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lammps/lib/gpu/balance.h
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balance.h
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/***************************************************************************
balance.h
-------------------
W. Michael Brown (ORNL)
Class for host-device load balancing
__________________________________________________________________________
This file is part of the LAMMPS Accelerator Library (LAMMPS_AL)
__________________________________________________________________________
begin :
email : brownw@ornl.gov
***************************************************************************/
#ifndef LAL_BALANCE_H
#define LAL_BALANCE_H
#include "device.h"
#include <math.h>
#define _HD_BALANCE_EVERY 25
#define _HD_BALANCE_WEIGHT 0.5
#define _HD_BALANCE_GAP 1.10
/// Host/device load balancer
template<class numtyp, class acctyp>
class Balance {
public:
inline Balance() : _init_done(false), _measure_this_step(false) {}
inline ~Balance() { clear(); }
/// Clear any old data and setup for new LAMMPS run
inline void init(Device<numtyp, acctyp> *gpu, const bool gpu_nbor,
const double split);
/// Clear all host and device data
inline void clear() {
if (_init_done) {
_device_time.clear();
_measure_this_step=false;
_init_done=false;
}
}
/// Return the timestep since initialization
inline int timestep() { return _timestep; }
/// Get a count of the number of particles host will handle for initial alloc
inline int first_host_count(const int nlocal, const double gpu_split,
const bool gpu_nbor) const {
int host_nlocal=0;
if (gpu_nbor && gpu_split!=1.0) {
if (gpu_split>0)
host_nlocal=static_cast<int>(ceil((1.0-gpu_split)*nlocal));
else
host_nlocal=static_cast<int>(ceil(0.05*nlocal));
}
return host_nlocal;
}
/// Return the number of particles the device will handle this timestep
inline int get_gpu_count(const int ago, const int inum_full);
/// Return the average fraction of particles handled by device on all procs
inline double all_avg_split() {
if (_load_balance) {
double _all_avg_split=0.0;
MPI_Reduce(&_avg_split,&_all_avg_split,1,MPI_DOUBLE,MPI_SUM,0,
_device->replica());
_all_avg_split/=_device->replica_size();
return _all_avg_split/_avg_count;
} else
return _actual_split;
}
/// If CPU neighboring, allow the device fraction to increase on 2nd timestep
inline int ago_first(int ago) const
{ if (_avg_count==1 && _actual_split<_desired_split) ago=0; return ago; }
/// Start the timer for asynchronous device execution
inline void start_timer() {
if (_measure_this_step) {
_device->gpu->sync();
_device->gpu_barrier();
_device->start_host_timer();
_device_time.start();
_device->gpu->sync();
_device->gpu_barrier();
}
}
/// Stop the timer for asynchronous device execution
inline void stop_timer() { if (_measure_this_step) { _device_time.stop(); } }
/// Calculate the new host/device split based on the cpu and device times
/** \note Only does calculation every _HD_BALANCE_EVERY timesteps
(and first 10) **/
inline void balance(const double cpu_time);
/// Calls balance() and then get_gpu_count()
inline int balance(const int ago,const int inum_full,const double cpu_time) {
balance(cpu_time);
return get_gpu_count(ago,inum_full);
}
private:
Device<numtyp,acctyp> *_device;
UCL_Timer _device_time;
bool _init_done, _gpu_nbor;
bool _load_balance;
double _actual_split, _avg_split, _desired_split, _max_split;
int _avg_count;
bool _measure_this_step;
int _inum, _inum_full, _timestep;
};
#define BalanceT Balance<numtyp,acctyp>
template <class numtyp, class acctyp>
void BalanceT::init(Device<numtyp, acctyp> *gpu,
const bool gpu_nbor, const double split) {
clear();
_gpu_nbor=gpu_nbor;
_init_done=true;
_device=gpu;
_device_time.init(*gpu->gpu);
if (split<0.0) {
_load_balance=true;
_desired_split=0.90;
} else {
_load_balance=false;
_desired_split=split;
}
_actual_split=_desired_split;
_avg_split=0.0;
_avg_count=0;
_timestep=0;
}
template <class numtyp, class acctyp>
int BalanceT::get_gpu_count(const int ago, const int inum_full) {
_measure_this_step=false;
if (_load_balance) {
if (_avg_count<11 || _timestep%_HD_BALANCE_EVERY==0) {
_measure_this_step=true;
_inum_full=inum_full;
}
if (ago==0) {
_actual_split=_desired_split;
_max_split=_desired_split;
}
}
_inum=static_cast<int>(floor(_actual_split*inum_full));
if (_inum==0) _inum++;
_timestep++;
return _inum;
}
template <class numtyp, class acctyp>
void BalanceT::balance(const double cpu_time) {
if (_measure_this_step) {
_measure_this_step=false;
double gpu_time=_device_time.seconds();
double max_gpu_time;
MPI_Allreduce(&gpu_time,&max_gpu_time,1,MPI_DOUBLE,MPI_MAX,
_device->gpu_comm());
if (_inum_full==_inum) {
_desired_split=1.0;
return;
}
double cpu_time_per_atom=cpu_time/(_inum_full-_inum);
double cpu_other_time=_device->host_time()-cpu_time;
int host_inum=static_cast<int>((max_gpu_time-cpu_other_time)/
cpu_time_per_atom);
double split=static_cast<double>(_inum_full-host_inum)/_inum_full;
_desired_split=split*_HD_BALANCE_GAP;
if (_desired_split>1.0)
_desired_split=1.0;
if (_desired_split<0.0)
_desired_split=0.0;
if (!_gpu_nbor) {
if (_desired_split<_max_split)
_actual_split=_desired_split;
else
_actual_split=_max_split;
}
//std::cout << gpu_time << " " << max_gpu_time << " " << cpu_other_time << " " << cpu_time_per_atom << " " << cpu_time << " " << _desired_split << " " << host_inum << std::endl;
}
_avg_split+=_desired_split;
_avg_count++;
}
#endif
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