def run():
max_ndim = 6
for i in xrange(1, max_ndim + 1):
src = dnumpytest.random_list(range(2, i + 2))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
Bd = np.array(src, dtype=float, dist=True)
Bf = np.array(src, dtype=float, dist=False)
Cd = Ad + Bd + 42 + Bd[-1]
Cf = Af + Bf + 42 + Bf[-1]
Cd = Cd[::2] + Cd[::2, ...] + Cd[0, np.newaxis]
Cf = Cf[::2] + Cf[::2, ...] + Cf[0, np.newaxis]
Dd = np.array(Cd, dtype=float, dist=True)
Df = np.array(Cf, dtype=float, dist=False)
Dd[1:] = Cd[:-1]
Df[1:] = Cf[:-1]
Cd = Dd + Bd[np.newaxis, -1]
Cf = Df + Bf[np.newaxis, -1]
Cd[1:] = Cd[:-1]
Cf[1:] = Cf[:-1]
if not dnumpytest.array_equal(Cd, Cf):
raise Exception("Uncorrect result array\n")
for i in xrange(3, max_ndim + 3):
src = dnumpytest.random_list([i, i, i])
Ad = np.array(src, dist=True, dtype=float)
Af = np.array(src, dist=False, dtype=float)
Bd = np.array(src, dist=True, dtype=float)
Bf = np.array(src, dist=False, dtype=float)
Cd = Ad[::2, ::2, ::2] + Bd[::2, ::2, ::2] + Ad[::2, 1, 2]
Cf = Af[::2, ::2, ::2] + Bf[::2, ::2, ::2] + Af[::2, 1, 2]
if not dnumpytest.array_equal(Cd, Cf):
raise Exception("Uncorrect result array\n")
def run():
max_ndim = 6
for i in xrange(1,max_ndim+1):
src = dnumpytest.random_list(range(2, i+2))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
Bd = np.array(src, dtype=float, dist=True)
Bf = np.array(src, dtype=float, dist=False)
Cd = Ad + Bd + 42 + Bd[-1]
Cf = Af + Bf + 42 + Bf[-1]
Cd = Cd[::2] + Cd[::2,...] + Cd[0,np.newaxis]
Cf = Cf[::2] + Cf[::2,...] + Cf[0,np.newaxis]
Dd = np.array(Cd, dtype=float, dist=True)
Df = np.array(Cf, dtype=float, dist=False)
Dd[1:] = Cd[:-1]
Df[1:] = Cf[:-1]
Cd = Dd + Bd[np.newaxis,-1]
Cf = Df + Bf[np.newaxis,-1]
Cd[1:] = Cd[:-1]
Cf[1:] = Cf[:-1]
if not dnumpytest.array_equal(Cd,Cf):
raise Exception("Uncorrect result array\n")
for i in xrange(3,max_ndim+3):
src = dnumpytest.random_list([i,i,i])
Ad = np.array(src, dist=True, dtype=float)
Af = np.array(src, dist=False, dtype=float)
Bd = np.array(src, dist=True, dtype=float)
Bf = np.array(src, dist=False, dtype=float)
Cd = Ad[::2, ::2, ::2] + Bd[::2, ::2, ::2] + Ad[::2,1,2]
Cf = Af[::2, ::2, ::2] + Bf[::2, ::2, ::2] + Af[::2,1,2]
if not dnumpytest.array_equal(Cd,Cf):
raise Exception("Uncorrect result array\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n"%(np.RANK),
return
try:#This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n"%(np.RANK),
return
#Non-view test - identical to the one in test_dot.py
niter = 6
for m in range(2,niter+2):
for n in range(2,niter+2):
for k in range(2,niter+2):
Asrc = dnumpytest.random_list([k,m])
Bsrc = dnumpytest.random_list([n,k])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = np.array(Bsrc, dtype=float, dist=True)
Bf = np.array(Bsrc, dtype=float, dist=False)
Cd = pyHPC.summa(Ad,Bd)
Cf = np.dot(Af,Bf)
if not dnumpytest.array_equal(Cd,Cf):
raise Exception("Uncorrect result matrix\n")
niter *= 2
Asrc = dnumpytest.random_list([niter,niter])
Bsrc = dnumpytest.random_list([niter,niter])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = np.array(Bsrc, dtype=float, dist=True)
Bf = np.array(Bsrc, dtype=float, dist=False)
Cd = np.zeros((niter,niter),dtype=float, dist=True)
BS = np.BLOCKSIZE
for m in xrange(0,niter-BS, BS):
for n in xrange(0,niter-BS,BS):
for k in xrange(0,niter-BS,BS):
tAd = Ad[m:,k:]
tAf = Af[m:,k:]
tBd = Bd[k:,n:]
tBf = Bf[k:,n:]
tCd = Cd[m:,n:]
tCd = pyHPC.matmul(tAd,tBd)
tCf = np.dot(tAf,tBf)
if not dnumpytest.array_equal(tCd,tCf):
raise Exception("Uncorrect result matrix\n")
for m in xrange(BS,niter+BS, BS):
for n in xrange(BS,niter+BS,BS):
for k in xrange(BS,niter+BS,BS):
tAd = Ad[:m,:k]
tAf = Af[:m,:k]
tBd = Bd[:k,:n]
tBf = Bf[:k,:n]
tCd = Cd[:m,:n]
tCd = pyHPC.matmul(tAd,tBd)
tCf = np.dot(tAf,tBf)
if not dnumpytest.array_equal(tCd,tCf):
raise Exception("Uncorrect result matrix\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n" % (np.RANK),
return
try: #This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n" % (np.RANK),
return
#Non-view test - identical to the one in test_dot.py
niter = 6
for m in range(2, niter + 2):
for n in range(2, niter + 2):
for k in range(2, niter + 2):
Asrc = dnumpytest.random_list([k, m])
Bsrc = dnumpytest.random_list([n, k])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = np.array(Bsrc, dtype=float, dist=True)
Bf = np.array(Bsrc, dtype=float, dist=False)
Cd = pyHPC.summa(Ad, Bd)
Cf = np.dot(Af, Bf)
if not dnumpytest.array_equal(Cd, Cf):
raise Exception("Uncorrect result matrix\n")
niter *= 2
Asrc = dnumpytest.random_list([niter, niter])
Bsrc = dnumpytest.random_list([niter, niter])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = np.array(Bsrc, dtype=float, dist=True)
Bf = np.array(Bsrc, dtype=float, dist=False)
Cd = np.zeros((niter, niter), dtype=float, dist=True)
BS = np.BLOCKSIZE
for m in xrange(0, niter - BS, BS):
for n in xrange(0, niter - BS, BS):
for k in xrange(0, niter - BS, BS):
tAd = Ad[m:, k:]
tAf = Af[m:, k:]
tBd = Bd[k:, n:]
tBf = Bf[k:, n:]
tCd = Cd[m:, n:]
tCd = pyHPC.matmul(tAd, tBd)
tCf = np.dot(tAf, tBf)
if not dnumpytest.array_equal(tCd, tCf):
raise Exception("Uncorrect result matrix\n")
for m in xrange(BS, niter + BS, BS):
for n in xrange(BS, niter + BS, BS):
for k in xrange(BS, niter + BS, BS):
tAd = Ad[:m, :k]
tAf = Af[:m, :k]
tBd = Bd[:k, :n]
tBf = Bf[:k, :n]
tCd = Cd[:m, :n]
tCd = pyHPC.matmul(tAd, tBd)
tCf = np.dot(tAf, tBf)
if not dnumpytest.array_equal(tCd, tCf):
raise Exception("Uncorrect result matrix\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n" % (np.RANK),
return
try: #This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n" % (np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n" % (
np.RANK),
return
max_ndim = 3
for i in xrange(4, max_ndim + 4):
SIZE = i * np.BLOCKSIZE
src = dnumpytest.random_list(
range(np.BLOCKSIZE * 2, SIZE, np.BLOCKSIZE))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
slice = [((1, Ad.shape[0] / np.BLOCKSIZE))]
for d in xrange(Ad.ndim - 1):
slice.append((0, Ad.shape[d + 1] / np.BLOCKSIZE))
for a in Ad.blocks(slice):
a += 100.0
Af[np.BLOCKSIZE:] += 100.0
if not dnumpytest.array_equal(Ad, Af):
raise Exception("Uncorrect result array\n")
max_ndim = 3
for i in xrange(4, max_ndim + 4):
SIZE = i * np.BLOCKSIZE
src = dnumpytest.random_list(
range(np.BLOCKSIZE * 2, SIZE, np.BLOCKSIZE))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
slice = [((0, (Ad.shape[0] / np.BLOCKSIZE) - 1))]
for d in xrange(Ad.ndim - 1):
slice.append((0, Ad.shape[d + 1] / np.BLOCKSIZE))
for a in Ad.blocks(slice):
a += 100.0
Af[:-np.BLOCKSIZE] += 100.0
if not dnumpytest.array_equal(Ad, Af):
raise Exception("Uncorrect result array\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n"%(np.RANK),
return
try:#This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n"%(np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n"%(np.RANK),
return
max_ndim = 3
for i in xrange(4, max_ndim+4):
SIZE = i*np.BLOCKSIZE
src = dnumpytest.random_list(range(np.BLOCKSIZE*2, SIZE, np.BLOCKSIZE))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
slice = [((1,Ad.shape[0]/np.BLOCKSIZE))]
for d in xrange(Ad.ndim-1):
slice.append((0,Ad.shape[d+1]/np.BLOCKSIZE))
for a in Ad.blocks(slice):
a += 100.0
Af[np.BLOCKSIZE:] += 100.0
if not dnumpytest.array_equal(Ad,Af):
raise Exception("Uncorrect result array\n")
max_ndim = 3
for i in xrange(4, max_ndim+4):
SIZE = i*np.BLOCKSIZE
src = dnumpytest.random_list(range(np.BLOCKSIZE*2, SIZE, np.BLOCKSIZE))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
slice = [((0,(Ad.shape[0]/np.BLOCKSIZE)-1))]
for d in xrange(Ad.ndim-1):
slice.append((0,Ad.shape[d+1]/np.BLOCKSIZE))
for a in Ad.blocks(slice):
a += 100.0
Af[:-np.BLOCKSIZE] += 100.0
if not dnumpytest.array_equal(Ad,Af):
raise Exception("Uncorrect result array\n")
def run():
niter = 6
for m in range(2, niter + 2):
for n in range(2, niter + 2):
for k in range(2, niter + 2):
Asrc = dnumpytest.random_list([k, m])
Bsrc = dnumpytest.random_list([m, k])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = np.array(Bsrc, dtype=float, dist=True)
Bf = np.array(Bsrc, dtype=float, dist=False)
Cd = np.dot(Ad, Bd)
Cf = np.dot(Af, Bf)
if not dnumpytest.array_equal(Cd, Cf):
raise Exception("Uncorrect result matrix\n")
def run():
niter = 6
for m in range(2,niter+2):
for n in range(2,niter+2):
for k in range(2,niter+2):
Asrc = dnumpytest.random_list([k,m])
Bsrc = dnumpytest.random_list([m,k])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = np.array(Bsrc, dtype=float, dist=True)
Bf = np.array(Bsrc, dtype=float, dist=False)
Cd = np.dot(Ad,Bd)
Cf = np.dot(Af,Bf)
if not dnumpytest.array_equal(Cd,Cf):
raise Exception("Uncorrect result matrix\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n" % (np.RANK),
return
try: #This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n" % (np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n" % (
np.RANK),
return
niter = 5
for m in range(np.BLOCKSIZE, niter * np.BLOCKSIZE, np.BLOCKSIZE):
for n in range(np.BLOCKSIZE, niter * np.BLOCKSIZE, np.BLOCKSIZE):
for k in range(np.BLOCKSIZE, niter * np.BLOCKSIZE, np.BLOCKSIZE):
for axis in permutations(xrange(3)):
Asrc = dnumpytest.random_list([m, n, k])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = pyHPC.transpose(Ad, axis)
Bf = np.transpose(Af, axis)
if not dnumpytest.array_equal(Bd, Bf):
raise Exception("Uncorrect result matrix\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n"%(np.RANK),
return
try:#This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n"%(np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n"%(np.RANK),
return
niter = 5
for m in range(np.BLOCKSIZE,niter*np.BLOCKSIZE, np.BLOCKSIZE):
for n in range(np.BLOCKSIZE,niter*np.BLOCKSIZE, np.BLOCKSIZE):
for k in range(np.BLOCKSIZE,niter*np.BLOCKSIZE, np.BLOCKSIZE):
for axis in permutations(xrange(3)):
Asrc = dnumpytest.random_list([m,n,k])
Ad = np.array(Asrc, dtype=float, dist=True)
Af = np.array(Asrc, dtype=float, dist=False)
Bd = pyHPC.transpose(Ad, axis)
Bf = np.transpose(Af, axis)
if not dnumpytest.array_equal(Bd,Bf):
raise Exception("Uncorrect result matrix\n")
def run():
db_length = 100
ndims = 64
src = dnumpytest.random_list((db_length, ndims))
Seq = kNN(src, False)
Par = kNN(src, True)
if not dnumpytest.array_equal(Seq, Par):
raise Exception("Uncorrect result matrix\n")
def run():
db_length = 100
ndims = 64
src = dnumpytest.random_list((db_length, ndims))
Seq = kNN(src, False)
Par = kNN(src, True)
if not dnumpytest.array_equal(Seq,Par):
raise Exception("Uncorrect result matrix\n")
def run():
#Make sure we have one non-distributed dimension.
np.datalayout([(2,1,1),(3,1,1)])
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n"%(np.RANK),
return
try:#This test requires the scipy module
from scipy import linalg
except:
print "[rank %d] Warning - ignored scipy not found\n"%(np.RANK),
return
try:#This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n"%(np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n"%(np.RANK),
return
#2D FFT
for SIZE1 in xrange(np.BLOCKSIZE,np.BLOCKSIZE*8,np.BLOCKSIZE):
for SIZE2 in xrange(np.BLOCKSIZE,np.BLOCKSIZE*8,np.BLOCKSIZE):
src = dnumpytest.random_list([SIZE1,SIZE2])
Ad = np.array(src, dtype=np.complex, dist=True)
Af = np.array(src, dtype=np.complex, dist=False)
Bd = pyHPC.fft2d(Ad)
Bf = np.fft.fft2(Af)
if not dnumpytest.array_equal(Bf,Bd,maxerror=1e-6):
raise Exception("Uncorrect result array\n")
#3D FFT
for SIZE1 in xrange(np.BLOCKSIZE,np.BLOCKSIZE*4,np.BLOCKSIZE):
for SIZE2 in xrange(np.BLOCKSIZE,np.BLOCKSIZE*4,np.BLOCKSIZE):
for SIZE3 in xrange(np.BLOCKSIZE,np.BLOCKSIZE*4,np.BLOCKSIZE):
src = dnumpytest.random_list([SIZE1,SIZE2,SIZE3])
Ad = np.array(src, dtype=np.complex, dist=True)
Af = np.array(src, dtype=np.complex, dist=False)
Bd = pyHPC.fft3d(Ad)
Bf = np.fft.fftn(Af)
if not dnumpytest.array_equal(Bd,Bf,maxerror=1e-6):
raise Exception("Uncorrect result array\n")
def run():
niters = 10
for i in xrange(niters):
for j in xrange(niters):
src = dnumpytest.random_list([i+1,j+1])
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
Cd = Ad.diagonal()
Cf = Af.diagonal()
if not dnumpytest.array_equal(Cd,Cf):
raise Exception("Uncorrect result matrix\n")
def run():
max_ndim = 6
for i in range(1,max_ndim+1):
src = dnumpytest.random_list(range(2, i+2))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
for j in range(len(Ad.shape)):
Cd = np.add.reduce(Ad,j)
Cf = np.add.reduce(Af,j)
if not dnumpytest.array_equal(Cd,Cf):
raise Exception("Uncorrect result array\n")
return (False, "")
def run():
max_ndim = 6
for i in range(1, max_ndim + 1):
src = dnumpytest.random_list(range(2, i + 2))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
for j in range(len(Ad.shape)):
Cd = np.add.reduce(Ad, j)
Cf = np.add.reduce(Af, j)
if not dnumpytest.array_equal(Cd, Cf):
raise Exception("Uncorrect result array\n")
return (False, "")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n" % (np.RANK),
return
try: #This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n" % (np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n" % (
np.RANK),
return
max_ndim = 4
BS = np.BLOCKSIZE
for i in xrange(2, max_ndim + 2):
src = dnumpytest.random_list(range(BS, i * BS, BS))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
Bd = Ad[BS:, ...]
Bf = Af[BS:, ...]
Cd = Bd.local()
Cf = Bf
Cd += 42.0
Cf += 42.0
Bd = Ad[BS * 2:, ...]
Bf = Af[BS * 2:, ...]
Cd = Bd.local()
Cf = Bf
Cd += 4.0
Cf += 4.0
Bd = Ad[:BS, ...]
Bf = Af[:BS, ...]
Cd = Bd.local()
Cf = Bf
Cd += 142.0
Cf += 142.0
Bd = Ad[:BS * 2, ...]
Bf = Af[:BS * 2, ...]
Cd = Bd.local()
Cf = Bf
Cd += 143.0
Cf += 143.0
Bd = Ad[..., :BS]
Bf = Af[..., :BS]
Cd = Bd.local()
Cf = Bf
Cd += 1042.0
Cf += 1042.0
if not dnumpytest.array_equal(Ad, Af):
raise Exception("Uncorrect result array\n")
def run():
if not np.SPMD_MODE:
print "[rank %d] Warning - ignored in non-SPMD mode\n"%(np.RANK),
return
try:#This test requires the pyHPC module
import pyHPC
except:
print "[rank %d] Warning - ignored pyHPC not found\n"%(np.RANK),
return
if np.BLOCKSIZE > 10:
print "[rank %d] Warning - ignored np.BLOCKSIZE too high\n"%(np.RANK),
return
max_ndim = 4
BS = np.BLOCKSIZE
for i in xrange(2, max_ndim+2):
src = dnumpytest.random_list(range(BS, i*BS, BS))
Ad = np.array(src, dtype=float, dist=True)
Af = np.array(src, dtype=float, dist=False)
Bd = Ad[BS:,...]
Bf = Af[BS:,...]
Cd = Bd.local()
Cf = Bf
Cd += 42.0
Cf += 42.0
Bd = Ad[BS*2:,...]
Bf = Af[BS*2:,...]
Cd = Bd.local()
Cf = Bf
Cd += 4.0
Cf += 4.0
Bd = Ad[:BS,...]
Bf = Af[:BS,...]
Cd = Bd.local()
Cf = Bf
Cd += 142.0
Cf += 142.0
Bd = Ad[:BS*2,...]
Bf = Af[:BS*2,...]
Cd = Bd.local()
Cf = Bf
Cd += 143.0
Cf += 143.0
Bd = Ad[...,:BS]
Bf = Af[...,:BS]
Cd = Bd.local()
Cf = Bf
Cd += 1042.0
Cf += 1042.0
if not dnumpytest.array_equal(Ad,Af):
raise Exception("Uncorrect result array\n")
def run():
if np.SPMD_MODE:
print "[rank %d] Warning - ignored in SPMD mode\n" % (np.RANK),
return
try: #This test requires zlib
import zlib
except:
print "[rank %d] Warning - ignored zlib not found\n" % (np.RANK),
return
max_ndim = 6
for i in xrange(1, max_ndim + 1):
src = dnumpytest.random_list(random.sample(xrange(1, 10), i))
A = np.array(src, dtype=float, dist=True)
fname = "distnumpt_test_matrix.npy"
np.save(fname, A)
Bf = np.load(fname, dist=False)
Bd = np.load(fname, dist=True)
if not dnumpytest.array_equal(Bf, Bd):
subprocess.check_call(('rm %s' % fname), shell=True)
raise Exception("Uncorrect result array\n")
subprocess.check_call(('rm %s' % fname), shell=True)
