origin='lower',
vmin=0,
vmax=0.01)
colorbar()
# measure execution time
from datetime import datetime
t1 = datetime.now()
# main loop
for tn in xrange(1, 100 + 1):
for sn in xrange(4):
dielectric.update_h(nx, ny, nz, cl[sn], ex, ey, ez, hx, hy, hz,
chx, chy, chz)
dielectric.update_e(nx, ny, nz, dl[sn], ex, ey, ez, hx, hy, hz,
cex, cey, cez)
ex[:, ny / 2, nz / 2] += np.sin(0.1 * (tn + sn / 5.))
dielectric.update_h(nx, ny, nz, cl[sn], ex, ey, ez, hx, hy, hz, chx,
chy, chz)
'''
if tn%10 == 0:
#if tn == 100:
print 'tn =', tn
imsh.set_array( ex[nx/2,:,:]**2 )
draw()
#savefig('./png-wave/%.5d.png' % tn)
'''
print datetime.now() - t1
colorbar()
"""
if myrank == 1:
# measure kernel execution time
# from datetime import datetime
# t1 = datetime.now()
start = cuda.Event()
stop = cuda.Event()
start.record()
# main loop
for tn in xrange(1, 11):
if myrank == 0:
dielectric.update_e(
8, nx, ny, nz, ex_cpu, ey_cpu, ez_cpu, hx_cpu, hy_cpu, hz_cpu, cex_cpu, cey_cpu, cez_cpu
)
ex_cpu[:, :, nz - 1] = mpi.world.recv(myrank + 1, 0)
ey_cpu[:, :, nz - 1] = mpi.world.recv(myrank + 1, 1)
dielectric.update_h(
8, nx, ny, nz, ex_cpu, ey_cpu, ez_cpu, hx_cpu, hy_cpu, hz_cpu, chx_cpu, chy_cpu, chz_cpu
)
mpi.world.send(myrank + 1, 0, hx_cpu[:, :, nz - 1])
mpi.world.send(myrank + 1, 1, hy_cpu[:, :, nz - 1])
else:
for i, Dg in enumerate(Dg_list):
update_e.prepared_call(Dg, nnx, nny, nnz, idx0_list[i], ex_gpu, ey_gpu, ez_gpu, hx_gpu, hy_gpu, hz_gpu)
colorbar() # measure execution time from datetime import datetime t1 = datetime.now() # main loop for tn in xrange(1, 100+1): for sn in xrange(4): dielectric.update_h( nx, ny, nz, cl[sn], ex, ey, ez, hx, hy, hz, chx, chy, chz) dielectric.update_e( nx, ny, nz, dl[sn], ex, ey, ez, hx, hy, hz, cex, cey, cez) ex[:,ny/2,nz/2] += np.sin(0.1*(tn+sn/5.)) dielectric.update_h( nx, ny, nz, cl[sn], ex, ey, ez, hx, hy, hz, chx, chy, chz) ''' if tn%10 == 0: #if tn == 100: print 'tn =', tn imsh.set_array( ex[nx/2,:,:]**2 ) draw()
chz_cpu = set_c(f, (0, 0, None)).copy()
'''
# prepare for plot
from matplotlib.pyplot import *
ion()
imsh = imshow(np.ones((ny,nz),'f'), cmap=cm.hot, origin='lower', vmin=0, vmax=0.001)
colorbar()
'''
# measure kernel execution time
from datetime import datetime
t1 = datetime.now()
# main loop
for tn in xrange(1, 1001):
dielectric.update_e(8, nx, ny, nz, ex_cpu, ey_cpu, ez_cpu, hx_cpu,
hy_cpu, hz_cpu, cex_cpu, cey_cpu, cez_cpu)
ex_cpu[:, ny / 2, nz / 2] += np.sin(0.1 * tn)
dielectric.update_h(8, nx, ny, nz, ex_cpu, ey_cpu, ez_cpu, hx_cpu,
hy_cpu, hz_cpu, chx_cpu, chy_cpu, chz_cpu)
'''
if tn%10 == 0:
#if tn == 100:
print 'tn =', tn
imsh.set_array( ex_cpu[nx/2,:,:]**2 )
draw()
#savefig('./png-wave/%.5d.png' % tstep)
'''
print datetime.now() - t1
from matplotlib.pyplot import *
ion()
imsh = imshow(np.ones((nx,ny),'f').T, cmap=cm.hot, origin='lower', vmin=0, vmax=0.005)
colorbar()
'''
# measure kernel execution time
from datetime import datetime
flop = (nx*ny*nz*30)*tgap
flops = np.zeros(tmax/tgap+1)
t1 = datetime.now()
# main loop
for tn in xrange(1, tmax+1):
dielectric.update_h(*eh_fields)
dielectric.update_e(*(eh_fields+ce_fields))
ez[nx/2,ny/2,:] += np.sin(0.1*tn)
if tn%tgap == 0:
dt = datetime.now()-t1
flops[tn/tgap] = flop/(dt.seconds + dt.microseconds*1e-6)*1e-9
print "[%s] %d/%d (%d %%) %1.3f GFLOPS\r" % (dt, tn, tmax, float(tn)/tmax*100, flops[tn/tgap]),
sys.stdout.flush()
#imsh.set_array( ez[:,:,nz/2].T**2 )
#draw()
#savefig('./png-wave/%.5d.png' % tn)
t1 = datetime.now()
print "\navg: %1.2f GFLOPS" % flops[2:-2].mean()
ion()
imsh = imshow(np.ones((nx,ny),'f').T, cmap=cm.hot, origin='lower', vmin=0, vmax=0.005)
colorbar()
'''
# measure kernel execution time
from datetime import datetime
flop = (nx * ny * nz * 30) * tgap
flops = np.zeros(tmax / tgap + 1)
t1 = datetime.now()
# main loop
for tn in xrange(1, tmax + 1):
dielectric.update_h(*eh_fields)
dielectric.update_e(*(eh_fields + ce_fields))
ez[nx / 2, ny / 2, :] += np.sin(0.1 * tn)
if tn % tgap == 0:
dt = datetime.now() - t1
flops[tn / tgap] = flop / (dt.seconds + dt.microseconds * 1e-6) * 1e-9
print "[%s] %d/%d (%d %%) %1.3f GFLOPS\r" % (
dt, tn, tmax, float(tn) / tmax * 100, flops[tn / tgap]),
sys.stdout.flush()
#imsh.set_array( ez[:,:,nz/2].T**2 )
#draw()
#savefig('./png-wave/%.5d.png' % tn)
t1 = datetime.now()
print "\navg: %1.2f GFLOPS" % flops[2:-2].mean()