Adios2: Python/Fortran bindings -- bug likely in write with different dimensionality / time stepping
I'm having some issues with writing and reading N-dimensional arrays in Python, possibly over multiple time steps. Here's a test script I made to investigate. Basically, I write a really simple piece of data, and then check if I get the same hash back on the read end. Sometimes I do, sometimes I don't. I attached an example, where I write up to 6-D arrays, over 10 time steps. Doing bpls looks more or less okay.
if __name__ == "__main__":
comm = MPI.COMM_WORLD
ndim = int(sys.argv[1])
ntime = int(sys.argv[2])
size = comm.Get_size()
rank = comm.Get_rank()
globalsizes = []
hashes = []
zeros = []
fw = adios2.open('ND.bp', "w", comm)
for time in range(ntime):
h = []
z = []
for i in range(ndim):
ind = i + 1
shape = np.array([ind]*ind)
data = (rank+1)*(np.reshape(1+np.arange(np.power(ind, ind), dtype=np.float64), tuple(shape)))
start = np.zeros(ind, dtype=np.int)
start[0] = rank * shape[0]
globalsize = np.copy(shape)
globalsize[0] *= size
globalsizes.append(globalsize)
fw.write("arr{0}".format(ind), data, list(globalsize), list(start), list(shape), endl=True)
newdata = np.empty(globalsize, dtype=np.float64)
comm.Gather(data, newdata, root=0)
if rank == 0:
newdata.flags.writeable = False
h.append(hash(newdata.data))
z.append(np.sum(newdata <= 0))
hashes.append(h)
zeros.append(z)
fw.close()
fr = adios2.open("ND.bp", "r", comm)
for time in range(ntime):
if rank == 0:
for i in range(ndim):
ind = i + 1
data = fr.read("arr{0}".format(ind), np.zeros(ind, dtype=np.int), globalsizes[i], endl=True)
data.flags.writeable = False
h = hash(data.data)
if hashes[time][i] == h:
print("time: {1}, dimensionality: {0} OK".format(ind, time))
else:
print("time: {1}, dimensionality: {0} ERROR -- unequal hashes {3} != {4} , {2} obvously bad values returned".format(ind, time, np.sum(data <= 0), hashes[time][i], h))
fr.close()
mpirun -host localhost -np 4 ./test-nd.py 6 10
time: 0, dimensionality: 1 OK
time: 0, dimensionality: 2 OK
time: 0, dimensionality: 3 OK
time: 0, dimensionality: 4 ERROR -- unequal hashes -5817521839159847613 != 4710672338217278104 , 648 obvously bad values returned
time: 0, dimensionality: 5 OK
time: 0, dimensionality: 6 OK
time: 1, dimensionality: 1 OK
time: 1, dimensionality: 2 OK
time: 1, dimensionality: 3 OK
time: 1, dimensionality: 4 ERROR -- unequal hashes -5817521839159847613 != 1644371425632087350 , 104 obvously bad values returned
time: 1, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != 411882050336115912 , 10576 obvously bad values returned
time: 1, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != -8040249360400408643 , 173120 obvously bad values returned
time: 2, dimensionality: 1 OK
time: 2, dimensionality: 2 OK
time: 2, dimensionality: 3 OK
time: 2, dimensionality: 4 OK
time: 2, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != 6921899629403144131 , 10068 obvously bad values returned
time: 2, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != 1150135602477787782 , 172612 obvously bad values returned
time: 3, dimensionality: 1 OK
time: 3, dimensionality: 2 OK
time: 3, dimensionality: 3 OK
time: 3, dimensionality: 4 OK
time: 3, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != 8606326729852778618 , 9560 obvously bad values returned
time: 3, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != 5384636986928982883 , 172108 obvously bad values returned
time: 4, dimensionality: 1 OK
time: 4, dimensionality: 2 OK
time: 4, dimensionality: 3 OK
time: 4, dimensionality: 4 OK
time: 4, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != -1677615237727256141 , 9052 obvously bad values returned
time: 4, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != -163812680856230090 , 171600 obvously bad values returned
time: 5, dimensionality: 1 OK
time: 5, dimensionality: 2 OK
time: 5, dimensionality: 3 OK
time: 5, dimensionality: 4 OK
time: 5, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != -1583164979516588521 , 8548 obvously bad values returned
time: 5, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != -5321989567651457599 , 171092 obvously bad values returned
time: 6, dimensionality: 1 OK
time: 6, dimensionality: 2 OK
time: 6, dimensionality: 3 OK
time: 6, dimensionality: 4 OK
time: 6, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != 2250494806201746315 , 8040 obvously bad values returned
time: 6, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != -7413598994453349213 , 170584 obvously bad values returned
time: 7, dimensionality: 1 OK
time: 7, dimensionality: 2 OK
time: 7, dimensionality: 3 OK
time: 7, dimensionality: 4 OK
time: 7, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != 6178203745650766318 , 7532 obvously bad values returned
time: 7, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != -3610488979245109061 , 170079 obvously bad values returned
time: 8, dimensionality: 1 OK
time: 8, dimensionality: 2 OK
time: 8, dimensionality: 3 OK
time: 8, dimensionality: 4 OK
time: 8, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != -8130837426127217004 , 7024 obvously bad values returned
time: 8, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != 1935007435453733543 , 169572 obvously bad values returned
time: 9, dimensionality: 1 OK
time: 9, dimensionality: 2 OK
time: 9, dimensionality: 3 OK
time: 9, dimensionality: 4 OK
time: 9, dimensionality: 5 ERROR -- unequal hashes 7537877987181268272 != -2487306817042493317 , 6520 obvously bad values returned
time: 9, dimensionality: 6 ERROR -- unequal hashes -4189560973479895787 != 6528630630600386117 , 169064 obvously bad values returned
bpls2 ND.bp --long
double arr1 10*{4} 1 / 4
double arr2 10*{8, 2} 1 / 16
double arr3 10*{12, 3, 3} 1 / 108
double arr4 10*{16, 4, 4, 4} 1 / 1024
double arr5 10*{20, 5, 5, 5, 5} 1 / 12500
double arr6 10*{24, 6, 6, 6, 6, 6} 1 / 186624
suchyta1
All 24 comments
Thanks @suchyta1 , are the numbers reported by adios1 bpls correct? -d will dump the data.
williamfgc
on 11 Apr 2018
@williamfgc Good thing to check. No -- there are lots of zeros in the output, and there shouldn't be any. I attached the output. Looking through it, it starts with the expected output, then there'll be a really small value, after which the rest of that rank is zeros.
bpls.txt
suchyta1
on 11 Apr 2018
OK, @suchyta1 sounds like a write problem then. The reader is just returning the wrong data as-is. I will take a look, try to reproduce and add this to the testing framework. Thanks again.
williamfgc
on 11 Apr 2018
@williamfgc Thanks, William! I'm going to try to produce some kind of similar test for Fortran too. I think, something similar _might_ be going on, but I haven't been systematic enough in testing to isolate anything whether it's actually an issue in the app.
suchyta1
on 11 Apr 2018
@suchyta1 Thanks, it might be related to a buffering issue #478 . Yes please, that will help enrich the bindings tests, which is lacking.
williamfgc
on 11 Apr 2018
@williamfgc Okay, here's some Fortran testing too. I wrote a script to test different dimensionality and time stepping in Fortran. This can't be done quite as simply as for Python, but at any rate the source is here. (Along with the previous Python script too).
For example doing a similar thing as before is below. This is writing a 3D array for two timesteps, and trying that same job 5 times. There are some of the bad values, and sometimes there is a runtime error, but not always.
./fortran-test.sh 3 2
Run 1: dimensionality: 3, times: 2
ERROR: MPI open failed for ftest_3.bp: 'File not found: ftest_3.bp'
ERROR: File open failed: ftest_3.bp
Run 2: dimensionality: 3, times: 2
ERROR: MPI open failed for ftest_3.bp: 'File not found: ftest_3.bp'
ERROR: File open failed: ftest_3.bp
Run 3: dimensionality: 3, times: 2
ERROR: MPI open failed for ftest_3.bp: 'File not found: ftest_3.bp'
ERROR: File open failed: ftest_3.bp
Run 4: dimensionality: 3, times: 2
ERROR: MPI open failed for ftest_3.bp: 'File not found: ftest_3.bp'
ERROR: File open failed: ftest_3.bp
Run 5: dimensionality: 3, times: 2
double arr3 2*{9, 3, 3}
(0,0,0,0) 1 2 3 4 5 6
(0,0,2,0) 7 8 9 10 11 12
(0,1,1,0) 3.90283e+19 14 15 16 17 18
(0,2,0,0) 19 20 21 22 23 24
(0,2,2,0) 25 26 27 2 4 6
(0,3,1,0) 8 10 12 14 16 18
(0,4,0,0) 20 22 24 26 28 30
(0,4,2,0) 32 34 36 38 40 42
(0,5,1,0) 44 46 48 50 52 54
(0,6,0,0) 3 6 9 12 15 18
(0,6,2,0) 21 24 27 30 33 36
(0,7,1,0) -2.1508e+275 42 45 48 51 54
(0,8,0,0) 57 60 63 66 69 72
(0,8,2,0) 75 78 81 1 2 3
(1,0,1,0) 4 5 6 7 8 9
(1,1,0,0) 10 11 12 3.90283e+19 14 15
(1,1,2,0) 16 17 18 19 20 21
(1,2,1,0) 22 23 24 25 26 27
(1,3,0,0) 2 4 6 8 10 12
(1,3,2,0) 14 16 18 20 22 24
(1,4,1,0) 26 28 30 32 34 36
(1,5,0,0) 38 40 42 44 46 48
(1,5,2,0) 50 52 54 3 6 9
(1,6,1,0) 12 15 18 21 24 27
(1,7,0,0) 30 33 36 -2.1508e+275 42 45
(1,7,2,0) 48 51 54 57 60 63
(1,8,1,0) 66 69 72 75 78 81
@suchyta1 can you try only advancing at write at the last value of the iteration. Advance, and endl in Python, affect all variables not just the current one. It is no different from the equivalent in each language, it is just that in adios2 the step (not line) is the logical grouping of data.
williamfgc
on 12 Apr 2018
I might not be understanding what you mean @williamfgc but I think that's what I'm doing. I'm only writing one array in this case (the only data in file) -- and that's the only write statement in each loop iteration.
suchyta1
on 12 Apr 2018
Everytime write is called it is advancing the step. It should be only arr"max" doing that. OK, I can see the Fortran example is doing only one variable, but the Python example is advancing the step in every write for multiple variables.
williamfgc
on 12 Apr 2018
Thanks @williamfgc . That was it in the python script. All the python tests now pass. In Fortran I did not have a barrier that I should. I'll share the results with these fixed next.
suchyta1
on 12 Apr 2018
On the Fortran side, the last issue I see looks a lot like issue #478. I write a 3x3 array from 3 processes, either from Fortran or Python using the high level API. (And do two runs each.) I attached the two output logs, and the Fortran source file. In summary I see a few bad values from Fortan, but not Python. I also attached the fortran source, and pushed updates into the repo.
program test
use mpi
use adios2
implicit none
integer, parameter :: ndims=3
integer :: ierr, size, rank, i
real(kind=8), dimension(27) :: dataarr
integer(kind=8), dimension(ndims) :: globalsizes, offsets, sizes
type(adios2_file) :: adios2_fhw, adios2_fhr
call mpi_init(ierr)
call mpi_comm_size(mpi_comm_world, size, ierr)
call mpi_comm_rank(mpi_comm_world, rank, ierr)
do i=1, ndims
if (i.eq.1) then
offsets(i) = rank * ndims
globalsizes(i) = size * ndims
else
offsets(i) = 0
globalsizes(i) = ndims
end if
sizes(i) = ndims
end do
do i=1, 27
dataarr(i) = (rank+1) * (dataarr(i) + i)
end do
call adios2_fopen(adios2_fhw, 'ftest_3.bp', adios2_mode_write, MPI_COMM_WORLD, ierr)
do i=1, 3
call adios2_fwrite(adios2_fhw, "arr3", dataarr, 3, globalsizes, offsets, sizes, adios2_advance_yes, ierr)
call mpi_barrier(mpi_comm_world, ierr)
end do
call adios2_fclose(adios2_fhw, ierr)
end program test
# fortran.txt
Run 1: dimensionality: 3, times: 3
double arr3 3*{9, 3, 3}
(0,0,0,0) 1 2 3 4 5 6
(0,0,2,0) 7 8 9 10 11 12
(0,1,1,0) 5.0812e+173 14 15 16 17 18
(0,2,0,0) 19 20 21 22 23 24
(0,2,2,0) 25 26 27 2 4 6
(0,3,1,0) 8 10 12 14 16 18
(0,4,0,0) 20 22 24 26 28 30
(0,4,2,0) 32 34 36 38 40 42
(0,5,1,0) 44 46 48 50 52 54
(0,6,0,0) 3 6 9 12 15 18
(0,6,2,0) 21 24 27 30 33 36
(0,7,1,0) 39 42 45 48 51 54
(0,8,0,0) 57 60 63 66 69 72
(0,8,2,0) 75 78 81 1 2 3
(1,0,1,0) 4 5 6 7 8 9
(1,1,0,0) 10 11 12 5.0812e+173 14 15
(1,1,2,0) 16 17 18 19 20 21
(1,2,1,0) 22 23 24 25 26 27
(1,3,0,0) 2 4 6 8 10 12
(1,3,2,0) 14 16 18 20 22 24
(1,4,1,0) 26 28 30 32 34 36
(1,5,0,0) 38 40 42 44 46 48
(1,5,2,0) 50 52 54 3 6 9
(1,6,1,0) 12 15 18 21 24 27
(1,7,0,0) 30 33 36 39 42 45
(1,7,2,0) 48 51 54 57 60 63
(1,8,1,0) 66 69 72 75 78 81
(2,0,0,0) 1 2 3 4 5 6
(2,0,2,0) 7 8 9 10 11 12
(2,1,1,0) 5.0812e+173 14 15 16 17 18
(2,2,0,0) 19 20 21 22 23 24
(2,2,2,0) 25 26 27 2 4 6
(2,3,1,0) 8 10 12 14 16 18
(2,4,0,0) 20 22 24 26 28 30
(2,4,2,0) 32 34 36 38 40 42
(2,5,1,0) 44 46 48 50 52 54
(2,6,0,0) 3 6 9 12 15 18
(2,6,2,0) 21 24 27 30 33 36
(2,7,1,0) 39 42 45 48 51 54
(2,8,0,0) 57 60 63 66 69 72
(2,8,2,0) 75 78 81
Run 2: dimensionality: 3, times: 3
double arr3 3*{9, 3, 3}
(0,0,0,0) 1 2 3 4 5 6
(0,0,2,0) 7 8 9 10 11 12
(0,1,1,0) 13 14 15 16 17 18
(0,2,0,0) 19 20 21 22 23 24
(0,2,2,0) 25 26 27 2 4 6
(0,3,1,0) 8 10 12 14 16 18
(0,4,0,0) 20 22 24 9.8557e+226 28 30
(0,4,2,0) 32 34 36 38 40 42
(0,5,1,0) 44 46 48 50 52 54
(0,6,0,0) 3 6 9 12 15 18
(0,6,2,0) 21 24 27 30 33 36
(0,7,1,0) 4.12371e+241 42 45 48 51 54
(0,8,0,0) 57 60 63 66 69 72
(0,8,2,0) 75 78 81 1 2 3
(1,0,1,0) 4 5 6 7 8 9
(1,1,0,0) 10 11 12 13 14 15
(1,1,2,0) 16 17 18 19 20 21
(1,2,1,0) 22 23 24 25 26 27
(1,3,0,0) 2 4 6 8 10 12
(1,3,2,0) 14 16 18 20 22 24
(1,4,1,0) 9.8557e+226 28 30 32 34 36
(1,5,0,0) 38 40 42 44 46 48
(1,5,2,0) 50 52 54 3 6 9
(1,6,1,0) 12 15 18 21 24 27
(1,7,0,0) 30 33 36 4.12371e+241 42 45
(1,7,2,0) 48 51 54 57 60 63
(1,8,1,0) 66 69 72 75 78 81
(2,0,0,0) 1 2 3 4 5 6
(2,0,2,0) 7 8 9 10 11 12
(2,1,1,0) 13 14 15 16 17 18
(2,2,0,0) 19 20 21 22 23 24
(2,2,2,0) 25 26 27 2 4 6
(2,3,1,0) 8 10 12 14 16 18
(2,4,0,0) 20 22 24 9.8557e+226 28 30
(2,4,2,0) 32 34 36 38 40 42
(2,5,1,0) 44 46 48 50 52 54
(2,6,0,0) 3 6 9 12 15 18
(2,6,2,0) 21 24 27 30 33 36
(2,7,1,0) 4.12371e+241 42 45 48 51 54
(2,8,0,0) 57 60 63 66 69 72
(2,8,2,0) 75 78 81
md5-7e6c5325750e637ddec9d98cddee32c7
# python.txt
double arr3 3*{9, 3, 3}
(0,0,0,0) 1 2 3 4 5 6
(0,0,2,0) 7 8 9 10 11 12
(0,1,1,0) 13 14 15 16 17 18
(0,2,0,0) 19 20 21 22 23 24
(0,2,2,0) 25 26 27 2 4 6
(0,3,1,0) 8 10 12 14 16 18
(0,4,0,0) 20 22 24 26 28 30
(0,4,2,0) 32 34 36 38 40 42
(0,5,1,0) 44 46 48 50 52 54
(0,6,0,0) 3 6 9 12 15 18
(0,6,2,0) 21 24 27 30 33 36
(0,7,1,0) 39 42 45 48 51 54
(0,8,0,0) 57 60 63 66 69 72
(0,8,2,0) 75 78 81 1 2 3
(1,0,1,0) 4 5 6 7 8 9
(1,1,0,0) 10 11 12 13 14 15
(1,1,2,0) 16 17 18 19 20 21
(1,2,1,0) 22 23 24 25 26 27
(1,3,0,0) 2 4 6 8 10 12
(1,3,2,0) 14 16 18 20 22 24
(1,4,1,0) 26 28 30 32 34 36
(1,5,0,0) 38 40 42 44 46 48
(1,5,2,0) 50 52 54 3 6 9
(1,6,1,0) 12 15 18 21 24 27
(1,7,0,0) 30 33 36 39 42 45
(1,7,2,0) 48 51 54 57 60 63
(1,8,1,0) 66 69 72 75 78 81
(2,0,0,0) 1 2 3 4 5 6
(2,0,2,0) 7 8 9 10 11 12
(2,1,1,0) 13 14 15 16 17 18
(2,2,0,0) 19 20 21 22 23 24
(2,2,2,0) 25 26 27 2 4 6
(2,3,1,0) 8 10 12 14 16 18
(2,4,0,0) 20 22 24 26 28 30
(2,4,2,0) 32 34 36 38 40 42
(2,5,1,0) 44 46 48 50 52 54
(2,6,0,0) 3 6 9 12 15 18
(2,6,2,0) 21 24 27 30 33 36
(2,7,1,0) 39 42 45 48 51 54
(2,8,0,0) 57 60 63 66 69 72
(2,8,2,0) 75 78 81
double arr3 3*{9, 3, 3}
(0,0,0,0) 1 2 3 4 5 6
(0,0,2,0) 7 8 9 10 11 12
(0,1,1,0) 13 14 15 16 17 18
(0,2,0,0) 19 20 21 22 23 24
(0,2,2,0) 25 26 27 2 4 6
(0,3,1,0) 8 10 12 14 16 18
(0,4,0,0) 20 22 24 26 28 30
(0,4,2,0) 32 34 36 38 40 42
(0,5,1,0) 44 46 48 50 52 54
(0,6,0,0) 3 6 9 12 15 18
(0,6,2,0) 21 24 27 30 33 36
(0,7,1,0) 39 42 45 48 51 54
(0,8,0,0) 57 60 63 66 69 72
(0,8,2,0) 75 78 81 1 2 3
(1,0,1,0) 4 5 6 7 8 9
(1,1,0,0) 10 11 12 13 14 15
(1,1,2,0) 16 17 18 19 20 21
(1,2,1,0) 22 23 24 25 26 27
(1,3,0,0) 2 4 6 8 10 12
(1,3,2,0) 14 16 18 20 22 24
(1,4,1,0) 26 28 30 32 34 36
(1,5,0,0) 38 40 42 44 46 48
(1,5,2,0) 50 52 54 3 6 9
(1,6,1,0) 12 15 18 21 24 27
(1,7,0,0) 30 33 36 39 42 45
(1,7,2,0) 48 51 54 57 60 63
(1,8,1,0) 66 69 72 75 78 81
(2,0,0,0) 1 2 3 4 5 6
(2,0,2,0) 7 8 9 10 11 12
(2,1,1,0) 13 14 15 16 17 18
(2,2,0,0) 19 20 21 22 23 24
(2,2,2,0) 25 26 27 2 4 6
(2,3,1,0) 8 10 12 14 16 18
(2,4,0,0) 20 22 24 26 28 30
(2,4,2,0) 32 34 36 38 40 42
(2,5,1,0) 44 46 48 50 52 54
(2,6,0,0) 3 6 9 12 15 18
(2,6,2,0) 21 24 27 30 33 36
(2,7,1,0) 39 42 45 48 51 54
(2,8,0,0) 57 60 63 66 69 72
(2,8,2,0) 75 78 81
@suchyta1 great! thanks for double checking. I wonder if it's 'cause we are passing a linearized 1D array in Fortran rather than an actual 3D array. Still they are both memory contiguous so it shouldn't matter. I am trying to debug now.
williamfgc
on 12 Apr 2018
@suchyta1 could you make the python test portable to python3...I get an error related to the hash table when running with python3. I want to make it part of the adios2 testing suite, which requires to make it agnostic of python version. Let me know!
williamfgc
on 12 Apr 2018
@williamfgc Sure. I wasn't aware hash would do that, but that's good to generally know anyway. I could also just use numpy.all() too.
suchyta1
on 12 Apr 2018
Updated hashing should work in either python 2 or 3.
suchyta1
on 12 Apr 2018
@suchyta1 great, thanks! In the Fortran test, what do you get if you zero initialize the dataarr. Right now it seems it depends on uninitialized values.
williamfgc
on 13 Apr 2018
@williamfgc Yes, zero initializing makes the bad values disappear and become what I expect.
suchyta1
on 13 Apr 2018
@williamfgc
There's now Fortran checking to make sure that the written and read data are the same here.
For example make fortran-test.
suchyta1
on 23 Apr 2018
@williamfgc I have another test where I'm reading from Python. I'm getting segfaults when I use read() from Python (high level API), and I'm not quite sure why. I generated this with some irrelevant test output from GENE (Fortran), but at any rate, I've attached the .bp data here. (It's too big to attach here).
What I use is a really simple reading script.
#!/usr/bin/env python
from __future__ import absolute_import, division, print_function, unicode_literals
from mpi4py import MPI
import adios2
import numpy as np
if __name__ == "__main__":
comm = MPI.COMM_WORLD
fr = adios2.open('g1_adios2.bp', "r", comm)
datashape = np.zeros(6, dtype=np.int64)
for i in range(3):
data = fr.read('global_offsets')
print(data)
var = fr.available_variables()
dshape = var['test']['Shape'].split(',')
for i in range(len(dshape)):
datashape[i] = int(dshape[i])
print(datashape)
print("reading test")
#data = fr.read('test', [0,0,0,0,0,0], datashape, 0, 1)
data = fr.read('test')
print(data)
comm.barrier()
fr.close()
python test.py
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 1 2 3]
[0 0 0 0]]
[ 1 4 24 16 64 64]
reading test
Segmentation fault: 11
bpls g1_adios2.bp
long long local_dims 10*{6, 4}
double test 10*{1, 4, 24, 16, 64, 64}
double g_imag 10*{1, 4, 24, 16, 64, 64}
integer timestep 10*scalar
double g_real 10*{1, 4, 24, 16, 64, 64}
long long global_offsets 10*{6, 4}
long long rank 10*{4}
The data for "test" should mostly be zeros, but bpls correctly shows a few non-zeros that I expect:
bpls g1_adios2.bp -d test | grep -v "0 0 0 0 0 0"
double test 10*{1, 4, 24, 16, 64, 64}
(0,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(0,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(0,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(1,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(1,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(1,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(2,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(2,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(2,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(3,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(3,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(3,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(4,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(4,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(4,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(5,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(5,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(5,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(6,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(6,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(6,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(7,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(7,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(7,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(8,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(8,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(8,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
(9,0,1, 0, 0, 0, 0) 1 0 0 0 0 0
(9,0,2, 0, 0, 0, 0) 2 0 0 0 0 0
(9,0,3, 0, 0, 0, 0) 3 0 0 0 0 0
@suchyta1 is test a local variable (shape is NULL) or is a global variable? I don't have support for local variable reads, yet. See issue #574
williamfgc
on 24 Apr 2018
@williamfgc It's a global variable, with these dimensions:
double test 10*{1, 4, 24, 16, 64, 64}. Using fr.available_variables() gets the dimensions correctly when I run the python script.
suchyta1
on 24 Apr 2018
@suchyta1 is this with the latest master?
williamfgc
on 24 Apr 2018
@williamfgc Just rebuilt Python with latest master and it looks the same as above. However, the file was produced awhile ago. I"ll rebuild the writer too to make sure that wasn't using something outdated that's since been updated.
suchyta1
on 24 Apr 2018
@suchyta1 thanks! My guess is that we are blowing up the stack. Can you run valgrind?
williamfgc
on 24 Apr 2018
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