def check_dot(gatype):
if 0 == me:
print '> Checking dot ...',
np.random.seed(12345) # everyone has same seed
g_a = create_global_array(gatype)
g_b = create_global_array(gatype)
a = create_local_a(gatype)
b = np.random.random_sample((n,n))
if MIRROR:
if 0 == iproc:
ga.put(g_b, b)
ga.put(g_a, a)
else:
if 0 == me:
ga.put(g_b, b)
ga.put(g_a, a)
ga.sync()
sum1 = np.sum(a*b)
sum2 = ga.dot(g_a, g_b)
if mismatch(sum1, sum2):
ga.error('dot wrong %s != %s' % (sum1, sum2))
if 0 == me:
print 'OK'
ga.destroy(g_a)
ga.destroy(g_b)
def convergence_test_L2(g_a, g_b):
# compute L2 norm of change
# subtract g_b from g_a, results stored in g_b
ga.add(g_a, g_b, g_b, beta=-1)
# compute elementwise dot product (i.e. treats N-d arrays as vectors)
value = ga.dot(g_b, g_b)
if DEBUG:
print_sync(value)
return value < EPSILON
def verify(g_a, g_b, g_c):
g_chk = ga.duplicate(g_a, "array check")
if not g_chk: ga.error("duplicate failed")
ga.sync()
ga.gemm(False, False, TOTALELEMS, TOTALELEMS, TOTALELEMS, 1.0, g_a, g_b,
0.0, g_chk);
ga.sync()
ga.add(g_c, g_chk, g_chk, 1.0, -1.0)
rchk = ga.dot(g_chk, g_chk)
if not me:
print "Normed difference in matrices: %12.4f" % rchk
if not (-TOLERANCE < rchk < TOLERANCE):
ga.error("Matrix multiply verify failed")
else:
print "Matrix Multiply OK"
ga.destroy(g_chk)
