i].stop();
}
over_timer.stop();
#ifndef GERYON_OCL_FLUSH
if (_data_out_estimate)
dev_data_out[0].flush();
#endif
driver_time=MPI_Wtime()-driver_time;
double time=over_timer.seconds();
if (time_device()) {
for (int i=0; i<_data_in_estimate; i++)
timers_in[i].add_to_total();
for (int i=0; i<kernel_calls; i++)
timers_kernel[i].add_to_total();
for (int i=0; i<_data_out_estimate; i++)
timers_out[i].add_to_total();
}
double mpi_time, mpi_driver_time;
MPI_Allreduce(&time,&mpi_time,1,MPI_DOUBLE,MPI_MAX,gpu_comm());
MPI_Allreduce(&driver_time,&mpi_driver_time,1,MPI_DOUBLE,MPI_MAX,
gpu_comm());
if (z>0) {
gpu_overhead+=mpi_time;
gpu_driver_overhead+=mpi_driver_time;
}
}
gpu_overhead/=10.0;
gpu_driver_overhead/=10.0;
if (_data_in_estimate>0) {
delete [] host_data_in;
delete [] dev_data_in;
delete [] timers_in;
}
if (_data_out_estimate>0) {
delete [] host_data_out;
delete [] dev_data_out;
delete [] timers_out;
}
if (kernel_calls>0) {
delete [] kernel_data;
delete [] timers_kernel;
}
}
template <class numtyp, class acctyp>
void DeviceT::output_times(UCL_Timer &time_pair, Answer<numtyp,acctyp> &ans,
Neighbor &nbor, const double avg_split,
const double max_bytes, const double gpu_overhead,
const double driver_overhead,
const int threads_per_atom, FILE *screen) {
double single[9], times[9];
int post_final=0;
single[0]=atom.transfer_time()+ans.transfer_time();
single[1]=nbor.time_nbor.total_seconds()+nbor.time_hybrid1.total_seconds()+
nbor.time_hybrid2.total_seconds();
single[2]=nbor.time_kernel.total_seconds();
single[3]=time_pair.total_seconds();
single[4]=atom.cast_time()+ans.cast_time();
single[5]=gpu_overhead;
single[6]=driver_overhead;
single[7]=ans.cpu_idle_time();
single[8]=nbor.bin_time();
MPI_Finalized(&post_final);
if (post_final) return;
MPI_Reduce(single,times,9,MPI_DOUBLE,MPI_SUM,0,_comm_replica);
double my_max_bytes=max_bytes+atom.max_gpu_bytes();
double mpi_max_bytes;
MPI_Reduce(&my_max_bytes,&mpi_max_bytes,1,MPI_DOUBLE,MPI_MAX,0,_comm_replica);
double max_mb=mpi_max_bytes/(1024.0*1024.0);
#ifdef USE_OPENCL
// Workaround for timing issue on Intel OpenCL
if (times[3] > 80e6) times[3]=0.0;
#endif
if (replica_me()==0)
if (screen && times[6]>0.0) {
fprintf(screen,"\n\n-------------------------------------");
fprintf(screen,"--------------------------------\n");
fprintf(screen," Device Time Info (average): ");
fprintf(screen,"\n-------------------------------------");
fprintf(screen,"--------------------------------\n");
if (time_device() && times[3]>0) {
fprintf(screen,"Data Transfer: %.4f s.\n",times[0]/_replica_size);
fprintf(screen,"Neighbor copy: %.4f s.\n",times[1]/_replica_size);
if (nbor.gpu_nbor()>0)
fprintf(screen,"Neighbor build: %.4f s.\n",times[2]/_replica_size);
else
fprintf(screen,"Neighbor unpack: %.4f s.\n",times[2]/_replica_size);
fprintf(screen,"Force calc: %.4f s.\n",times[3]/_replica_size);
}
if (times[5]>0)
fprintf(screen,"Device Overhead: %.4f s.\n",times[5]/_replica_size);
fprintf(screen,"Average split: %.4f.\n",avg_split);
fprintf(screen,"Lanes / atom: %d.\n",threads_per_atom);
fprintf(screen,"Vector width: %d.\n", simd_size());
fprintf(screen,"Max Mem / Proc: %.2f MB.\n",max_mb);
if (nbor.gpu_nbor()==2)
fprintf(screen,"CPU Neighbor: %.4f s.\n",times[8]/_replica_size);
fprintf(screen,"CPU Cast/Pack: %.4f s.\n",times[4]/_replica_size);
fprintf(screen,"CPU Driver_Time: %.4f s.\n",times[6]/_replica_size);
fprintf(screen,"CPU Idle_Time: %.4f s.\n",times[7]/_replica_size);
fprintf(screen,"-------------------------------------");
fprintf(screen,"--------------------------------\n\n");
}
}
template <class numtyp, class acctyp>
void DeviceT::output_kspace_times(UCL_Timer &time_in,
UCL_Timer &time_out,
UCL_Timer &time_map,
UCL_Timer &time_rho,
UCL_Timer &time_interp,
Answer<numtyp,acctyp> &ans,
const double max_bytes,
const double cpu_time,
const double idle_time, FILE *screen) {
double single[9], times[9];
single[0]=time_out.total_seconds();
single[1]=time_in.total_seconds()+atom.transfer_time()+atom.cast_time();
single[2]=time_map.total_seconds();
single[3]=time_rho.total_seconds();
single[4]=time_interp.total_seconds();
single[5]=ans.transfer_time();
single[6]=cpu_time;
single[7]=idle_time;
single[8]=ans.cast_time();
MPI_Reduce(single,times,9,MPI_DOUBLE,MPI_SUM,0,_comm_replica);
double my_max_bytes=max_bytes+atom.max_gpu_bytes();
double mpi_max_bytes;
MPI_Reduce(&my_max_bytes,&mpi_max_bytes,1,MPI_DOUBLE,MPI_MAX,0,_comm_replica);
double max_mb=mpi_max_bytes/(1024.0*1024.0);
#ifdef USE_OPENCL
// Workaround for timing issue on Intel OpenCL
if (times[3] > 80e6) times[3]=0.0;
#endif
if (replica_me()==0)
if (screen && times[6]>0.0) {
fprintf(screen,"\n\n-------------------------------------");
fprintf(screen,"--------------------------------\n");
fprintf(screen," Device Time Info (average) for kspace: ");
fprintf(screen,"\n-------------------------------------");
fprintf(screen,"--------------------------------\n");
if (time_device() && times[3]>0) {
fprintf(screen,"Data Out: %.4f s.\n",times[0]/_replica_size);
fprintf(screen,"Data In: %.4f s.\n",times[1]/_replica_size);
fprintf(screen,"Kernel (map): %.4f s.\n",times[2]/_replica_size);
fprintf(screen,"Kernel (rho): %.4f s.\n",times[3]/_replica_size);
fprintf(screen,"Force interp: %.4f s.\n",times[4]/_replica_size);
fprintf(screen,"Total rho: %.4f s.\n",
(times[0]+times[2]+times[3])/_replica_size);
fprintf(screen,"Total interp: %.4f s.\n",
(times[1]+times[4])/_replica_size);
fprintf(screen,"Force copy: %.4f s.\n",times[5]/_replica_size);
fprintf(screen,"Total: %.4f s.\n",
(times[0]+times[1]+times[2]+times[3]+times[4]+times[5])/
_replica_size);
}
fprintf(screen,"CPU Poisson: %.4f s.\n",times[6]/_replica_size);
fprintf(screen,"CPU Data Cast: %.4f s.\n",times[8]/_replica_size);
fprintf(screen,"CPU Idle Time: %.4f s.\n",times[7]/_replica_size);
fprintf(screen,"Max Mem / Proc: %.2f MB.\n",max_mb);
fprintf(screen,"-------------------------------------");
fprintf(screen,"--------------------------------\n\n");
}
}
template <class numtyp, class acctyp>
void DeviceT::clear() {
if (_init_count>0) {
_long_range_precompute=0;
_init_count--;
if (_init_count==0) {
atom.clear();
_neighbor_shared.clear();
}
}
}
template <class numtyp, class acctyp>
void DeviceT::clear_device() {
while (_init_count>0)
clear();
if (_compiled) {
k_zero.clear();
k_info.clear();
delete dev_program;
_compiled=false;
}
if (_device_init) {
delete gpu;
_device_init=false;
}
}
template <class numtyp, class acctyp>
int DeviceT::compile_kernels() {
int flag=0;
if (_compiled)
return flag;
dev_program=new UCL_Program(*gpu);
int success=dev_program->load_string(device,compile_string().c_str(),
nullptr,stderr);
if (success!=UCL_SUCCESS)
return -6;
k_zero.set_function(*dev_program,"kernel_zero");
k_info.set_function(*dev_program,"kernel_info");
_compiled=true;
UCL_Vector<int,int> gpu_lib_data(19,*gpu,UCL_NOT_PINNED);
k_info.set_size(1,1);
k_info.run(&gpu_lib_data);
gpu_lib_data.update_host(false);
_ptx_arch=static_cast<double>(gpu_lib_data[0])/100.0;
#if !(defined(USE_OPENCL) || defined(USE_HIP))
if (_ptx_arch>gpu->arch() || floor(_ptx_arch)<floor(gpu->arch()))
return -4;
#endif
_config_id=gpu_lib_data[1];
if (sizeof(numtyp)==sizeof(float))
_simd_size=std::max(gpu_lib_data[2],gpu->preferred_fp32_width());
else
_simd_size=std::max(gpu_lib_data[2],gpu->preferred_fp64_width());
_num_mem_threads=gpu_lib_data[3];
_shuffle_avail=gpu_lib_data[4];
_fast_math=gpu_lib_data[5];
if (_threads_per_atom<1)
_threads_per_atom=gpu_lib_data[6];
if (_threads_per_charge<1)
_threads_per_charge=gpu_lib_data[7];
if (_threads_per_three<1)
_threads_per_three=gpu_lib_data[8];
if (_block_pair == -1) {
_block_pair=gpu_lib_data[9];
_block_bio_pair=gpu_lib_data[10];
_block_ellipse=gpu_lib_data[11];
} else {
_block_bio_pair=_block_pair;
_block_ellipse=_block_pair;
}
_pppm_block=gpu_lib_data[12];
_block_nbor_build=gpu_lib_data[13];
_block_cell_2d=gpu_lib_data[14];
_block_cell_id=gpu_lib_data[15];
_max_shared_types=gpu_lib_data[16];
_max_bio_shared_types=gpu_lib_data[17];
_pppm_max_spline=gpu_lib_data[18];
if (static_cast<size_t>(_block_pair)>gpu->group_size_dim(0) ||
static_cast<size_t>(_block_bio_pair)>gpu->group_size_dim(0) ||
static_cast<size_t>(_block_ellipse)>gpu->group_size_dim(0) ||
static_cast<size_t>(_pppm_block)>gpu->group_size_dim(0) ||
static_cast<size_t>(_block_nbor_build)>gpu->group_size_dim(0) ||
static_cast<size_t>(_block_cell_2d)>gpu->group_size_dim(0) ||
static_cast<size_t>(_block_cell_2d)>gpu->group_size_dim(1) ||
static_cast<size_t>(_block_cell_id)>gpu->group_size_dim(0) ||
static_cast<size_t>(_max_shared_types*_max_shared_types*
sizeof(numtyp)*17 > gpu->slm_size()) ||
static_cast<size_t>(_max_bio_shared_types*2*sizeof(numtyp) >
gpu->slm_size()))
return -13;
if (_block_pair % _simd_size != 0 || _block_bio_pair % _simd_size != 0 ||
_block_ellipse % _simd_size != 0 || _pppm_block % _simd_size != 0 ||
_block_nbor_build % _simd_size != 0 ||
_block_pair < _max_shared_types * _max_shared_types ||
_block_bio_pair * 2 < _max_bio_shared_types ||
_pppm_block < _pppm_max_spline * _pppm_max_spline)
return -11;
if (_threads_per_atom>_simd_size)
_threads_per_atom=_simd_size;
if (_simd_size%_threads_per_atom!=0)
_threads_per_atom=1;
if (_threads_per_atom & (_threads_per_atom - 1))
_threads_per_atom=1;
if (_threads_per_charge>_simd_size)
_threads_per_charge=_simd_size;
if (_simd_size%_threads_per_charge!=0)
_threads_per_charge=1;
if (_threads_per_charge & (_threads_per_charge - 1))
_threads_per_charge=1;
if (_threads_per_three>_simd_size)
_threads_per_three=_simd_size;
if (_simd_size%_threads_per_three!=0)
_threads_per_three=1;
if (_threads_per_three & (_threads_per_three - 1))
_threads_per_three=1;
return flag;
}
template <class numtyp, class acctyp>
double DeviceT::host_memory_usage() const {
return atom.host_memory_usage()+4*sizeof(numtyp)+
sizeof(Device<numtyp,acctyp>);
}
template class Device<PRECISION,ACC_PRECISION>;
Device<PRECISION,ACC_PRECISION> global_device;
}
using namespace LAMMPS_AL;
int lmp_init_device(MPI_Comm world, MPI_Comm replica, const int ngpu,
const int first_gpu_id, const int gpu_mode,
const double particle_split, const int t_per_atom,
const double user_cell_size, char *opencl_config,
const int ocl_platform, char *device_type_flags,
const int block_pair) {
return global_device.init_device(world,replica,ngpu,first_gpu_id,gpu_mode,
particle_split,t_per_atom,user_cell_size,
opencl_config,ocl_platform,
device_type_flags,block_pair);
}
void lmp_clear_device() {
global_device.clear_device();
}
double lmp_gpu_forces(double **f, double **tor, double *eatom,
double **vatom, double *virial, double &ecoul,
int &error_flag) {
return global_device.fix_gpu(f,tor,eatom,vatom,virial,ecoul,error_flag);
}
double lmp_gpu_update_bin_size(const double subx, const double suby,
const double subz, const int nlocal,
const double cut) {
return global_device._neighbor_shared.update_cell_size(subx, suby,
subz, nlocal, cut);
}
bool lmp_gpu_config(const std::string &category, const std::string &setting)
{
if (category == "api") {
#if defined(USE_OPENCL)
return setting == "opencl";
#elif defined(USE_HIP)
return setting == "hip";
#elif defined(USE_CUDA)
return setting == "cuda";
#endif
return false;
}
if (category == "precision") {
if (setting == "single") {
#if defined(_SINGLE_SINGLE)
return true;
#else
return false;
#endif
} else if (setting == "mixed") {
#if defined(_SINGLE_DOUBLE)
return true;
#else
return false;
#endif
} else if (setting == "double") {
#if defined(_DOUBLE_DOUBLE)
return true;
#else
return false;
#endif
} else return false;
}
return false;
}
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