class LLMatMatVecBenchmark_4(LLMatMatVecBenchmark):
label = "matvec with 5000 elements and size = 1,000,000"
each = 100
def setUp(self):
self.nbr_elements = 5000
self.size = 1000000
self.A_c = LLSparseMatrix(size=self.size, size_hint=self.nbr_elements, itype=INT32_T, dtype=FLOAT64_T)
self.A_p = spmatrix.ll_mat(self.size, self.size, self.nbr_elements)
self.A_s = lil_matrix((self.size, self.size), dtype=np.float64)
self.list_of_matrices = []
self.list_of_matrices.append(self.A_c)
self.list_of_matrices.append(self.A_p)
self.list_of_matrices.append(self.A_s)
construct_random_matrices(self.list_of_matrices, self.size, self.nbr_elements)
self.CSR_c = self.A_c.to_csr()
self.CSR_p = self.A_p.to_csr()
self.CSR_s = self.A_s.tocsr()
self.v = np.arange(0, self.size, dtype=np.float64)
class LLMatToCSRBenchmark(benchmark.Benchmark):
label = "to_csr() with 100 elements and size = 1,000"
each = 100
def setUp(self):
self.nbr_elements = 100
self.size = 1000
self.A_c = LLSparseMatrix(size=self.size, size_hint=self.nbr_elements, dtype=FLOAT64_T)
construct_sparse_matrix(self.A_c, self.size, self.nbr_elements)
self.A_p = spmatrix.ll_mat(self.size, self.size, self.nbr_elements)
construct_sparse_matrix(self.A_p, self.size, self.nbr_elements)
#def tearDown(self):
# for i in xrange(self.size):
# for j in xrange(self.size):
# assert self.csr_c[i, j] == self.A_c[i, j]
def test_pysparse(self):
self.csr_p = self.A_p.to_csr()
return
def test_cysparse(self):
self.csr_c = self.A_c.to_csr()
return
class LLMatMatVecBenchmark(benchmark.Benchmark):
label = "CSR * CSC * v with 1000 elements and size = 10,000"
each = 100
def setUp(self):
self.nbr_elements = 1000
self.size = 10000
self.A_c = LLSparseMatrix(size=self.size,
size_hint=self.nbr_elements,
itype=INT32_T,
dtype=FLOAT64_T)
self.A_s = lil_matrix((self.size, self.size), dtype=np.float64)
self.list_of_matrices = []
self.list_of_matrices.append(self.A_c)
self.list_of_matrices.append(self.A_s)
construct_random_matrices(self.list_of_matrices, self.size,
self.nbr_elements)
self.CSR_c = self.A_c.to_csr()
self.CSR_s = self.A_s.tocsr()
self.B_c = LLSparseMatrix(size=self.size,
size_hint=self.nbr_elements,
itype=INT32_T,
dtype=FLOAT64_T)
self.B_s = lil_matrix((self.size, self.size), dtype=np.float64)
self.list_of_matrices = []
self.list_of_matrices.append(self.B_c)
self.list_of_matrices.append(self.B_s)
construct_random_matrices(self.list_of_matrices, self.size,
self.nbr_elements)
self.CSC_c = self.B_c.to_csc()
self.CSC_s = self.B_s.tocsc()
self.v = np.arange(0, self.size, dtype=np.float64)
#def tearDown(self):
# for i in xrange(self.size):
# assert self.w_c[i] == self.w_p[i]
# assert self.w_c[i] == self.w_s[i]
def test_cysparse(self):
self.w_c = self.CSR_c * self.CSC_c * self.v
return
def test_scipy_sparse(self):
self.w_s = self.CSR_s * self.CSC_s * self.v
return
class LLMatMatVecBenchmark(benchmark.Benchmark):
label = "matvec with 1000 elements and size = 10,000"
each = 100
def setUp(self):
self.nbr_elements = 1000
self.size = 10000
self.A_c = LLSparseMatrix(size=self.size, size_hint=self.nbr_elements, itype=INT32_T, dtype=FLOAT64_T)
self.A_p = spmatrix.ll_mat(self.size, self.size, self.nbr_elements)
self.A_s = lil_matrix((self.size, self.size), dtype=np.float64)
self.list_of_matrices = []
self.list_of_matrices.append(self.A_c)
self.list_of_matrices.append(self.A_p)
self.list_of_matrices.append(self.A_s)
construct_random_matrices(self.list_of_matrices, self.size, self.nbr_elements)
self.CSR_c = self.A_c.to_csr()
self.CSR_p = self.A_p.to_csr()
self.CSR_s = self.A_s.tocsr()
self.v = np.arange(0, self.size, dtype=np.float64)
#def tearDown(self):
# for i in xrange(self.size):
# assert self.w_c[i] == self.w_p[i]
# assert self.w_c[i] == self.w_s[i]
def test_pysparse(self):
self.w_p = np.empty(self.size, dtype=np.float64)
self.CSR_p.matvec(self.v, self.w_p)
return
def test_cysparse(self):
self.w_c = self.CSR_c * self.v
return
def test_cysparse2(self):
self.w_c2 = self.CSR_c.matvec(self.v)
return
def test_scipy_sparse(self):
self.w_s = self.CSR_s * self.v
return
def test_scipy_sparse2(self):
self.w_s2 = self.CSR_s._mul_vector(self.v)