def __setitem__(self, key, value):
# unpack cache
cache, key = unpack_cache_key(key)
cache_key = None if cache is None else "set"
# short code path to zero lattice
if type(key) == slice and key == slice(None, None, None):
if gpt.util.is_num(value):
for o in self.v_obj:
cgpt.lattice_set_to_number(o, value)
return
cache_key = (
f"{self.otype.__name__}_{self.checkerboard().__name__}_{self.grid.describe()}"
)
cache = lattice.cache
# general code path, map key
pos, tidx, shape = gpt.map_key(self, key)
n_pos = len(pos)
# convert input to proper numpy array
value = gpt.util.tensor_to_value(
value, dtype=self.grid.precision.complex_dtype)
if value is None:
value = memoryview(bytearray())
# needed bytes and optional cyclic upscaling
nbytes_needed = n_pos * numpy.prod(
shape) * self.grid.precision.nbytes * 2
value = cgpt.copy_cyclic_upscale(value, nbytes_needed)
# create plan
if cache_key is None or cache_key not in cache:
plan = gpt.copy_plan(self, value)
plan.destination += gpt.lattice_view(self, pos, tidx)
plan.source += gpt.global_memory_view(
self.grid,
[[self.grid.processor, value, 0, value.nbytes]]
if value.nbytes > 0 else None,
)
# skip optimization if we only use it once
xp = plan(
local_only=isinstance(pos, gpt.core.local_coordinates),
skip_optimize=cache_key is None,
)
if cache_key is not None:
cache[cache_key] = xp
else:
xp = cache[cache_key]
xp(self, value)
def mk_qlat_gpt_copy_plan(ctype, total_site, multiplicity, tag):
geo = q.Geometry(total_site, multiplicity)
f_gpt = mk_gpt_field(ctype, geo)
f_qlat = q.Field(ctype, geo)
lexicographic_coordinates = g.coordinates(f_gpt)
buf = f_qlat.mview()
if tag == "qlat_from_gpt":
qlat_from_gpt = g.copy_plan(buf, f_gpt)
qlat_from_gpt.destination += g.global_memory_view(
f_gpt.grid, [[f_gpt.grid.processor, buf, 0, buf.nbytes]])
qlat_from_gpt.source += f_gpt.view[lexicographic_coordinates]
qlat_from_gpt = qlat_from_gpt(local_only=True)
return qlat_from_gpt
elif tag == "gpt_from_qlat":
gpt_from_qlat = g.copy_plan(f_gpt, buf)
gpt_from_qlat.source += g.global_memory_view(
f_gpt.grid, [[f_gpt.grid.processor, buf, 0, buf.nbytes]])
gpt_from_qlat.destination += f_gpt.view[lexicographic_coordinates]
gpt_from_qlat = gpt_from_qlat(local_only=True)
return gpt_from_qlat
else:
q.displayln_info(tag)
raise Exception("mk_qlat_gpt_copy_plan")
def __getitem__(self, key):
# unpack cache
cache, key = unpack_cache_key(key)
cache_key = None if cache is None else "get"
# general code path, map key
pos, tidx, shape = gpt.map_key(self, key)
n_pos = len(pos)
# create target
value = cgpt.ndarray((n_pos, *shape),
self.grid.precision.complex_dtype)
# create plan
if cache_key is None or cache_key not in cache:
plan = gpt.copy_plan(value, self)
plan.destination += gpt.global_memory_view(
self.grid,
[[self.grid.processor, value, 0, value.nbytes]]
if value.nbytes > 0 else None,
)
plan.source += gpt.lattice_view(self, pos, tidx)
xp = plan()
if cache_key is not None:
cache[cache_key] = xp
else:
xp = cache[cache_key]
xp(value, self)
# if only a single element is returned and we have the full shape,
# wrap in a tensor
if len(value) == 1 and shape == self.otype.shape:
return gpt.util.value_to_tensor(value[0], self.otype)
return value
def load(filename, params):
# first check if this is right file format
if not os.path.exists(filename + "/00/0000000000.compressed"
) or not os.path.exists(filename + "/metadata.txt"):
raise NotImplementedError()
# verbosity
verbose = gpt.default.is_verbose("io")
# site checkerboard
# only odd is used in this file format but
# would be easy to generalize here
site_cb = gpt.odd
# need grids parameter
assert params["grids"] is not None
assert type(params["grids"]) == gpt.grid
fgrid = params["grids"]
assert fgrid.precision == gpt.single
fdimensions = fgrid.fdimensions
# read metadata
metadata = read_metadata(filename + "/metadata.txt")
s = get_ivec(metadata, "s")
ldimensions = [s[4]] + s[:4]
blocksize = get_ivec(metadata, "b")
blocksize = [blocksize[4]] + blocksize[:4]
nb = get_ivec(metadata, "nb")
nb = [nb[4]] + nb[:4]
crc32 = get_xvec(metadata, "crc32")
neigen = int(metadata["neig"])
nbasis = int(metadata["nkeep"])
nsingle = int(metadata["nkeep_single"])
blocks = int(metadata["blocks"])
FP16_COEF_EXP_SHARE_FLOATS = int(metadata["FP16_COEF_EXP_SHARE_FLOATS"])
nsingleCap = min([nsingle, nbasis])
# check
nd = len(ldimensions)
assert nd == 5
assert nd == len(fdimensions)
assert nd == len(blocksize)
assert fgrid.cb.n == 2
assert fgrid.cb.cb_mask == [0, 1, 1, 1, 1]
# create coarse grid
cgrid = gpt.block.grid(fgrid, blocksize)
# allow for partial loading of data
if params["nmax"] is not None:
nmax = params["nmax"]
nbasis_max = min([nmax, nbasis])
neigen_max = min([nmax, neigen])
nsingleCap_max = min([nmax, nsingleCap])
else:
nbasis_max = nbasis
neigen_max = neigen
nsingleCap_max = nsingleCap
# allocate all lattices
basis = [gpt.vspincolor(fgrid) for i in range(nbasis_max)]
cevec = [gpt.vcomplex(cgrid, nbasis) for i in range(neigen_max)]
if params["advise_basis"] is not None:
gpt.advise(basis, params["advise_basis"])
if params["advise_cevec"] is not None:
gpt.advise(cevec, params["advise_cevec"])
# fix checkerboard of basis
for i in range(nbasis_max):
basis[i].checkerboard(site_cb)
# mpi layout
mpi = []
for i in range(nd):
assert fdimensions[i] % ldimensions[i] == 0
mpi.append(fdimensions[i] // ldimensions[i])
assert mpi[0] == 1 # assert no mpi in 5th direction
# create cartesian view on fine grid
cv0 = gpt.cartesian_view(-1, mpi, fdimensions, fgrid.cb, site_cb)
views = cv0.views_for_node(fgrid)
# timing
totalSizeGB = 0
dt_fp16 = 1e-30
dt_distr = 1e-30
dt_munge = 1e-30
dt_crc = 1e-30
dt_fread = 1e-30
t0 = gpt.time()
# load all views
if verbose:
gpt.message("Loading %s with %d views per node" %
(filename, len(views)))
for i, v in enumerate(views):
cv = gpt.cartesian_view(v if v is not None else -1, mpi, fdimensions,
fgrid.cb, site_cb)
cvc = gpt.cartesian_view(v if v is not None else -1, mpi,
cgrid.fdimensions, gpt.full, gpt.none)
pos_coarse = gpt.coordinates(cvc, "canonical")
dn, fn = get_local_name(filename, cv)
# sizes
slot_lsites = numpy.prod(cv.view_dimensions)
assert slot_lsites % blocks == 0
block_data_size_single = slot_lsites * 12 // 2 // blocks * 2 * 4
block_data_size_fp16 = FP_16_SIZE(slot_lsites * 12 // 2 // blocks * 2,
24)
coarse_block_size_part_fp32 = 2 * (4 * nsingleCap)
coarse_block_size_part_fp16 = 2 * (FP_16_SIZE(
nbasis - nsingleCap, FP16_COEF_EXP_SHARE_FLOATS))
coarse_vector_size = (coarse_block_size_part_fp32 +
coarse_block_size_part_fp16) * blocks
coarse_fp32_vector_size = 2 * (4 * nbasis) * blocks
# checksum
crc32_comp = 0
# file
f = gpt.FILE(fn, "rb") if fn is not None else None
# block positions
pos = [
cgpt.coordinates_from_block(cv.top, cv.bottom, b, nb,
"canonicalOdd") for b in range(blocks)
]
# group blocks
read_blocks = blocks
block_reduce = 1
max_read_blocks = get_param(params, "max_read_blocks", 8)
while read_blocks > max_read_blocks and read_blocks % 2 == 0:
pos = [
numpy.concatenate((pos[2 * i + 0], pos[2 * i + 1]))
for i in range(read_blocks // 2)
]
block_data_size_single *= 2
block_data_size_fp16 *= 2
read_blocks //= 2
block_reduce *= 2
gpt.message("Read blocks", blocks)
# make read-only to enable caching
for x in pos:
x.setflags(write=0)
# dummy buffer
data0 = memoryview(bytes())
# single-precision data
data_munged = memoryview(bytearray(block_data_size_single *
nsingleCap))
for b in range(read_blocks):
fgrid.barrier()
dt_fread -= gpt.time()
if f is not None:
data = memoryview(f.read(block_data_size_single * nsingleCap))
globalReadGB = len(data) / 1024.0**3.0
else:
globalReadGB = 0.0
globalReadGB = fgrid.globalsum(globalReadGB)
dt_fread += gpt.time()
totalSizeGB += globalReadGB
if f is not None:
dt_crc -= gpt.time()
crc32_comp = gpt.crc32(data, crc32_comp)
dt_crc += gpt.time()
dt_munge -= gpt.time()
# data: lattice0_posA lattice1_posA .... lattice0_posB lattice1_posB
cgpt.munge_inner_outer(data_munged, data, nsingleCap,
block_reduce)
# data_munged: lattice0 lattice1 lattice2 ...
dt_munge += gpt.time()
else:
data_munged = data0
fgrid.barrier()
dt_distr -= gpt.time()
rhs = data_munged[0:block_data_size_single]
distribute_plan = gpt.copy_plan(basis[0], rhs)
distribute_plan.destination += basis[0].view[pos[b]]
distribute_plan.source += gpt.global_memory_view(
fgrid, [[fgrid.processor, rhs, 0, rhs.nbytes]])
rhs = None
distribute_plan = distribute_plan()
for i in range(nsingleCap_max):
distribute_plan(
basis[i],
data_munged[block_data_size_single *
i:block_data_size_single * (i + 1)],
)
dt_distr += gpt.time()
if verbose:
gpt.message(
"* read %g GB: fread at %g GB/s, crc32 at %g GB/s, munge at %g GB/s, distribute at %g GB/s; available = %g GB"
% (
totalSizeGB,
totalSizeGB / dt_fread,
totalSizeGB / dt_crc,
totalSizeGB / dt_munge,
totalSizeGB / dt_distr,
mem_avail(),
))
# fp16 data
if nbasis != nsingleCap:
# allocate data buffer
data_fp32 = memoryview(
bytearray(block_data_size_single * (nbasis - nsingleCap)))
data_munged = memoryview(
bytearray(block_data_size_single * (nbasis - nsingleCap)))
for b in range(read_blocks):
fgrid.barrier()
dt_fread -= gpt.time()
if f is not None:
data = memoryview(
f.read(block_data_size_fp16 * (nbasis - nsingleCap)))
globalReadGB = len(data) / 1024.0**3.0
else:
globalReadGB = 0.0
globalReadGB = fgrid.globalsum(globalReadGB)
dt_fread += gpt.time()
totalSizeGB += globalReadGB
if f is not None:
dt_crc -= gpt.time()
crc32_comp = gpt.crc32(data, crc32_comp)
dt_crc += gpt.time()
dt_fp16 -= gpt.time()
cgpt.fp16_to_fp32(data_fp32, data, 24)
dt_fp16 += gpt.time()
dt_munge -= gpt.time()
cgpt.munge_inner_outer(
data_munged,
data_fp32,
nbasis - nsingleCap,
block_reduce,
)
dt_munge += gpt.time()
else:
data_munged = data0
fgrid.barrier()
dt_distr -= gpt.time()
if nsingleCap < nbasis_max:
rhs = data_munged[0:block_data_size_single]
distribute_plan = gpt.copy_plan(basis[0], rhs)
distribute_plan.destination += basis[0].view[pos[b]]
distribute_plan.source += gpt.global_memory_view(
fgrid, [[fgrid.processor, rhs, 0, rhs.nbytes]])
rhs = None
distribute_plan = distribute_plan()
for i in range(nsingleCap, nbasis_max):
j = i - nsingleCap
distribute_plan(
basis[i],
data_munged[block_data_size_single *
j:block_data_size_single * (j + 1)],
)
dt_distr += gpt.time()
if verbose:
gpt.message(
"* read %g GB: fread at %g GB/s, crc32 at %g GB/s, munge at %g GB/s, distribute at %g GB/s, fp16 at %g GB/s; available = %g GB"
% (
totalSizeGB,
totalSizeGB / dt_fread,
totalSizeGB / dt_crc,
totalSizeGB / dt_munge,
totalSizeGB / dt_distr,
totalSizeGB / dt_fp16,
mem_avail(),
))
# coarse grid data
data_fp32 = memoryview(bytearray(coarse_fp32_vector_size))
distribute_plan = None
for j in range(neigen):
fgrid.barrier()
dt_fread -= gpt.time()
if f is not None:
data = memoryview(f.read(coarse_vector_size))
globalReadGB = len(data) / 1024.0**3.0
else:
globalReadGB = 0.0
globalReadGB = fgrid.globalsum(globalReadGB)
dt_fread += gpt.time()
totalSizeGB += globalReadGB
if f is not None:
dt_crc -= gpt.time()
crc32_comp = gpt.crc32(data, crc32_comp)
dt_crc += gpt.time()
dt_fp16 -= gpt.time()
cgpt.mixed_fp32fp16_to_fp32(
data_fp32,
data,
coarse_block_size_part_fp32,
coarse_block_size_part_fp16,
FP16_COEF_EXP_SHARE_FLOATS,
)
dt_fp16 += gpt.time()
data = data_fp32
else:
data = data0
fgrid.barrier()
dt_distr -= gpt.time()
if j < neigen_max:
if distribute_plan is None:
distribute_plan = gpt.copy_plan(cevec[j], data)
distribute_plan.destination += cevec[j].view[pos_coarse]
distribute_plan.source += gpt.global_memory_view(
cgrid, [[cgrid.processor, data, 0, data.nbytes]])
distribute_plan = distribute_plan()
distribute_plan(cevec[j], data)
dt_distr += gpt.time()
if verbose and j % (neigen // 10) == 0:
gpt.message(
"* read %g GB: fread at %g GB/s, crc32 at %g GB/s, munge at %g GB/s, distribute at %g GB/s, fp16 at %g GB/s; available = %g GB"
% (
totalSizeGB,
totalSizeGB / dt_fread,
totalSizeGB / dt_crc,
totalSizeGB / dt_munge,
totalSizeGB / dt_distr,
totalSizeGB / dt_fp16,
mem_avail(),
))
# crc checks
if f is not None:
assert crc32_comp == crc32[cv.rank]
# timing
t1 = gpt.time()
# verbosity
if verbose:
gpt.message("* load %g GB at %g GB/s" % (totalSizeGB, totalSizeGB /
(t1 - t0)))
# eigenvalues
evln = list(
filter(lambda x: x != "",
open(filename + "/eigen-values.txt").read().split("\n")))
nev = int(evln[0])
ev = [float(x) for x in evln[1:]]
assert len(ev) == nev
return (basis, cevec, ev)
def save(filename, objs, params):
# split data to save
assert len(objs) == 3
basis = objs[0]
cevec = objs[1]
ev = objs[2]
# verbosity
verbose = gpt.default.is_verbose("io")
if verbose:
gpt.message(
"Saving %d basis vectors, %d coarse-grid vectors, %d eigenvalues to %s"
% (len(basis), len(cevec), len(ev), filename))
# create directory
if gpt.rank() == 0:
os.makedirs(filename, exist_ok=True)
# now sync since only root has created directory
gpt.barrier()
# write eigenvalues
if gpt.rank() == 0:
f = open("%s/eigen-values.txt" % filename, "wt")
f.write("%d\n" % len(ev))
for v in ev:
f.write("%.15E\n" % v)
f.close()
# site checkerboard
# only odd is used in this file format but
# would be easy to generalize here
site_cb = gpt.odd
# grids
assert len(basis) > 0
assert len(cevec) > 0
fgrid = basis[0].grid
cgrid = cevec[0].grid
# mpi layout
if params["mpi"] is not None:
mpi = params["mpi"]
else:
mpi = fgrid.mpi
assert mpi[0] == 1 # assert no mpi in 5th direction
# params
assert basis[0].checkerboard() == site_cb
nd = 5
assert len(fgrid.ldimensions) == nd
fdimensions = fgrid.fdimensions
ldimensions = [conformDiv(fdimensions[i], mpi[i]) for i in range(nd)]
assert fgrid.precision == gpt.single
s = ldimensions
b = [
conformDiv(fgrid.fdimensions[i], cgrid.fdimensions[i])
for i in range(nd)
]
nb = [conformDiv(s[i], b[i]) for i in range(nd)]
neigen = len(cevec)
nbasis = len(basis)
if "nsingle" in params:
nsingle = params["nsingle"]
assert nsingle <= nbasis
else:
nsingle = nbasis
nsingleCap = min([nsingle, nbasis])
blocks = numpy.prod(nb)
FP16_COEF_EXP_SHARE_FLOATS = 10
# write metadata
if gpt.rank() == 0:
fmeta = open("%s/metadata.txt" % filename, "wt")
for i in range(nd):
fmeta.write("s[%d] = %d\n" % (i, s[(i + 1) % nd]))
for i in range(nd):
fmeta.write("b[%d] = %d\n" % (i, b[(i + 1) % nd]))
for i in range(nd):
fmeta.write("nb[%d] = %d\n" % (i, nb[(i + 1) % nd]))
fmeta.write("neig = %d\n" % neigen)
fmeta.write("nkeep = %d\n" % nbasis)
fmeta.write("nkeep_single = %d\n" % nsingle)
fmeta.write("blocks = %d\n" % blocks)
fmeta.write("FP16_COEF_EXP_SHARE_FLOATS = %d\n" %
FP16_COEF_EXP_SHARE_FLOATS)
fmeta.flush() # write crc32 later
# create cartesian view on fine grid
cv0 = gpt.cartesian_view(-1, mpi, fdimensions, fgrid.cb, site_cb)
views = cv0.views_for_node(fgrid)
crc32 = numpy.array([0] * cv0.ranks, dtype=numpy.uint64)
# timing
t0 = gpt.time()
totalSizeGB = 0
dt_fp16 = 1e-30
dt_distr = 1e-30
dt_munge = 1e-30
dt_crc = 1e-30
dt_fwrite = 1e-30
t0 = gpt.time()
# load all views
if verbose:
gpt.message("Saving %s with %d views per node" %
(filename, len(views)))
for i, v in enumerate(views):
cv = gpt.cartesian_view(v if v is not None else -1, mpi, fdimensions,
fgrid.cb, site_cb)
cvc = gpt.cartesian_view(v if v is not None else -1, mpi,
cgrid.fdimensions, gpt.full, gpt.none)
pos_coarse = gpt.coordinates(cvc, "canonical")
dn, fn = get_local_name(filename, cv)
if fn is not None:
os.makedirs(dn, exist_ok=True)
# sizes
slot_lsites = numpy.prod(cv.view_dimensions)
assert slot_lsites % blocks == 0
block_data_size_single = slot_lsites * 12 // 2 // blocks * 2 * 4
block_data_size_fp16 = FP_16_SIZE(slot_lsites * 12 // 2 // blocks * 2,
24)
coarse_block_size_part_fp32 = 2 * (4 * nsingleCap)
coarse_block_size_part_fp16 = 2 * (FP_16_SIZE(
nbasis - nsingleCap, FP16_COEF_EXP_SHARE_FLOATS))
coarse_vector_size = (coarse_block_size_part_fp32 +
coarse_block_size_part_fp16) * blocks
totalSize = (
blocks *
(block_data_size_single * nsingleCap + block_data_size_fp16 *
(nbasis - nsingleCap)) + neigen * coarse_vector_size)
totalSizeGB += totalSize / 1024.0**3.0 if v is not None else 0.0
# checksum
crc32_comp = 0
# file
f = gpt.FILE(fn, "wb") if fn is not None else None
# block positions
pos = [
cgpt.coordinates_from_block(cv.top, cv.bottom, b, nb,
"canonicalOdd") for b in range(blocks)
]
# group blocks
read_blocks = blocks
block_reduce = 1
max_read_blocks = get_param(params, "max_read_blocks", 8)
while read_blocks > max_read_blocks and read_blocks % 2 == 0:
pos = [
numpy.concatenate((pos[2 * i + 0], pos[2 * i + 1]))
for i in range(read_blocks // 2)
]
block_data_size_single *= 2
block_data_size_fp16 *= 2
read_blocks //= 2
block_reduce *= 2
# make read-only to enable caching
for x in pos:
x.setflags(write=0)
# single-precision data
data = memoryview(bytearray(block_data_size_single * nsingleCap))
data_munged = memoryview(bytearray(block_data_size_single *
nsingleCap))
for b in range(read_blocks):
fgrid.barrier()
dt_distr -= gpt.time()
lhs_size = basis[0].otype.nfloats * 4 * len(pos[b])
lhs = data_munged[0:lhs_size]
distribute_plan = gpt.copy_plan(lhs, basis[0])
distribute_plan.destination += gpt.global_memory_view(
fgrid, [[fgrid.processor, lhs, 0, lhs.nbytes]])
distribute_plan.source += basis[0].view[pos[b]]
distribute_plan = distribute_plan()
lhs = None
for i in range(nsingleCap):
distribute_plan(
data_munged[block_data_size_single *
i:block_data_size_single * (i + 1)],
basis[i],
)
dt_distr += gpt.time()
if f is not None:
dt_munge -= gpt.time()
cgpt.munge_inner_outer(
data,
data_munged,
block_reduce,
nsingleCap,
)
dt_munge += gpt.time()
dt_crc -= gpt.time()
crc32_comp = gpt.crc32(data, crc32_comp)
dt_crc += gpt.time()
fgrid.barrier()
dt_fwrite -= gpt.time()
if f is not None:
f.write(data)
globalWriteGB = len(data) / 1024.0**3.0
else:
globalWriteGB = 0.0
globalWriteGB = fgrid.globalsum(globalWriteGB)
dt_fwrite += gpt.time()
totalSizeGB += globalWriteGB
if verbose:
gpt.message(
"* write %g GB: fwrite at %g GB/s, crc32 at %g GB/s, munge at %g GB/s, distribute at %g GB/s"
% (
totalSizeGB,
totalSizeGB / dt_fwrite,
totalSizeGB / dt_crc,
totalSizeGB / dt_munge,
totalSizeGB / dt_distr,
))
# fp16 data
if nbasis != nsingleCap:
# allocate data buffer
data_fp32 = memoryview(
bytearray(block_data_size_single * (nbasis - nsingleCap)))
data_munged = memoryview(
bytearray(block_data_size_single * (nbasis - nsingleCap)))
data = memoryview(
bytearray(block_data_size_fp16 * (nbasis - nsingleCap)))
for b in range(read_blocks):
fgrid.barrier()
dt_distr -= gpt.time()
lhs_size = basis[0].otype.nfloats * 4 * len(pos[b])
lhs = data_munged[0:lhs_size]
distribute_plan = gpt.copy_plan(lhs, basis[0])
distribute_plan.destination += gpt.global_memory_view(
fgrid, [[fgrid.processor, lhs, 0, lhs.nbytes]])
distribute_plan.source += basis[0].view[pos[b]]
distribute_plan = distribute_plan()
lhs = None
for i in range(nsingleCap, nbasis):
j = i - nsingleCap
distribute_plan(
data_munged[j * block_data_size_single:(j + 1) *
block_data_size_single],
basis[i],
)
dt_distr += gpt.time()
if f is not None:
dt_munge -= gpt.time()
cgpt.munge_inner_outer(
data_fp32,
data_munged,
block_reduce,
nbasis - nsingleCap,
)
dt_munge += gpt.time()
dt_fp16 -= gpt.time()
cgpt.fp32_to_fp16(data, data_fp32, 24)
dt_fp16 += gpt.time()
dt_crc -= gpt.time()
crc32_comp = gpt.crc32(data, crc32_comp)
dt_crc += gpt.time()
fgrid.barrier()
dt_fwrite -= gpt.time()
if f is not None:
f.write(data)
globalWriteGB = len(data) / 1024.0**3.0
else:
globalWriteGB = 0.0
globalWriteGB = fgrid.globalsum(globalWriteGB)
dt_fwrite += gpt.time()
totalSizeGB += globalWriteGB
if verbose:
gpt.message(
"* write %g GB: fwrite at %g GB/s, crc32 at %g GB/s, munge at %g GB/s, distribute at %g GB/s, fp16 at %g GB/s"
% (
totalSizeGB,
totalSizeGB / dt_fwrite,
totalSizeGB / dt_crc,
totalSizeGB / dt_munge,
totalSizeGB / dt_distr,
totalSizeGB / dt_fp16,
))
# coarse grid data
data = memoryview(bytearray(coarse_vector_size))
data_fp32 = memoryview(
bytearray(cevec[0].otype.nfloats * 4 * len(pos_coarse)))
distribute_plan = gpt.copy_plan(data_fp32, cevec[0])
distribute_plan.destination += gpt.global_memory_view(
cgrid, [[cgrid.processor, data_fp32, 0, data_fp32.nbytes]])
distribute_plan.source += cevec[0].view[pos_coarse]
distribute_plan = distribute_plan()
for j in range(neigen):
fgrid.barrier()
dt_distr -= gpt.time()
distribute_plan(data_fp32, cevec[j])
dt_distr += gpt.time()
if f is not None:
dt_fp16 -= gpt.time()
cgpt.fp32_to_mixed_fp32fp16(
data,
data_fp32,
coarse_block_size_part_fp32,
coarse_block_size_part_fp16,
FP16_COEF_EXP_SHARE_FLOATS,
)
dt_fp16 += gpt.time()
dt_crc -= gpt.time()
crc32_comp = gpt.crc32(data, crc32_comp)
dt_crc += gpt.time()
fgrid.barrier()
dt_fwrite -= gpt.time()
if f is not None:
f.write(data)
globalWriteGB = len(data) / 1024.0**3.0
else:
globalWriteGB = 0.0
globalWriteGB = fgrid.globalsum(globalWriteGB)
dt_fwrite += gpt.time()
totalSizeGB += globalWriteGB
if verbose and j % (neigen // 10) == 0:
gpt.message(
"* write %g GB: fwrite at %g GB/s, crc32 at %g GB/s, munge at %g GB/s, distribute at %g GB/s, fp16 at %g GB/s"
% (
totalSizeGB,
totalSizeGB / dt_fwrite,
totalSizeGB / dt_crc,
totalSizeGB / dt_munge,
totalSizeGB / dt_distr,
totalSizeGB / dt_fp16,
))
# save crc
crc32[cv.rank] = crc32_comp
# synchronize crc32
fgrid.globalsum(crc32)
# timing
t1 = gpt.time()
# write crc to metadata
if gpt.rank() == 0:
for i in range(len(crc32)):
fmeta.write("crc32[%d] = %X\n" % (i, crc32[i]))
fmeta.close()
# verbosity
if verbose:
gpt.message("* save %g GB at %g GB/s" % (totalSizeGB, totalSizeGB /
(t1 - t0)))