Diffusion lammps (efficient_neuronet) Browse

lal_soft_ext.cpp
e1ae2b2e65c9efficient_neuronet

lal_soft_ext.cpp
View Options

			/***************************************************************************
			soft_ext.cpp
			-------------------
			Trung Dac Nguyen (ORNL)

			Functions for LAMMPS access to soft acceleration routines.

			__________________________________________________________________________
			This file is part of the LAMMPS Accelerator Library (LAMMPS_AL)
			__________________________________________________________________________

			begin :
			email : nguyentd@ornl.gov
			***************************************************************************/

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

			#include "lal_soft.h"

			using namespace std;
			using namespace LAMMPS_AL;

			static Soft<PRECISION,ACC_PRECISION> SLMF;

			// ---------------------------------------------------------------------------
			// Allocate memory on host and device and copy constants to device
			// ---------------------------------------------------------------------------
			int soft_gpu_init(const int ntypes, double cutsq, double host_prefactor,
			double *host_cut, double special_lj,
			const int inum, const int nall, const int max_nbors,
			const int maxspecial,
			const double cell_size, int &gpu_mode, FILE *screen) {
			SLMF.clear();
			gpu_mode=SLMF.device->gpu_mode();
			double gpu_split=SLMF.device->particle_split();
			int first_gpu=SLMF.device->first_device();
			int last_gpu=SLMF.device->last_device();
			int world_me=SLMF.device->world_me();
			int gpu_rank=SLMF.device->gpu_rank();
			int procs_per_gpu=SLMF.device->procs_per_gpu();

			SLMF.device->init_message(screen,"soft",first_gpu,last_gpu);

			bool message=false;
			if (SLMF.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=SLMF.init(ntypes, cutsq, host_prefactor, host_cut,
			special_lj, inum, nall, 300,
			maxspecial, cell_size, gpu_split, screen);

			SLMF.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=SLMF.init(ntypes, cutsq, host_prefactor, host_cut,
			special_lj, inum, nall, 300, maxspecial,
			cell_size, gpu_split, screen);

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

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

			// ---------------------------------------------------------------------------
			// Copy updated constants to device
			// ---------------------------------------------------------------------------
			void soft_gpu_reinit(const int ntypes, double cutsq, double host_prefactor,
			double **host_cut) {
			int world_me=SLMF.device->world_me();
			int gpu_rank=SLMF.device->gpu_rank();
			int procs_per_gpu=SLMF.device->procs_per_gpu();

			if (world_me==0)
			SLMF.reinit(ntypes, cutsq, host_prefactor, host_cut);

			SLMF.device->world_barrier();

			for (int i=0; i<procs_per_gpu; i++) {
			if (gpu_rank==i && world_me!=0)
			SLMF.reinit(ntypes, cutsq, host_prefactor, host_cut);

			SLMF.device->gpu_barrier();
			}
			}

			void soft_gpu_clear() {
			SLMF.clear();
			}

			int ** soft_gpu_compute_n(const int ago, const int inum_full,
			const int nall, double *host_x, int host_type,
			double sublo, double subhi, tagint tag, int *nspecial,
			tagint **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) {
			return SLMF.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);
			}

			void soft_gpu_compute(const int ago, const int inum_full, 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) {
			SLMF.compute(ago,inum_full,nall,host_x,host_type,ilist,numj,
			firstneigh,eflag,vflag,eatom,vatom,host_start,cpu_time,success);
			}

			double soft_gpu_bytes() {
			return SLMF.host_memory_usage();
			}

lammps/lib/gpu/lal_soft_ext.cppe1ae2b2e65c9efficient_neuronet

lal_soft_ext.cppView Options

lammps/lib/gpu/lal_soft_ext.cpp
e1ae2b2e65c9efficient_neuronet

lal_soft_ext.cpp
View Options