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(): 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(): A = load("%sJacobi_Amatrix.npy"%dnumpytest.DataSetDir, dist=True) B = load("%sJacobi_Bvector.npy"%dnumpytest.DataSetDir, dist=True) C = load("%sJacobi_Cvector.npy"%dnumpytest.DataSetDir, dist=True) result = jacobi(A,B) if not dnumpytest.array_equal(C,result): raise Exception("Uncorrect result vector\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(): if np.SPMD_MODE: #SPMD mode is not support since we use random values. print "[rank %d] Warning - ignored in SPMD mode\n" % (np.RANK), return random_state = random.getstate() Seq = gameoflife(100, 100, 5, False, random_state) Par = gameoflife(100, 100, 5, True, random_state) if not dnumpytest.array_equal(Seq, Par): raise Exception("Uncorrect result matrix\n")
def run(): if np.SPMD_MODE: #SPMD mode is not support since we use random values. print "[rank %d] Warning - ignored in SPMD mode\n"%(np.RANK), return random_state = random.getstate() Seq = gameoflife(100,100,5,False,random_state) Par = gameoflife(100,100,5,True,random_state) 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 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 for SIZE in xrange(np.BLOCKSIZE,np.BLOCKSIZE*10,np.BLOCKSIZE): (Ld, Ud) = pyHPC.lu(gen_matrix(SIZE,True)) (P, Lf, Uf) = linalg.lu(gen_matrix(SIZE,False)) #There seems to be a transpose bug in SciPy's LU Lf = Lf.T Uf = Uf.T if not (np.diag(P) == 1).all():#We do not support pivoting raise Exception("Pivoting was needed!") if not dnumpytest.array_equal(Ld,Lf,maxerror=1e-1): del Ld del Ud raise Exception("Uncorrect L matrix\n") if not dnumpytest.array_equal(Ud,Uf,maxerror=1e-1): del Ld del Ud raise Exception("Uncorrect U 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(): 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 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)
def run(): Seq = SOR(10,10,False) Par = SOR(10,10,True) if not dnumpytest.array_equal(Seq,Par): raise Exception("Uncorrect result matrix\n")
def run(): Seq = jacobi_sencil(5, 5, False) Par = jacobi_sencil(5, 5, True) if not dnumpytest.array_equal(Seq, Par): raise Exception("Uncorrect result matrix\n")
def run(): Seq = jacobi_sencil(5,5,False) Par = jacobi_sencil(5,5,True) if not dnumpytest.array_equal(Seq,Par): raise Exception("Uncorrect result matrix\n")
def run(): Seq = SOR(10, 10, False) Par = SOR(10, 10, True) if not dnumpytest.array_equal(Seq, Par): raise Exception("Uncorrect result matrix\n")