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lal_eam_lj_ext.cpp
29ae4bd8dc3defficient_neuronet

lal_eam_lj_ext.cpp
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			// **************************************************************************
			// lal_eam_ext.cpp
			// -------------------
			// W. Michael Brown, Trung Dac Nguyen (ORNL)
			//
			// Class for acceleration of the eam pair style
			//
			// __________________________________________________________________________
			// This file is part of the LAMMPS Accelerator Library (LAMMPS_AL)
			// __________________________________________________________________________
			//
			// begin :
			// email : brownw@ornl.gov nguyentd@ornl.gov
			// ***************************************************************************/

			#include <iostream>
			#include <cassert>
			#include <math.h>

			#include "lal_eam_lj.h"

			using namespace std;
			using namespace LAMMPS_AL;

			static EAMLJ<PRECISION,ACC_PRECISION> EAMLJMF;

			// ---------------------------------------------------------------------------
			// Allocate memory on host and device and copy constants to device
			// ---------------------------------------------------------------------------
			int eam_lj_gpu_init(const int ntypes, double host_cutforcesq,
			int host_type2rhor, int host_type2z2r, int *host_type2frho,
			double *host_rhor_spline, double *host_z2r_spline,
			double *host_frho_spline, double host_cutljsq,
			double host_lj1, double host_lj2, double **host_lj3,
			double host_lj4, double offset, double *special_lj,
			double rdr, double rdrho, int nrhor,
			int nrho, int nz2r, int nfrho, int nr,
			const int nlocal, const int nall, const int max_nbors,
			const int maxspecial, const double cell_size,
			int &gpu_mode, FILE *screen, int &fp_size) {
			EAMLJMF.clear();
			gpu_mode=EAMLJMF.device->gpu_mode();
			double gpu_split=EAMLJMF.device->particle_split();
			int first_gpu=EAMLJMF.device->first_device();
			int last_gpu=EAMLJMF.device->last_device();
			int world_me=EAMLJMF.device->world_me();
			int gpu_rank=EAMLJMF.device->gpu_rank();
			int procs_per_gpu=EAMLJMF.device->procs_per_gpu();

			// disable host/device split for now
			if (gpu_split != 1.0)
			return -8;

			fp_size=sizeof(PRECISION);

			EAMLJMF.device->init_message(screen,"eam_lj",first_gpu,last_gpu);

			bool message=false;
			if (EAMLJMF.device->replica_me()==0 && screen)
			message=true;

			if (message) {
			fprintf(screen,"Initializing Device and compiling on process 0...");
			fflush(screen);
			}

			int init_ok=0;
			if (world_me==0)
			init_ok=EAMLJMF.init(ntypes, host_cutforcesq,
			host_type2rhor, host_type2z2r, host_type2frho,
			host_rhor_spline, host_z2r_spline,
			host_frho_spline, host_cutljsq, host_lj1,
			host_lj2, host_lj3, host_lj4,
			offset, special_lj, rdr, rdrho, nrhor,
			nrho, nz2r, nfrho, nr,
			nlocal, nall, 300, maxspecial,
			cell_size, gpu_split, screen);

			EAMLJMF.device->world_barrier();
			if (message)
			fprintf(screen,"Done.\n");

			for (int i=0; i<procs_per_gpu; i++) {
			if (message) {
			if (last_gpu-first_gpu==0)
			fprintf(screen,"Initializing Device %d on core %d...",first_gpu,i);
			else
			fprintf(screen,"Initializing Devices %d-%d on core %d...",first_gpu,
			last_gpu,i);
			fflush(screen);
			}
			if (gpu_rank==i && world_me!=0)
			init_ok=EAMLJMF.init(ntypes, host_cutforcesq,
			host_type2rhor, host_type2z2r, host_type2frho,
			host_rhor_spline, host_z2r_spline,
			host_frho_spline, host_cutljsq, host_lj1,
			host_lj2, host_lj3, host_lj4,
			offset, special_lj, rdr, rdrho, nrhor,
			nrho, nz2r, nfrho, nr,
			nlocal, nall, 300, maxspecial,
			cell_size, gpu_split, screen);

			EAMLJMF.device->gpu_barrier();
			if (message)
			fprintf(screen,"Done.\n");
			}
			if (message)
			fprintf(screen,"\n");

			if (init_ok==0)
			EAMLJMF.estimate_gpu_overhead();
			return init_ok;
			}

			void eam_lj_gpu_clear() {
			EAMLJMF.clear();
			}

			int ** eam_lj_gpu_compute_n(const int ago, const int inum_full,
			const int nall, double *host_x, int host_type,
			double sublo, double subhi, int tag, int *nspecial,
			int **special, const bool eflag, const bool vflag,
			const bool eatom, const bool vatom, int &host_start,
			int ilist, int jnum, const double cpu_time,
			bool &success, int &inum, void **fp_ptr) {
			return EAMLJMF.compute(ago, inum_full, nall, host_x, host_type, sublo,
			subhi, tag, nspecial, special, eflag, vflag, eatom,
			vatom, host_start, ilist, jnum, cpu_time, success,
			inum, fp_ptr);
			}

			void eam_lj_gpu_compute(const int ago, const int inum_full, const int nlocal,
			const int nall, double *host_x, int host_type,
			int ilist, int numj, int **firstneigh, const bool eflag,
			const bool vflag, const bool eatom, const bool vatom,
			int &host_start, const double cpu_time, bool &success,
			void **fp_ptr) {
			EAMLJMF.compute(ago,inum_full,nlocal,nall,host_x,host_type,ilist,numj,
			firstneigh,eflag,vflag,eatom,vatom,host_start,cpu_time,success,
			fp_ptr);
			}

			void eam_lj_gpu_compute_force(int *ilist, const bool eflag, const bool vflag,
			const bool eatom, const bool vatom) {
			EAMLJMF.compute2(ilist, eflag, vflag, eatom, vatom);
			}


			double eam_lj_gpu_bytes() {
			return EAMLJMF.host_memory_usage();
			}

lammps/lib/gpu/lal_eam_lj_ext.cpp29ae4bd8dc3defficient_neuronet

lal_eam_lj_ext.cppView Options

lammps/lib/gpu/lal_eam_lj_ext.cpp
29ae4bd8dc3defficient_neuronet

lal_eam_lj_ext.cpp
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