/*
* =======================================================================================
*
* Author: Jan Eitzinger (je), jan.eitzinger@fau.de
* Copyright (c) 2021 RRZE, University Erlangen-Nuremberg
*
* This file is part of MD-Bench.
*
* MD-Bench is free software: you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published
* by the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* MD-Bench is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
* PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License along
* with MD-Bench. If not, see .
* =======================================================================================
*/
#include
#include
#include
#include
#include
#include
#include
extern "C" {
#include
#include
#include
#include
#include
#include
}
// cuda kernel
__global__ void calc_force(
Atom a,
MD_FLOAT cutforcesq, MD_FLOAT sigma6, MD_FLOAT epsilon,
int Nlocal, int neigh_maxneighs, int *neigh_neighbors, int *neigh_numneigh) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if( i >= Nlocal ) {
return;
}
Atom *atom = &a;
const int numneighs = neigh_numneigh[i];
MD_FLOAT xtmp = atom_x(i);
MD_FLOAT ytmp = atom_y(i);
MD_FLOAT ztmp = atom_z(i);
MD_FLOAT fix = 0;
MD_FLOAT fiy = 0;
MD_FLOAT fiz = 0;
for(int k = 0; k < numneighs; k++) {
int j = neigh_neighbors[atom->Nlocal * k + i];
MD_FLOAT delx = xtmp - atom_x(j);
MD_FLOAT dely = ytmp - atom_y(j);
MD_FLOAT delz = ztmp - atom_z(j);
MD_FLOAT rsq = delx * delx + dely * dely + delz * delz;
#ifdef EXPLICIT_TYPES
const int type_j = atom->type[j];
const int type_ij = type_i * atom->ntypes + type_j;
const MD_FLOAT cutforcesq = atom->cutforcesq[type_ij];
const MD_FLOAT sigma6 = atom->sigma6[type_ij];
const MD_FLOAT epsilon = atom->epsilon[type_ij];
#endif
if(rsq < cutforcesq) {
MD_FLOAT sr2 = 1.0 / rsq;
MD_FLOAT sr6 = sr2 * sr2 * sr2 * sigma6;
MD_FLOAT force = 48.0 * sr6 * (sr6 - 0.5) * sr2 * epsilon;
fix += delx * force;
fiy += dely * force;
fiz += delz * force;
}
}
atom_fx(i) = fix;
atom_fy(i) = fiy;
atom_fz(i) = fiz;
}
__global__ void kernel_initial_integrate(MD_FLOAT dtforce, MD_FLOAT dt, int Nlocal, Atom a) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if( i >= Nlocal ) {
return;
}
Atom *atom = &a;
atom_vx(i) += dtforce * atom_fx(i);
atom_vy(i) += dtforce * atom_fy(i);
atom_vz(i) += dtforce * atom_fz(i);
atom_x(i) = atom_x(i) + dt * atom_vx(i);
atom_y(i) = atom_y(i) + dt * atom_vy(i);
atom_z(i) = atom_z(i) + dt * atom_vz(i);
}
__global__ void kernel_final_integrate(MD_FLOAT dtforce, int Nlocal, Atom a) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if( i >= Nlocal ) {
return;
}
Atom *atom = &a;
atom_vx(i) += dtforce * atom_fx(i);
atom_vy(i) += dtforce * atom_fy(i);
atom_vz(i) += dtforce * atom_fz(i);
}
extern "C" {
int get_num_threads() {
const char *num_threads_env = getenv("NUM_THREADS");
int num_threads = 0;
if(num_threads_env == nullptr)
num_threads = 32;
else {
num_threads = atoi(num_threads_env);
}
return num_threads;
}
void cuda_final_integrate(bool doReneighbour, Parameter *param, Atom *atom, Atom *c_atom) {
const int Nlocal = atom->Nlocal;
const int num_threads = get_num_threads();
const int num_threads_per_block = num_threads; // this should be multiple of 32 as operations are performed at the level of warps
const int num_blocks = ceil((float)Nlocal / (float)num_threads_per_block);
kernel_final_integrate <<< num_blocks, num_threads_per_block >>> (param->dtforce, Nlocal, *c_atom);
checkCUDAError( "PeekAtLastError FinalIntegrate", cudaPeekAtLastError() );
checkCUDAError( "DeviceSync FinalIntegrate", cudaDeviceSynchronize() );
if(doReneighbour) {
checkCUDAError( "FinalIntegrate: velocity memcpy", cudaMemcpy(atom->vx, c_atom->vx, sizeof(MD_FLOAT) * atom->Nlocal * 3, cudaMemcpyDeviceToHost) );
}
}
void cuda_initial_integrate(bool doReneighbour, Parameter *param, Atom *atom, Atom *c_atom) {
const int Nlocal = atom->Nlocal;
const int num_threads = get_num_threads();
const int num_threads_per_block = num_threads; // this should be multiple of 32 as operations are performed at the level of warps
const int num_blocks = ceil((float)Nlocal / (float)num_threads_per_block);
kernel_initial_integrate <<< num_blocks, num_threads_per_block >>> (param->dtforce, param->dt, Nlocal, *c_atom);
checkCUDAError( "PeekAtLastError InitialIntegrate", cudaPeekAtLastError() );
checkCUDAError( "DeviceSync InitialIntegrate", cudaDeviceSynchronize() );
if(doReneighbour) {
checkCUDAError( "InitialIntegrate: velocity memcpy", cudaMemcpy(atom->vx, c_atom->vx, sizeof(MD_FLOAT) * atom->Nlocal * 3, cudaMemcpyDeviceToHost) );
}
checkCUDAError( "InitialIntegrate: position memcpy", cudaMemcpy(atom->x, c_atom->x, sizeof(MD_FLOAT) * atom->Nlocal * 3, cudaMemcpyDeviceToHost) );
}
double computeForce(
bool reneighbourHappenend,
Parameter *param,
Atom *atom,
Neighbor *neighbor,
Atom *c_atom,
Neighbor *c_neighbor
)
{
int Nlocal = atom->Nlocal;
#ifndef EXPLICIT_TYPES
MD_FLOAT cutforcesq = param->cutforce * param->cutforce;
MD_FLOAT sigma6 = param->sigma6;
MD_FLOAT epsilon = param->epsilon;
#endif
const int num_threads = get_num_threads();
c_atom->Natoms = atom->Natoms;
c_atom->Nlocal = atom->Nlocal;
c_atom->Nghost = atom->Nghost;
c_atom->Nmax = atom->Nmax;
c_atom->ntypes = atom->ntypes;
/*
int nDevices;
cudaGetDeviceCount(&nDevices);
size_t free, total;
for(int i = 0; i < nDevices; ++i) {
cudaMemGetInfo( &free, &total );
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, i);
printf("DEVICE %d/%d NAME: %s\r\n with %ld MB/%ld MB memory used", i + 1, nDevices, prop.name, free / 1024 / 1024, total / 1024 / 1024);
}
*/
// HINT: Run with cuda-memcheck ./MDBench-NVCC in case of error
// checkCUDAError( "c_atom->fx memset", cudaMemset(c_atom->fx, 0, sizeof(MD_FLOAT) * Nlocal * 3) );
cudaProfilerStart();
checkCUDAError( "c_atom->x memcpy", cudaMemcpy(c_atom->x, atom->x, sizeof(MD_FLOAT) * atom->Nmax * 3, cudaMemcpyHostToDevice) );
if(reneighbourHappenend) {
checkCUDAError( "c_neighbor->numneigh memcpy", cudaMemcpy(c_neighbor->numneigh, neighbor->numneigh, sizeof(int) * Nlocal, cudaMemcpyHostToDevice) );
checkCUDAError( "c_neighbor->neighbors memcpy", cudaMemcpy(c_neighbor->neighbors, neighbor->neighbors, sizeof(int) * Nlocal * neighbor->maxneighs, cudaMemcpyHostToDevice) );
}
const int num_threads_per_block = num_threads; // this should be multiple of 32 as operations are performed at the level of warps
const int num_blocks = ceil((float)Nlocal / (float)num_threads_per_block);
double S = getTimeStamp();
LIKWID_MARKER_START("force");
calc_force <<< num_blocks, num_threads_per_block >>> (*c_atom, cutforcesq, sigma6, epsilon, Nlocal, neighbor->maxneighs, c_neighbor->neighbors, c_neighbor->numneigh);
checkCUDAError( "PeekAtLastError ComputeForce", cudaPeekAtLastError() );
checkCUDAError( "DeviceSync ComputeForce", cudaDeviceSynchronize() );
cudaProfilerStop();
LIKWID_MARKER_STOP("force");
double E = getTimeStamp();
return E-S;
}
}