forked from lijiext/lammps
615 lines
20 KiB
C++
615 lines
20 KiB
C++
/* ----------------------------------------------------------------------
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LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
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http://lammps.sandia.gov, Sandia National Laboratories
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Steve Plimpton, sjplimp@sandia.gov
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Copyright (2003) Sandia Corporation. Under the terms of Contract
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DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains
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certain rights in this software. This software is distributed under
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the GNU General Public License.
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See the README file in the top-level LAMMPS directory.
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------------------------------------------------------------------------- */
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/* ----------------------------------------------------------------------
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Contributing authors: Mike Brown (ORNL), brownw@ornl.gov
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------------------------------------------------------------------------- */
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#include "pair_gpu_device.h"
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#include "pair_gpu_precision.h"
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#include <map>
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#include <math.h>
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#ifdef _OPENMP
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#include <omp.h>
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#endif
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#ifdef USE_OPENCL
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#include "pair_gpu_dev_cl.h"
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#else
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#include "pair_gpu_dev_ptx.h"
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#endif
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#define PairGPUDeviceT PairGPUDevice<numtyp, acctyp>
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template <class numtyp, class acctyp>
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PairGPUDeviceT::PairGPUDevice() : _init_count(0), _device_init(false),
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_gpu_mode(GPU_FORCE), _first_device(0),
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_last_device(0), _compiled(false) {
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}
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template <class numtyp, class acctyp>
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PairGPUDeviceT::~PairGPUDevice() {
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clear_device();
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}
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template <class numtyp, class acctyp>
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int PairGPUDeviceT::init_device(MPI_Comm world, MPI_Comm replica,
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const int first_gpu, const int last_gpu,
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const int gpu_mode, const double p_split,
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const int nthreads, const int t_per_atom) {
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_nthreads=nthreads;
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#ifdef _OPENMP
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omp_set_num_threads(nthreads);
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#endif
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_threads_per_atom=t_per_atom;
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_threads_per_charge=t_per_atom;
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if (_device_init)
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return 0;
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_device_init=true;
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_comm_world=world;
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_comm_replica=replica;
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_first_device=first_gpu;
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_last_device=last_gpu;
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_gpu_mode=gpu_mode;
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_particle_split=p_split;
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// Get the rank/size within the world
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MPI_Comm_rank(_comm_world,&_world_me);
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MPI_Comm_size(_comm_world,&_world_size);
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// Get the rank/size within the replica
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MPI_Comm_rank(_comm_replica,&_replica_me);
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MPI_Comm_size(_comm_replica,&_replica_size);
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// Get the names of all nodes
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int name_length;
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char node_name[MPI_MAX_PROCESSOR_NAME];
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char node_names[MPI_MAX_PROCESSOR_NAME*_world_size];
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MPI_Get_processor_name(node_name,&name_length);
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MPI_Allgather(&node_name,MPI_MAX_PROCESSOR_NAME,MPI_CHAR,&node_names,
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MPI_MAX_PROCESSOR_NAME,MPI_CHAR,_comm_world);
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std::string node_string=std::string(node_name);
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// Get the number of procs per node
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std::map<std::string,int> name_map;
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std::map<std::string,int>::iterator np;
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for (int i=0; i<_world_size; i++) {
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std::string i_string=std::string(&node_names[i*MPI_MAX_PROCESSOR_NAME]);
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np=name_map.find(i_string);
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if (np==name_map.end())
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name_map[i_string]=1;
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else
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np->second++;
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}
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int procs_per_node=name_map.begin()->second;
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// Assign a unique id to each node
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int split_num=0, split_id=0;
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for (np=name_map.begin(); np!=name_map.end(); ++np) {
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if (np->first==node_string)
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split_id=split_num;
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split_num++;
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}
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// Set up a per node communicator and find rank within
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MPI_Comm node_comm;
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MPI_Comm_split(_comm_world, split_id, 0, &node_comm);
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int node_rank;
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MPI_Comm_rank(node_comm,&node_rank);
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// set the device ID
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_procs_per_gpu=static_cast<int>(ceil(static_cast<double>(procs_per_node)/
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(last_gpu-first_gpu+1)));
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int my_gpu=node_rank/_procs_per_gpu+first_gpu;
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// Time on the device only if 1 proc per gpu
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_time_device=true;
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if (_procs_per_gpu>1)
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_time_device=false;
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// Set up a per device communicator
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MPI_Comm_split(node_comm,my_gpu,0,&_comm_gpu);
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MPI_Comm_rank(_comm_gpu,&_gpu_rank);
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gpu=new UCL_Device();
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if (my_gpu>=gpu->num_devices())
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return -2;
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gpu->set(my_gpu);
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_long_range_precompute=0;
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int flag=compile_kernels();
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return flag;
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}
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template <class numtyp, class acctyp>
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int PairGPUDeviceT::init(PairGPUAns<numtyp,acctyp> &ans, const bool charge,
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const bool rot, const int nlocal,
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const int host_nlocal, const int nall,
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PairGPUNbor *nbor, const int maxspecial,
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const int gpu_host, const int max_nbors,
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const double cell_size, const bool pre_cut) {
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if (!_device_init)
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return -1;
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if (sizeof(acctyp)==sizeof(double) && gpu->double_precision()==false)
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return -5;
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// Counts of data transfers for timing overhead estimates
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_data_in_estimate=0;
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_data_out_estimate=1;
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// Initial number of local particles
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int ef_nlocal=nlocal;
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if (_particle_split<1.0 && _particle_split>0.0)
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ef_nlocal=static_cast<int>(_particle_split*nlocal);
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bool gpu_nbor=false;
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if (_gpu_mode==GPU_NEIGH)
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gpu_nbor=true;
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if (_init_count==0) {
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// Initialize atom and nbor data
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if (!atom.init(nall,charge,rot,*gpu,gpu_nbor,gpu_nbor && maxspecial>0))
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return -3;
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_data_in_estimate++;
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if (charge)
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_data_in_estimate++;
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if (rot)
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_data_in_estimate++;
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} else {
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if (atom.charge()==false && charge)
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_data_in_estimate++;
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if (atom.quat()==false && rot)
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_data_in_estimate++;
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if (!atom.add_fields(charge,rot,gpu_nbor,gpu_nbor && maxspecial))
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return -3;
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}
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if (!ans.init(ef_nlocal,charge,rot,*gpu))
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return -3;
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if (!nbor->init(&_nbor_shared,ef_nlocal,host_nlocal,max_nbors,maxspecial,
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*gpu,gpu_nbor,gpu_host,pre_cut, _block_cell_2d,
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_block_cell_id, _block_nbor_build))
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return -3;
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nbor->cell_size(cell_size);
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_init_count++;
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return 0;
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}
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template <class numtyp, class acctyp>
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int PairGPUDeviceT::init(PairGPUAns<numtyp,acctyp> &ans, const int nlocal,
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const int nall) {
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if (!_device_init)
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return -1;
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if (sizeof(acctyp)==sizeof(double) && gpu->double_precision()==false)
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return -5;
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if (_init_count==0) {
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// Initialize atom and nbor data
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if (!atom.init(nall,true,false,*gpu,false,false))
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return -3;
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} else
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if (!atom.add_fields(true,false,false,false))
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return -3;
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if (!ans.init(nlocal,true,false,*gpu))
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return -3;
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_init_count++;
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return 0;
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}
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template <class numtyp, class acctyp>
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void PairGPUDeviceT::set_single_precompute
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(PPPMGPUMemory<numtyp,acctyp,float,_lgpu_float4> *pppm) {
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_long_range_precompute=1;
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pppm_single=pppm;
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}
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template <class numtyp, class acctyp>
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void PairGPUDeviceT::set_double_precompute
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(PPPMGPUMemory<numtyp,acctyp,double,_lgpu_double4> *pppm) {
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_long_range_precompute=2;
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pppm_double=pppm;
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}
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template <class numtyp, class acctyp>
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void PairGPUDeviceT::init_message(FILE *screen, const char *name,
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const int first_gpu, const int last_gpu) {
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#ifdef USE_OPENCL
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std::string fs="";
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#else
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std::string fs=toa(gpu->free_gigabytes())+"/";
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#endif
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if (_replica_me == 0 && screen) {
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fprintf(screen,"\n-------------------------------------");
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fprintf(screen,"-------------------------------------\n");
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fprintf(screen,"- Using GPGPU acceleration for %s:\n",name);
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fprintf(screen,"- with %d proc(s) per device.\n",_procs_per_gpu);
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#ifdef _OPENMP
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fprintf(screen,"- with %d thread(s) per proc.\n",_nthreads);
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#endif
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fprintf(screen,"-------------------------------------");
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fprintf(screen,"-------------------------------------\n");
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int last=last_gpu+1;
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if (last>gpu->num_devices())
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last=gpu->num_devices();
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for (int i=first_gpu; i<last; i++) {
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std::string sname=gpu->name(i)+", "+toa(gpu->cores(i))+" cores, "+fs+
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toa(gpu->gigabytes(i))+" GB, "+toa(gpu->clock_rate(i))+
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" GHZ (";
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if (sizeof(PRECISION)==4) {
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if (sizeof(ACC_PRECISION)==4)
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sname+="Single Precision)";
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else
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sname+="Mixed Precision)";
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} else
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sname+="Double Precision)";
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fprintf(screen,"GPU %d: %s\n",i,sname.c_str());
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}
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fprintf(screen,"-------------------------------------");
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fprintf(screen,"-------------------------------------\n\n");
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}
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}
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template <class numtyp, class acctyp>
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void PairGPUDeviceT::estimate_gpu_overhead(const int kernel_calls,
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double &gpu_overhead,
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double &gpu_driver_overhead) {
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UCL_H_Vec<int> *host_data_in=NULL, *host_data_out=NULL;
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UCL_D_Vec<int> *dev_data_in=NULL, *dev_data_out=NULL, *kernel_data=NULL;
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UCL_Timer *timers_in=NULL, *timers_out=NULL, *timers_kernel=NULL;
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UCL_Timer over_timer(*gpu);
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if (_data_in_estimate>0) {
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host_data_in=new UCL_H_Vec<int>[_data_in_estimate];
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dev_data_in=new UCL_D_Vec<int>[_data_in_estimate];
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timers_in=new UCL_Timer[_data_in_estimate];
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}
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if (_data_out_estimate>0) {
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host_data_out=new UCL_H_Vec<int>[_data_out_estimate];
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dev_data_out=new UCL_D_Vec<int>[_data_out_estimate];
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timers_out=new UCL_Timer[_data_out_estimate];
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}
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if (kernel_calls>0) {
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kernel_data=new UCL_D_Vec<int>[kernel_calls];
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timers_kernel=new UCL_Timer[kernel_calls];
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}
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for (int i=0; i<_data_in_estimate; i++) {
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host_data_in[i].alloc(1,*gpu);
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dev_data_in[i].alloc(1,*gpu);
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timers_in[i].init(*gpu);
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}
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for (int i=0; i<_data_out_estimate; i++) {
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host_data_out[i].alloc(1,*gpu);
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dev_data_out[i].alloc(1,*gpu);
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timers_out[i].init(*gpu);
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}
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for (int i=0; i<kernel_calls; i++) {
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kernel_data[i].alloc(1,*gpu);
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timers_kernel[i].init(*gpu);
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}
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gpu_overhead=0.0;
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gpu_driver_overhead=0.0;
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for (int i=0; i<10; i++) {
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gpu->sync();
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gpu_barrier();
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over_timer.start();
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gpu->sync();
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gpu_barrier();
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double driver_time=MPI_Wtime();
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for (int i=0; i<_data_in_estimate; i++) {
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timers_in[i].start();
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ucl_copy(dev_data_in[i],host_data_in[i],true);
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timers_in[i].stop();
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}
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for (int i=0; i<kernel_calls; i++) {
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timers_kernel[i].start();
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zero(kernel_data[i],1);
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timers_kernel[i].stop();
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}
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for (int i=0; i<_data_out_estimate; i++) {
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timers_out[i].start();
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ucl_copy(host_data_out[i],dev_data_out[i],true);
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timers_out[i].stop();
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}
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over_timer.stop();
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double time=over_timer.seconds();
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driver_time=MPI_Wtime()-driver_time;
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if (time_device()) {
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for (int i=0; i<_data_in_estimate; i++)
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timers_in[i].add_to_total();
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for (int i=0; i<kernel_calls; i++)
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timers_kernel[i].add_to_total();
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for (int i=0; i<_data_out_estimate; i++)
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timers_out[i].add_to_total();
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}
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double mpi_time, mpi_driver_time;
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MPI_Allreduce(&time,&mpi_time,1,MPI_DOUBLE,MPI_MAX,gpu_comm());
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MPI_Allreduce(&driver_time,&mpi_driver_time,1,MPI_DOUBLE,MPI_MAX,gpu_comm());
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gpu_overhead+=mpi_time;
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gpu_driver_overhead+=mpi_driver_time;
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}
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gpu_overhead/=10.0;
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gpu_driver_overhead/=10.0;
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if (_data_in_estimate>0) {
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delete [] host_data_in;
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delete [] dev_data_in;
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delete [] timers_in;
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}
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if (_data_out_estimate>0) {
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delete [] host_data_out;
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delete [] dev_data_out;
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delete [] timers_out;
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}
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if (kernel_calls>0) {
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delete [] kernel_data;
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delete [] timers_kernel;
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}
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}
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template <class numtyp, class acctyp>
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void PairGPUDeviceT::output_times(UCL_Timer &time_pair,
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PairGPUAns<numtyp,acctyp> &ans,
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PairGPUNbor &nbor, const double avg_split,
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const double max_bytes,
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const double gpu_overhead,
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const double driver_overhead,
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const int threads_per_atom, FILE *screen) {
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double single[8], times[8];
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single[0]=atom.transfer_time()+ans.transfer_time();
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single[1]=nbor.time_nbor.total_seconds();
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single[2]=nbor.time_kernel.total_seconds();
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single[3]=time_pair.total_seconds();
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single[4]=atom.cast_time()+ans.cast_time();
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single[5]=gpu_overhead;
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single[6]=driver_overhead;
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single[7]=ans.cpu_idle_time();
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MPI_Reduce(single,times,8,MPI_DOUBLE,MPI_SUM,0,_comm_replica);
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double my_max_bytes=max_bytes+atom.max_gpu_bytes();
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double mpi_max_bytes;
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MPI_Reduce(&my_max_bytes,&mpi_max_bytes,1,MPI_DOUBLE,MPI_MAX,0,_comm_replica);
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double max_mb=mpi_max_bytes/(1024.0*1024.0);
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if (replica_me()==0)
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if (screen && times[5]>0.0) {
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fprintf(screen,"\n\n-------------------------------------");
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fprintf(screen,"--------------------------------\n");
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fprintf(screen," GPU Time Info (average): ");
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fprintf(screen,"\n-------------------------------------");
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fprintf(screen,"--------------------------------\n");
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if (time_device()) {
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fprintf(screen,"Data Transfer: %.4f s.\n",times[0]/_replica_size);
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fprintf(screen,"Data Cast/Pack: %.4f s.\n",times[4]/_replica_size);
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fprintf(screen,"Neighbor copy: %.4f s.\n",times[1]/_replica_size);
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if (nbor.gpu_nbor())
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fprintf(screen,"Neighbor build: %.4f s.\n",times[2]/_replica_size);
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else
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fprintf(screen,"Neighbor unpack: %.4f s.\n",times[2]/_replica_size);
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fprintf(screen,"Force calc: %.4f s.\n",times[3]/_replica_size);
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}
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fprintf(screen,"GPU Overhead: %.4f s.\n",times[5]/_replica_size);
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fprintf(screen,"Average split: %.4f.\n",avg_split);
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fprintf(screen,"Threads / atom: %d.\n",threads_per_atom);
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fprintf(screen,"Max Mem / Proc: %.2f MB.\n",max_mb);
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fprintf(screen,"CPU Driver_Time: %.4f s.\n",times[6]/_replica_size);
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fprintf(screen,"CPU Idle_Time: %.4f s.\n",times[7]/_replica_size);
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fprintf(screen,"-------------------------------------");
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fprintf(screen,"--------------------------------\n\n");
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}
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}
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template <class numtyp, class acctyp>
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void PairGPUDeviceT::output_kspace_times(UCL_Timer &time_in,
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UCL_Timer &time_out,
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UCL_Timer &time_map,
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UCL_Timer &time_rho,
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UCL_Timer &time_interp,
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PairGPUAns<numtyp,acctyp> &ans,
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const double max_bytes,
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const double cpu_time,
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const double idle_time, FILE *screen) {
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double single[8], times[8];
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single[0]=time_out.total_seconds();
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single[1]=time_in.total_seconds()+atom.transfer_time()+atom.cast_time();
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single[2]=time_map.total_seconds();
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single[3]=time_rho.total_seconds();
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single[4]=time_interp.total_seconds();
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single[5]=ans.transfer_time()+ans.cast_time();
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single[6]=cpu_time;
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single[7]=idle_time;
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MPI_Reduce(single,times,8,MPI_DOUBLE,MPI_SUM,0,_comm_replica);
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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);
|
|
|
|
if (replica_me()==0)
|
|
if (screen && times[6]>0.0) {
|
|
fprintf(screen,"\n\n-------------------------------------");
|
|
fprintf(screen,"--------------------------------\n");
|
|
fprintf(screen," GPU Time Info (average): ");
|
|
fprintf(screen,"\n-------------------------------------");
|
|
fprintf(screen,"--------------------------------\n");
|
|
|
|
if (time_device()) {
|
|
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/cast: %.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 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 PairGPUDeviceT::clear() {
|
|
if (_init_count>0) {
|
|
_long_range_precompute=0;
|
|
_init_count--;
|
|
if (_init_count==0) {
|
|
atom.clear();
|
|
_nbor_shared.clear();
|
|
if (_compiled) {
|
|
k_zero.clear();
|
|
k_info.clear();
|
|
delete dev_program;
|
|
_compiled=false;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <class numtyp, class acctyp>
|
|
void PairGPUDeviceT::clear_device() {
|
|
while (_init_count>0)
|
|
clear();
|
|
if (_device_init) {
|
|
delete gpu;
|
|
_device_init=false;
|
|
}
|
|
}
|
|
|
|
template <class numtyp, class acctyp>
|
|
int PairGPUDeviceT::compile_kernels() {
|
|
int flag=0;
|
|
|
|
if (_compiled)
|
|
return flag;
|
|
|
|
std::string flags="-cl-mad-enable";
|
|
dev_program=new UCL_Program(*gpu);
|
|
int success=dev_program->load_string(pair_gpu_dev_kernel,flags.c_str());
|
|
if (success!=UCL_SUCCESS)
|
|
return -4;
|
|
k_zero.set_function(*dev_program,"kernel_zero");
|
|
k_info.set_function(*dev_program,"kernel_info");
|
|
_compiled=true;
|
|
|
|
UCL_H_Vec<int> h_gpu_lib_data(14,*gpu,UCL_NOT_PINNED);
|
|
UCL_D_Vec<int> d_gpu_lib_data(14,*gpu);
|
|
k_info.set_size(1,1);
|
|
k_info.run(&d_gpu_lib_data.begin());
|
|
ucl_copy(h_gpu_lib_data,d_gpu_lib_data,false);
|
|
|
|
_ptx_arch=static_cast<double>(h_gpu_lib_data[0])/100.0;
|
|
#ifndef USE_OPENCL
|
|
if (_ptx_arch>gpu->arch())
|
|
return -4;
|
|
#endif
|
|
|
|
_num_mem_threads=h_gpu_lib_data[1];
|
|
_warp_size=h_gpu_lib_data[2];
|
|
if (_threads_per_atom<1)
|
|
_threads_per_atom=h_gpu_lib_data[3];
|
|
if (_threads_per_charge<1)
|
|
_threads_per_charge=h_gpu_lib_data[13];
|
|
_pppm_max_spline=h_gpu_lib_data[4];
|
|
_pppm_block=h_gpu_lib_data[5];
|
|
_block_pair=h_gpu_lib_data[6];
|
|
_max_shared_types=h_gpu_lib_data[7];
|
|
_block_cell_2d=h_gpu_lib_data[8];
|
|
_block_cell_id=h_gpu_lib_data[9];
|
|
_block_nbor_build=h_gpu_lib_data[10];
|
|
_block_bio_pair=h_gpu_lib_data[11];
|
|
_max_bio_shared_types=h_gpu_lib_data[12];
|
|
|
|
if (static_cast<size_t>(_block_pair)>gpu->group_size())
|
|
_block_pair=gpu->group_size();
|
|
if (static_cast<size_t>(_block_bio_pair)>gpu->group_size())
|
|
_block_bio_pair=gpu->group_size();
|
|
if (_threads_per_atom>_warp_size)
|
|
_threads_per_atom=_warp_size;
|
|
if (_warp_size%_threads_per_atom!=0)
|
|
_threads_per_atom=1;
|
|
if (_threads_per_charge>_warp_size)
|
|
_threads_per_charge=_warp_size;
|
|
if (_warp_size%_threads_per_charge!=0)
|
|
_threads_per_charge=1;
|
|
|
|
return flag;
|
|
}
|
|
|
|
template <class numtyp, class acctyp>
|
|
double PairGPUDeviceT::host_memory_usage() const {
|
|
return atom.host_memory_usage()+4*sizeof(numtyp)+
|
|
sizeof(PairGPUDevice<numtyp,acctyp>);
|
|
}
|
|
|
|
template class PairGPUDevice<PRECISION,ACC_PRECISION>;
|
|
PairGPUDevice<PRECISION,ACC_PRECISION> pair_gpu_device;
|
|
|
|
int lmp_init_device(MPI_Comm world, MPI_Comm replica, const int first_gpu,
|
|
const int last_gpu, const int gpu_mode,
|
|
const double particle_split, const int nthreads,
|
|
const int t_per_atom) {
|
|
return pair_gpu_device.init_device(world,replica,first_gpu,last_gpu,gpu_mode,
|
|
particle_split,nthreads,t_per_atom);
|
|
}
|
|
|
|
void lmp_clear_device() {
|
|
pair_gpu_device.clear_device();
|
|
}
|
|
|
|
double lmp_gpu_forces(double **f, double **tor, double *eatom,
|
|
double **vatom, double *virial, double &ecoul) {
|
|
return pair_gpu_device.fix_gpu(f,tor,eatom,vatom,virial,ecoul);
|
|
}
|