71ff7b8096
computations. Changed the load balancing scheme. Added timing estimates for GPU and driver overhead, cpu idle time.
126 lines
4.6 KiB
C++
126 lines
4.6 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 <iostream>
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#include <cassert>
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#include <math.h>
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#include "cmm_cut_gpu_memory.h"
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using namespace std;
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static CMM_GPU_Memory<PRECISION,ACC_PRECISION> CMMMF;
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// ---------------------------------------------------------------------------
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// Allocate memory on host and device and copy constants to device
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// ---------------------------------------------------------------------------
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bool cmm_gpu_init(const int ntypes, double **cutsq, int **cg_types,
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double **host_lj1, double **host_lj2, double **host_lj3,
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double **host_lj4, double **offset, double *special_lj,
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const int inum, const int nall, const int max_nbors,
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const int maxspecial, const double cell_size, int &gpu_mode,
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FILE *screen) {
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CMMMF.clear();
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gpu_mode=CMMMF.device->gpu_mode();
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double gpu_split=CMMMF.device->particle_split();
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int first_gpu=CMMMF.device->first_device();
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int last_gpu=CMMMF.device->last_device();
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int world_me=CMMMF.device->world_me();
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int gpu_rank=CMMMF.device->gpu_rank();
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int procs_per_gpu=CMMMF.device->procs_per_gpu();
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CMMMF.device->init_message(screen,"cg/cmm",first_gpu,last_gpu);
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bool message=false;
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if (CMMMF.device->replica_me()==0 && screen)
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message=true;
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if (message) {
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fprintf(screen,"Initializing GPU and compiling on process 0...");
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fflush(screen);
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}
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if (world_me==0) {
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bool init_ok=CMMMF.init(ntypes,cutsq,cg_types,host_lj1,host_lj2,host_lj3,
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host_lj4, offset, special_lj, inum, nall, 300,
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maxspecial, cell_size, gpu_split, screen);
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if (!init_ok)
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return false;
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}
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CMMMF.device->world_barrier();
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if (message)
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fprintf(screen,"Done.\n");
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for (int i=0; i<procs_per_gpu; i++) {
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if (message) {
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if (last_gpu-first_gpu==0)
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fprintf(screen,"Initializing GPU %d on core %d...",first_gpu,i);
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else
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fprintf(screen,"Initializing GPUs %d-%d on core %d...",first_gpu,
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last_gpu,i);
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fflush(screen);
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}
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if (gpu_rank==i && world_me!=0) {
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bool init_ok=CMMMF.init(ntypes,cutsq,cg_types,host_lj1,host_lj2,host_lj3,
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host_lj4, offset, special_lj, inum, nall, 300,
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maxspecial, cell_size, gpu_split,
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screen);
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if (!init_ok)
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return false;
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}
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CMMMF.device->gpu_barrier();
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if (message)
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fprintf(screen,"Done.\n");
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}
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if (message)
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fprintf(screen,"\n");
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CMMMF.estimate_gpu_overhead();
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return true;
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}
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void cmm_gpu_clear() {
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CMMMF.clear();
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}
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int** cmm_gpu_compute_n(const int ago, const int inum_full,
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const int nall, double **host_x, int *host_type,
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double *boxlo, double *boxhi, int *tag, int **nspecial,
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int **special, const bool eflag, const bool vflag,
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const bool eatom, const bool vatom, int &host_start,
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int **ilist, int **jnum, const double cpu_time,
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bool &success) {
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return CMMMF.compute(ago, inum_full, nall, host_x, host_type, boxlo,
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boxhi, tag, nspecial, special, eflag, vflag, eatom,
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vatom, host_start, ilist, jnum, cpu_time, success);
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}
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void cmm_gpu_compute(const int ago, const int inum_full, const int nall,
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double **host_x, int *host_type, int *ilist, int *numj,
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int **firstneigh, const bool eflag, const bool vflag,
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const bool eatom, const bool vatom, int &host_start,
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const double cpu_time, bool &success) {
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CMMMF.compute(ago,inum_full,nall,host_x,host_type,ilist,numj,
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firstneigh,eflag,vflag,eatom,vatom,host_start,cpu_time,success);
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}
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double cmm_gpu_bytes() {
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return CMMMF.host_memory_usage();
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}
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