def bench_file(filename):
"""
Perform comparative benchmark on matrices loaded from Matrix Market files.
:param filename: a Matrix Market file
"""
bs = dict()
for dtype in WANT_DTYPES:
print("# loading matrix from file ", filename)
lt = - rsb.rsb_time()
a = rsb.rsb_matrix(filename,dtype=dtype)
lt = lt + rsb.rsb_time()
printf("# loaded a matrix with %.1e nnz in %.1e s (%.1e nnz/s)\n",a.nnz,lt,a.nnz/lt)
printf("# loaded as type %s/%c (default is %s/%c)\n", a.dtype, DT2TC[a.dtype], rsb.rsb_dtype, DT2TC[rsb.rsb_dtype])
if not a._is_unsymmetric():
if WANT_SYMMETRIZE:
print("# NOTE: loaded RSB matrix is NOT unsymmetric: expanding symmetry to cope with csr.")
(I, J, V) = a.find()
a = rsb.rsb_matrix((np.append(V,V), (np.append(I,J), np.append(J,I))), a.shape, dtype=dtype)
else:
print("# NOTE: loaded RSB matrix is NOT unsymmetric, but scipy will only perform unsymmetric SpMM")
if a is not None:
(I, J, V) = a.find()
c = sp.sparse.csr_matrix((V, (I, J)))
( mtxname, _ ) = os.path.splitext(os.path.basename(filename))
( mtxname, _ ) = os.path.splitext(mtxname)
bd = bench_matrix(a, c, mtxname)
dict_stat_merge(bs, derived_bench_stats(bd))
bsc = bs.copy()
dict_sum_average_all(bsc)
print_perf_record(bsc,beg="pyrsb:speedups:",fields=True)
return bs
def bench_random_files():
for nrA in want_nrA:
ncA = nrA
dnst = (math.sqrt(1.0 * nrA)) / nrA
# print("# generating ",nrA,"x",ncA," with density ",dnst)
printf("# generating %d x %d with with density %.1e\n", nrA, ncA, dnst)
c = sp.sparse.rand(nrA,
ncA,
density=dnst,
format=want_psf,
dtype=sp.double)
(I, J, V) = sp.sparse.find(c)
a = rsb.rsb_matrix((V, (I, J)), [nrA, ncA])
bench_matrix(a, c)
def bench_random_files():
"""
Perform comparative benchmark on randomly generated matrices.
"""
for nrA in WANT_NRA:
ncA = nrA
dnst = (math.sqrt(1.0 * nrA)) / nrA
# print("# generating ",nrA,"x",ncA," with density ",dnst)
printf("# generating %d x %d with with density %.1e\n", nrA, ncA, dnst)
gt = -rsb.rsb_time()
c = sp.sparse.rand(nrA, ncA, density=dnst, format=WANT_PSF, dtype=sp.double)
gt = gt + rsb.rsb_time()
(I, J, V) = sp.sparse.find(c)
ct = -rsb.rsb_time()
a = rsb.rsb_matrix((V, (I, J)), [nrA, ncA])
ct = ct + rsb.rsb_time()
printf("# generated a matrix with %.1e nnz in %.1e s (%.1e nnz/s), converted to RSB in %.1e s\n",a.nnz(),gt,a.nnz()/gt,ct)
bench_matrix(a, c)
def bench_random_matrices():
"""
Perform comparative benchmark on randomly generated matrices.
"""
for dtype in WANT_DTYPES:
for nrA in WANT_NRA:
ncA = nrA
dnst = (math.sqrt(1.0 * nrA)) / nrA
# print("# generating ",nrA,"x",ncA," with density ",dnst)
printf("# generating %d x %d with with density %.1e\n", nrA, ncA, dnst)
gt = -rsb.rsb_time()
c = sp.sparse.rand(nrA, ncA, density=dnst, format=WANT_PSF, dtype=rsb.rsb_dtype)
gt = gt + rsb.rsb_time()
(I, J, V) = sp.sparse.find(c)
V = dtype(V)
c = sp.sparse.csr_matrix((V, (I, J)), [nrA, ncA])
ct = -rsb.rsb_time()
a = rsb.rsb_matrix((V, (I, J)), [nrA, ncA], dtype=dtype)
ct = ct + rsb.rsb_time()
printf("# generated a matrix with %.1e nnz in %.1e s (%.1e nnz/s), converted to RSB in %.1e s\n",a.nnz,gt,a.nnz/gt,ct)
bd = bench_matrix(a, c, "random")
derived_bench_stats(bd)
def test_demo():
import numpy
import scipy
from scipy.sparse import csr_matrix
from rsb import rsb_matrix
V = [11.0, 12.0, 22.0]
I = [0, 0, 1]
J = [0, 1, 1]
c = csr_matrix((V, (I, J)))
print(c)
# several constructor forms, as with csr_matrix:
a = rsb_matrix((V, (I, J)))
a = rsb_matrix((V, (I, J)), [3, 3])
a = rsb_matrix((V, I, J))
a = rsb_matrix((V, I, J), sym="S") # symmetric example
print(a)
a = rsb_matrix((4, 4))
a = rsb_matrix(c)
nrhs = 1 # set to nrhs>1 to multiply by multiple vectors at once
nr = a.shape[0]
nc = a.shape[1]
order = "F"
x = numpy.empty([nc, nrhs], dtype=scipy.double, order=order)
y = numpy.empty([nr, nrhs], dtype=scipy.double, order=order)
x[:, :] = 1.0
y[:, :] = 0.0
print(a)
print(x)
print(y)
# import rsb # import operators
# a.autotune() # makes only sense for large matrices
y = y + a * x
# equivalent to y=y+c*x
print(y)
del a
def test_init_tuple():
V = [11.0, 12.0, 22.0]
I = [0, 0, 1]
J = [0, 1, 1]
mat = rsb_matrix((V, I, J))
assert mat.shape == (2, 2)
import numpy import scipy from scipy.sparse import csr_matrix from rsb import rsb_matrix V = [11., 12., 22.] I = [0, 0, 1] J = [0, 1, 1] c = csr_matrix((V, (I, J))) print(c) # several constructor forms, as with csr_matrix: a = rsb_matrix((V, (I, J))) a = rsb_matrix((V, (I, J)), [3, 3]) a = rsb_matrix((V, I, J)) a = rsb_matrix((V, I, J), sym='S') # symmetric example print(a) a = rsb_matrix((4, 4)) a = rsb_matrix(c) nrhs = 1 # set to nrhs>1 to multiply by multiple vectors at once nr = a.shape[0] nc = a.shape[1] x = numpy.empty([nc, nrhs], dtype=scipy.double) y = numpy.empty([nr, nrhs], dtype=scipy.double) x[:, :] = 1.0 y[:, :] = 0.0 print(a) print(x) print(y) #import rsb # import operators # a.autotune() # makes only sense for large matrices y = y + a * x # equivalent to y=y+c*x
#A, HF_TRANS = derv.computeFourierDerivativeMatrix(DIMS)
#A = derv.computeCompactFiniteDiffDerivativeMatrix1(DIMS, REFS[0])
#A = derv.computeCubicSplineDerivativeMatrix(DIMS, REFS[0], True)
# Make a dense teste vector (MUST be NX1 for Octave to work)
ACSR = sps.csr_matrix(np.real(A))
V = np.expand_dims(REFS[0], 1) * np.ones((NX + 1, 1))
# Test native dot product
start = time.time()
R1 = A.dot(V)
end = time.time()
print('Native numpy MV: ', end - start, ' sec')
# Test native rsb dot product
AR = rsb_matrix(ACSR)
#AR.autotune()
start = time.time()
R2 = AR.dot(V)
end = time.time()
print('PyRSB SpMV: ', end - start, ' sec')
# Test SpMV serial implementation
start = time.time()
R3 = computeMatVecDotParfor(ACSR, V, False)
end = time.time()
print('Serial SpMV: ', end - start, ' sec')
# Test SpMV parallel implementation
start = time.time()
R4 = computeMatVecDotParfor(ACSR, V, True)
print ("# bench timeout:", WANT_TIMEOUT )
print ("# operands alloc:", not WANT_ZERO_ALLOC)
print ("# want RSB and scipy.sparse:", WANT_BOTH)
print ("# render:", WANT_RENDER)
print ("# symmetrize:", WANT_SYMMETRIZE )
if len(args) > 0:
bs = dict()
if len(args):
printf("# will load matrix files:")
for arg in args[0:]:
printf(" %s", arg)
printf("\n")
elt0 = elapsed_time()
if True:
# warm up threads
u = rsb.rsb_matrix(np.ones(shape=(1000,1000)))
u.autotune(verbose=False,nrhs=1,tmax=0.0001)
del u
for arg in args[0:]:
bd = bench_file(arg)
dict_stat_merge(bs, bd)
elt1 = elapsed_time()
printf("# time elapsed: %.1f s\n", elt1-elt0)
dict_sum_average_all(bs)
print_perf_record(bs,beg="pyrsb:speedups:",fields=True)
else:
# bench_file("venkat50.mtx.gz")
bench_random_matrices()
# a.save("file.mtx")
rsb.rsb_lib_exit()
