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physicsSolverSDampGPUs.py
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physicsSolverSDampGPUs.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Copyright, 2018
Xue Li
Solver class provides the solver of the CVFES project.
One Solver instance corresponds to one mesh and one method
which can be decided by solver configuration.
du: velocity
p: pressure
ddu: acceleration
u: displacement
"""
from mpi4py import MPI
import pyopencl as cl
from cvconfig import CVConfig
from mesh import *
from shape import *
from physicsSolver import PhysicsSolver
from timeit import default_timer as timer
import math
__author__ = "Xue Li"
__copyright__ = "Copyright 2018, the CVFES project"
TAG_COMM_DOF = 211
TAG_COMM_DOF_VALUE = 212
# TAG_ELM_ID = 221
TAG_LHS = 224
TAG_STRESSES = 222
TAG_DISPLACEMENT = 223
TAG_NODE_ID = 224
# TAG_UNION = 224
TAG_CHECKING_STIFFNESS = 311
DOUBLE_NBYTES = 8
class GPUSolidSolver(PhysicsSolver):
def __init__(self, comm, mesh, config):
PhysicsSolver.__init__(self, comm, mesh, config)
self.InitializeSync()
if self.InitializeGPU() < 0:
exit(-1)
# Remember the fluid's time step,
# used for read in stress from fluid solution
# for segregated solvers.
self.dt_f = config.dt_f
self.stressFilename = config.exportBdyStressFilename # Global force
self.useConstantStress = config.useConstantStress
self.constant_T = config.constant_T
self.constantPressure = config.constant_pressure # Local pressure
if self.stressFilename is not None:
if self.useConstantStress:
self.etrac = np.load('{}.npy'.format(self.stressFilename))
else:
self.nt = 0
self.strac = np.load('{}{}.npy'.format(self.stressFilename, self.nt))
self.etrac = np.load('{}{}.npy'.format(self.stressFilename, self.nt+1))
else:
self.etrac = np.zeros((mesh.nNodes, 3))
# self.etrac = np.zeros((mesh.ndof, 1))
# self.appTrac = np.zeros((mesh.nNodes, 3))
# # self.appTrac = np.zeros((mesh.ndof, 1))
self.pressure = 0.0
# Initialize the number of samples.
self.nSmp = config.nSmp
self.ndof = mesh.ndof
self.nNodes = mesh.nNodes
self.nElms = mesh.nElements
self.lclNNodes = mesh.lclNNodes
self.lclNodeIds = mesh.lclNodeIds
self.lclElmNodeIds = mesh.lclElmNodeIds
self.lclNDof = self.lclNNodes * mesh.dof # 3
self.lclNCommNodes = mesh.lclNCommNodes
self.lclNCommDof = self.lclNCommNodes * mesh.dof
self.lclNSpecialHeadDof = mesh.lclNSpecialHead * mesh.dof
self.lclBoundary = mesh.lclBoundary
# Damp coef.
self.damp = mesh.damp
# Prepare the mesh info for union.
self.dofs = np.array([[3*node, 3*node+1, 3*node+2] for node in self.lclNodeIds]).astype(int).ravel()
# Initialize the context.
self.du = mesh.iniDu[self.dofs] # velocity
self.u = mesh.iniU[self.dofs] # displacement
# Calculate u_{-1} to start of the time looping.
# u_-1 = u_0 - dt*du_0 + 0.5*dt**2*ddu_0
self.InitializeSolver()
def InitializeGPU(self):
platforms = cl.get_platforms()
devices = platforms[0].get_devices(cl.device_type.GPU)
ndevices = len(devices)
if ndevices < self.size:
print('GPUs is not enough! Actural size: {}, need: {}'.format(ndevices, self.size))
return -1
self.device = devices[self.rank]
self.context = cl.Context([self.device])
# self.queues = [cl.CommandQueue(self.context) for i in range(2)]
self.queue = cl.CommandQueue(self.context)
self.localWorkSize = 64
self.num_compute_units = self.device.max_compute_units # assumes all the devices have same number of computes unit.
self.globalWorkSize = 8 * self.num_compute_units * self.localWorkSize
print('gpu {} num of computing unites {}'.format(self.rank, self.num_compute_units))
# Read and build the kernel.
kernelsource = open("physicsSolverSDampGPUs.cl").read()
self.program = cl.Program(self.context, kernelsource).build()
return 0
def InitializeSync(self):
self.bdyDofs = np.array([[3*node, 3*node+1, 3*node+2] for node in self.mesh.lclBoundary]).astype(int).ravel()
if self.size > 1:
self.totalCommDofs = np.array([[i*3, i*3+1, i*3+2] for i in self.mesh.totalCommNodeIds]).astype(int).ravel()
self.commDofs = np.array([[i*3, i*3+1, i*3+2] for i in self.mesh.commNodeIds]).astype(int).ravel()
def InitializeSolver(self):
""" Calculate u_{-1} to start of the time looping.
u_-1 = u_0 - dt*du_0 + 0.5*dt**2*ddu_0
"""
# Allocate the np.array object in CPU.
self.LM = np.zeros((self.lclNDof, self.nSmp)) # no synchronized
self.LHS = np.zeros((self.lclNDof, self.nSmp)) # synchronized
# Allocate the OpenCL source and result buffer memory objects on GPU device GMEM.
mem_flags = cl.mem_flags
self.nodes_buf = cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.mesh.nodes[self.lclNodeIds])
# self.elmNodeIds_buf = cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.mesh.elementNodeIds)
# mesh coloring's color tags
self.colorGps_buf = [cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.mesh.lclElmNodeIds[self.mesh.colorGroups[i]]) for i in range(len(self.mesh.colorGroups))]
# self.colorGps_elmIds_buf = [cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.mesh.colorGroups[i]) for i in range(len(self.mesh.colorGroups))]
# for calculating M (mass) matrix, do not need to always exist in GPU memory
thickness_buf = cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.mesh.vthickness[self.lclNodeIds])
# for calculating K (stiffness) matrix, thicknessE (nElms, nSmp)
# -- Young's Modulus
elmVerE = self.mesh.vE[self.mesh.elementNodeIds,:]
elmVerE = elmVerE.swapaxes(1,2)
elmAveE = np.mean(elmVerE, axis=2)
# -- thickness
elmVerThick = self.mesh.vthickness[self.mesh.elementNodeIds,:]
elmVerThick = elmVerThick.swapaxes(1,2)
# elmAveThick = np.mean(elmVerThick, axis=2)
# - thickness x E
elmTE = np.mean(elmVerE*elmVerThick, axis=2)
self.elmTE_buf = [cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = elmTE[self.mesh.colorGroups[i]]) for i in range(len(self.mesh.colorGroups))]
# self.elmE_buf = [cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = elmAveE[self.mesh.colorGroups[i]]) for i in range(len(self.mesh.colorGroups))]
# for calculating K (stiffness) matrix, D needs
k = 5.0/6.0
v = self.mesh.v
pVals = np.array([self.mesh.density, v, 0.5*(1.0-v), 0.5*k*(1.0-v), (1.0-v*v)])
self.pVals_buf = cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = pVals)
# The initial displacement b.c. (nNodes*3,)
u_buf = cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.u)
self.LM_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
self.Ku_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
self.P_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
# cl.enqueue_fill_buffer(self.queue, self.LM_buf, np.float64(0.0), 0, self.LM.nbytes)
# cl.enqueue_fill_buffer(self.queue, self.Ku_buf, np.float64(0.0), 0, self.LM.nbytes)
# cl.enqueue_fill_buffer(self.queue, self.P_buf, np.float64(0.0), 0, self.LM.nbytes)
map_flags = cl.map_flags
self.appTrac_buf = cl.Buffer(self.context, mem_flags.READ_ONLY, int(self.lclNNodes*24))
self.pinned_appTrac = cl.Buffer(self.context, mem_flags.READ_WRITE | mem_flags.ALLOC_HOST_PTR, int(self.lclNNodes*24))
self.appTrac, _eventAppTrac = cl.enqueue_map_buffer(self.queue, self.pinned_appTrac, map_flags.WRITE, 0,
(self.lclNNodes, 3), self.LM.dtype)
self.appTrac[:,:] = 0.0
# prep_appTrac_event = cl.enqueue_copy(self.queue, self.appTrac_buf, self.appTrac)
# 'Assemble' the inital M (mass) and Ku (stiffness) 'matrices'.
# Kernel.
initial_assemble_events = []
for iColorGroup in range(len(self.colorGps_buf)):
initial_assemble_event = \
self.program.assemble_K_M_P(self.queue, (len(self.mesh.colorGroups[iColorGroup]),), (1,),
np.int64(self.nSmp), np.float64(self.pressure),
self.pVals_buf, self.nodes_buf, self.colorGps_buf[iColorGroup], thickness_buf,
self.elmTE_buf[iColorGroup], u_buf, self.Ku_buf, self.LM_buf, self.P_buf,
wait_for=initial_assemble_events)
initial_assemble_events = [initial_assemble_event]
initial_assemble_copy_event = \
cl.enqueue_copy(self.queue, self.LM, self.LM_buf, wait_for=initial_assemble_events)
initial_assemble_copy_event.wait()
# Synchronize the left-hand-side of each equition which is LM.
# Copy the LM first to LHS.
self.LHS[:,:] = self.LM
# Synchronize.
self.SyncCommNodes(self.LHS)
# Copy into GPU device and prepared.
self.LHS_buf = cl.Buffer(self.context, mem_flags.READ_ONLY | mem_flags.COPY_HOST_PTR, hostbuf = self.LHS)
# Calculate accelaration u''.
# ddu = (F0 - C*du - Ku)/M
self.ddu = np.zeros((self.lclNDof, self.nSmp))
self.ddu_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
initial_calc_ddu_event = \
self.program.calc_ddu(self.queue, (self.globalWorkSize,), (self.localWorkSize,),
np.int64(self.nSmp), np.int64(self.lclNDof),
self.P_buf, self.Ku_buf, self.LHS_buf, self.ddu_buf)
initial_ddu_copy_event = \
cl.enqueue_copy(self.queue, self.ddu, self.ddu_buf, wait_for=[initial_calc_ddu_event])
initial_ddu_copy_event.wait()
# Synchronize the acceleration on common nodes.
self.SyncCommNodes(self.ddu)
# Add on the global force.
self.ddu += self.appTrac.reshape(self.lclNDof, 1) / self.LHS
# Prepare the memories.
# Memory on GPU devices.
map_flags = cl.map_flags
self.ures_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
self.u_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
self.up_buf = cl.Buffer(self.context, mem_flags.READ_WRITE, self.LM.nbytes)
self.stress_buf = cl.Buffer(self.context, mem_flags.WRITE_ONLY, int(self.nElms*self.nSmp*40))
# Pinned memory on CPU.
self.pinned_ures = cl.Buffer(self.context, mem_flags.READ_WRITE | mem_flags.ALLOC_HOST_PTR, self.LM.nbytes)
self.pinned_u = cl.Buffer(self.context, mem_flags.READ_WRITE | mem_flags.ALLOC_HOST_PTR, self.LM.nbytes)
self.pinned_up = cl.Buffer(self.context, mem_flags.READ_WRITE | mem_flags.ALLOC_HOST_PTR, self.LM.nbytes)
self.pinned_stress = cl.Buffer(self.context, mem_flags.READ_WRITE | mem_flags.ALLOC_HOST_PTR, int(self.nElms*self.nSmp*40))
# Map to CPU.
self.srcURes, _eventSrcURes = cl.enqueue_map_buffer(self.queue, self.pinned_ures, map_flags.WRITE | map_flags.READ, 0,
self.LM.shape, self.LM.dtype)
self.srcU, _eventSrcU = cl.enqueue_map_buffer(self.queue, self.pinned_u, map_flags.WRITE | map_flags.READ, 0,
self.LM.shape, self.LM.dtype)
self.srcUP, _eventSrcUP = cl.enqueue_map_buffer(self.queue, self.pinned_up, map_flags.WRITE | map_flags.READ, 0,
self.LM.shape, self.LM.dtype)
self.stress, _eventStress = cl.enqueue_map_buffer(self.queue, self.pinned_stress, map_flags.READ, 0,
(self.nElms, self.nSmp, 5), self.LM.dtype)
# Use Taylor Expansion to get u_-1.
self.srcU[:,:] = self.u[np.newaxis].transpose()
self.srcUP[:,:] = self.srcU - self.dt * self.du[np.newaxis].transpose() + self.dt**2 * self.ddu / 2.0
# copy up first to device
prep_up_event = cl.enqueue_copy(self.queue, self.up_buf, self.srcUP)
prep_u_event = cl.enqueue_copy(self.queue, self.u_buf, self.srcU)
def RefreshContext(self, physicSolver):
t = physicSolver.t
dt_f = self.dt_f
ramp_T = self.constant_T
# Set up the global force.
if self.stressFilename is not None:
if self.useConstantStress:
if t > ramp_T:
appTrac = self.etrac
else:
a = b = self.etrac/2.0
n = math.pi/self.constant_T
appTrac = a - b*math.cos(n*t)
else:
if t > ramp_T:
if int((t-ramp_T)/dt_f) > self.nt:
self.nt += 1
self.strac = self.etrac
self.etrac = np.load('{}{}.npy'.format(self.stressFilename, self.nt+1))
print('At t={} read in wallpressure_{}'.format(t, self.nt+1))
appTrac = self.strac + (t-ramp_T - self.nt*dt_f)*(self.etrac - self.strac)/dt_f
else:
a = b = self.strac/2.0
n = math.pi/ramp_T
appTrac = a - b*math.cos(n*t)
# Only use values of the dofs contained in the partition.
self.appTrac[:,:] = appTrac[self.lclNodeIds,:]
self.appTrac[self.lclBoundary,:] = 0.0
# Set up the pressure.
if t > self.constant_T:
self.pressure = self.constantPressure
else:
a = b = self.constantPressure/2.0
n = math.pi/self.constant_T
self.pressure = a - b*math.cos(n*t)
def Solve(self, t, dt):
# start = timer()
cl.enqueue_fill_buffer(self.queue, self.Ku_buf, np.float64(0.0), 0, self.LM.nbytes)
cl.enqueue_fill_buffer(self.queue, self.P_buf, np.float64(0.0), 0, self.LM.nbytes)
# end = timer()
# print('--- Rank: {} time 0: {:10.1f} ms'.format(self.rank, (end - start) * 1000.0))
# start = timer()
calc_Ku_events = []
for iColorGrp in range(len(self.colorGps_buf)):
calc_Ku_event = \
self.program.assemble_K_P(self.queue, (self.globalWorkSize,), (self.localWorkSize,),
np.int64(len(self.mesh.colorGroups[iColorGrp])),
np.int64(self.nSmp), np.float64(self.pressure),
self.pVals_buf, self.nodes_buf, self.colorGps_buf[iColorGrp],
self.elmTE_buf[iColorGrp], self.u_buf, self.Ku_buf, self.P_buf,
wait_for=calc_Ku_events)
calc_Ku_events = [calc_Ku_event]
# end = timer()
# print('--- Rank: {} time 1: {:10.1f} ms'.format(self.rank, (end - start) * 1000.0))
# start = timer()
calc_u_event = \
self.program.calc_u(self.queue, (self.globalWorkSize,), (self.localWorkSize,),
np.int64(self.nSmp), np.int64(self.lclNDof),
np.float64(dt), np.float64(self.damp),
self.P_buf, self.Ku_buf, self.LM_buf, self.LHS_buf,
self.u_buf, self.up_buf, self.ures_buf, wait_for=[calc_Ku_event])
# calc_u_event.wait() # TODO:: Comment off after debugging
# end = timer()
# print('--- Rank: {} time 2: {:10.1f} ms'.format(self.rank, (end - start) * 1000.0))
# start = timer()
ures_copy_event = cl.enqueue_copy(self.queue, self.srcURes[:self.lclNCommDof], self.ures_buf,
wait_for=[calc_u_event])
# ures_copy_event.wait()
# end = timer()
# print('--- Rank: {} time 3: {:10.1f} ms'.format(self.rank, (end - start) * 1000.0))
# start = timer()
# Synchronize the ures.
self.SyncCommNodes(self.srcURes)
# end = timer()
# print('--- Rank: {} time 4: {:10.1f} ms'.format(self.rank, (end - start) * 1000.0))
# start = timer()
# Apply boundary condition.
self.ApplyBoundaryCondition(self.srcURes)
# Enforce the applied boundary condition back to GPU. <lclNSpecialHeadDof>
update_u_event = cl.enqueue_copy(self.queue, self.ures_buf, self.srcURes[:self.lclNSpecialHeadDof])
# Add on the global force.
appTrac_copy_event = cl.enqueue_copy(self.queue, self.appTrac_buf, self.appTrac)
calc_u_event = \
self.program.calc_u_appTrac(self.queue, (self.globalWorkSize,), (self.localWorkSize,),
np.int64(self.nSmp), np.int64(self.lclNDof), np.float64(dt),
self.LHS_buf, self.appTrac_buf, self.ures_buf,
wait_for=[update_u_event, appTrac_copy_event])
calc_u_event.wait()
# end = timer()
# print('--- Rank: {} time 5: {:10.1f} ms'.format(self.rank, (end - start) * 1000.0))
# start = timer()
# Update/Shift the pointers.
self.srcURes, self.srcU, self.srcUP = self.srcUP, self.srcURes, self.srcU
self.ures_buf, self.u_buf, self.up_buf = self.up_buf, self.ures_buf, self.u_buf
def ApplyBoundaryCondition(self, quant): # TODO:: Change to according to configuration.
quant[self.bdyDofs,:] = self.mesh.bdyU
def SyncCommNodes(self, quant):
""" Synchronize the quantity fo common nodes.
"""
if self.size == 1:
return
totalCommDofs = self.totalCommDofs
commDofs = self.commDofs
commQuant = quant[:len(commDofs)]
totalQuant = np.zeros((len(totalCommDofs), self.nSmp))
if self.rank == 0:
# Add on self's (root processor's) quantity.
indices = np.where(np.isin(totalCommDofs, commDofs))[0]
totalQuant[indices] += commQuant
quantIdBuf = np.zeros(len(totalCommDofs), dtype=np.int64)
quantBuf = np.zeros(len(totalCommDofs)*self.nSmp)
recvInfo = MPI.Status()
for i in range(1, self.size):
self.comm.Recv(quantIdBuf, MPI.ANY_SOURCE, TAG_COMM_DOF, recvInfo)
recvLen = recvInfo.Get_count(MPI.INT64_T)
recvSource = recvInfo.Get_source()
# Receive the quantity.
self.comm.Recv(quantBuf, recvSource, TAG_COMM_DOF_VALUE, recvInfo)
# TODO:: make sure the quant received length is consistent with quantIds'.
# Add the quantity received to the totalQuant.
indices = np.where(np.isin(totalCommDofs, quantIdBuf[:recvLen]))[0]
totalQuant[indices] += quantBuf[:recvLen*self.nSmp].reshape(recvLen, self.nSmp)
else:
self.comm.Send(commDofs, 0, TAG_COMM_DOF)
self.comm.Send(commQuant.ravel(), 0, TAG_COMM_DOF_VALUE)
# Get the collected total quantities by broadcast.
self.comm.Bcast(totalQuant, root=0)
# Update the original quantity.
indices = np.where(np.isin(totalCommDofs, commDofs))[0]
quant[:len(commDofs)] = totalQuant[indices]
def Save(self, filename, counter):
# Copy out the displacement from GPU to CPU.
copy_u_event = cl.enqueue_copy(self.queue, self.srcU, self.u_buf)
# Prepare/Union the displacement.
resU = self.UnionDisplacement(self.srcU)
if self.rank == 0:
self.mesh.SaveDisplacement(filename, counter,
resU.transpose().reshape(self.nSmp, self.mesh.nNodes, self.Dof))
# Barrier everyone!
self.comm.Barrier()
def UnionDisplacement(self, quant):
if self.size == 1:
resU = np.empty((self.ndof, self.nSmp))
resU[self.dofs,:] = quant
return resU
if self.rank == 0:
resU = np.empty((self.ndof, self.nSmp))
resU[self.dofs,:] = quant
nodesInfo = MPI.Status()
dofBuf = np.empty(self.ndof, dtype=np.int64)
uBuf = np.zeros((self.ndof, self.nSmp))
for i in range(1, self.size):
self.comm.Recv(dofBuf, i, TAG_NODE_ID, nodesInfo) # MPI.ANY_SOURCE
nodesSource = nodesInfo.Get_source()
dofs = dofBuf[:nodesInfo.Get_count(MPI.INT64_T)]
self.comm.Recv(uBuf, nodesSource, TAG_DISPLACEMENT, nodesInfo)
# Flag the nodes uBuf acctually contains.
resU[dofs,:] = uBuf[:len(dofs)]
else:
self.comm.Send(self.dofs, 0, TAG_NODE_ID)
self.comm.Send(quant, 0, TAG_DISPLACEMENT)
resU = None
return resU