def MC_int(c, s, dist):
x = np.empty([s], dtype=np.double, dist=dist)
y = np.empty([s], dtype=np.double, dist=dist)
sum = 0.0
np.timer_reset()
np.evalflush()
start = time.time()
for i in range(c):
np.ufunc_random(x, x)
np.ufunc_random(y, y)
np.square(x, x)
np.square(y, y)
np.add(x, y, x)
z = np.less_equal(x, 1)
sum += np.add.reduce(z) * 4.0 / s
sum = sum / c
np.evalflush()
stop = time.time()
print 'Pi: ', sum, ' with ', s, ' samples in sec: ', stop - start,
if dist:
print "(Dist) notes: %s" % sys.argv[4]
else:
print "(Non-Dist) notes: %s" % sys.argv[4]
def MC_int(c, s, dist):
x = np.empty([s], dtype=np.double, dist=dist)
y = np.empty([s], dtype=np.double, dist=dist)
sum=0.0
np.timer_reset()
np.evalflush()
start=time.time()
for i in range(c):
np.ufunc_random(x,x)
np.ufunc_random(y,y)
np.square(x,x)
np.square(y,y)
np.add(x,y,x)
z = np.less_equal(x, 1)
sum += np.add.reduce(z)*4.0/s
sum = sum / c
np.evalflush()
stop=time.time()
print 'Pi: ', sum, ' with ', s,' samples in sec: ', stop-start,
if dist:
print "(Dist) notes: %s"%sys.argv[4]
else:
print "(Non-Dist) notes: %s"%sys.argv[4]
def gen_matrix(SIZE, dist):
A = np.zeros((SIZE, SIZE), dtype=float, dist=dist)
np.ufunc_random(A, A)
for i in xrange(SIZE):
A[i, i] *= 2**30
return A
work += right
work += down
work *= 0.2
parser = util.Parsing()
DIST = parser.dist
nonaligned = bool(eval(parser.argv[0]))
N = int(parser.argv[1])
iters = int(parser.argv[2])
BS = 1
full = np.empty((N + BS * 2, N + BS * 2), dtype=np.double, dist=DIST)
align = np.empty((N, N), dtype=np.double, dist=DIST)
work = np.zeros((N, N), dtype=np.double, dist=DIST)
np.ufunc_random(full, full)
np.ufunc_random(align, align)
center = full[1 * BS:-1 * BS, 1 * BS:-1 * BS]
up = full[1 * BS:-1 * BS, :-2 * BS]
left = full[:-2 * BS, 1 * BS:-1 * BS]
right = full[2 * BS:, 1 * BS:-1 * BS]
down = full[1 * BS:-1 * BS, 2 * BS:]
np.timer_reset()
if nonaligned:
stencil(iters, work, center, up, left, right, down)
else:
stencil(iters, work, align, align, align, align, align)
timing = np.timer_getdict()
np.subtract(B, tmp1, tmp1)
np.divide(tmp1, tmp2, tmp1)
hnew = h + tmp1
np.subtract(hnew,h,tmp2)
np.divide(tmp2,h,tmp1)
np.absolute(tmp1,tmp1)
dmax = np.maximum.reduce(tmp1)
h = hnew
timing = np.timer_getdict()
print timing
print 'Iter: ', n, ' size:', np.shape(A)
parser.pprint(timing)
return h
size = int(parser.argv[0])
iter = int(parser.argv[1])
#A = array([[4, -1, -1, 0], [-1, 4, 0, -1], [-1, 0, 4, -1], [0, -1, -1, 4]], float, dist=d)
#B = array([1,2,0,1], float, dist=d)
A = np.zeros([size,size], dtype=float, dist=parser.dist)
np.ufunc_random(A,A)
B = np.zeros([size], dtype=float, dist=parser.dist)
np.ufunc_random(B,B)
C = jacobi(A, B, forcedIter=iter)
if jp != j: #Do full pivot!!!
temp[:] = A[jp, :]
A[jp, :] = A[j, :]
A[j, :] = temp[:]
if j < M - 1:
recp = 1.0 / A[j][j]
A[j:, j] *= recp
if (j < minMN - 1):
t1 = A[j + 1:, j + 1:] - A[j + 1:, j] * A[j, j + 1:]
A[j + 1:, j + 1:] = t1
return A, pivot
A = np.ufunc_random(np.empty((N, N), dtype=float, dist=DIST))
np.timer_reset()
np.evalflush()
t1 = time.time()
LU_factor(A)
np.evalflush()
t1 = time.time() - t1
print 'Iter: ', I, ' size: ', N, ' time: ', t1,
if A.dist():
print "(Dist) notes: %s" % sys.argv[4]
else:
print "(Non-Dist) notes: %s" % sys.argv[4]
import numpy as np import sys import time d = int(sys.argv[1]) # Distributed n = int(sys.argv[2]) # Number of bodies k = int(sys.argv[3]) # Number of iterations G = 1 # Gravitational constant dT = 0.01 # Time increment M = np.empty((n, 1), dist=d) np.ufunc_random(M, M) MT = np.empty((1, n), dist=d) np.ufunc_random(MT, MT) Px = np.empty((n, 1), dist=d) np.ufunc_random(Px, Px) Py = np.empty((n, 1), dist=d) np.ufunc_random(Py, Py) Pz = np.empty((n, 1), dist=d) np.ufunc_random(Pz, Pz) PxT = np.empty((1, n), dist=d) np.ufunc_random(PxT, PxT) PyT = np.empty((1, n), dist=d) np.ufunc_random(PyT, PyT) PzT = np.empty((1, n), dist=d) np.ufunc_random(PzT, PzT) Vx = np.empty((n, 1), dist=d) np.ufunc_random(Vx, Vx) Vy = np.empty((n, 1), dist=d) np.ufunc_random(Vy, Vy)
import numpy as np
import util
import pyHPC
parser = util.Parsing()
DIST = parser.dist
M = int(parser.argv[0])
N = int(parser.argv[1])
K = int(parser.argv[2])
C = int(parser.argv[3])
A = np.empty([M, K], dtype=float, dist=DIST)
np.ufunc_random(A, A)
B = np.empty([K, M], dtype=float, dist=DIST)
np.ufunc_random(B, B)
if DIST:
matmul = pyHPC.summa
else:
matmul = np.dot
np.timer_reset()
for i in range(C):
tmp = matmul(A, B)
timing = np.timer_getdict()
if np.RANK == 0:
print "SUMMA M:%d, N:%d, K:%d, C:%d" % (M, N, K, C)
parser.pprint(timing)
parser.write_dict(timing)
t = base - target
#print t.shape, tmp1.shape
np.subtract(base, target, tmp1)
tmp1 **= 2
np.add.reduce(tmp1, out=tmp2)
np.sqrt(tmp2, tmp2)
r = np.max(tmp2, axis=0)
return r
DIST = int(sys.argv[1])
ndims = int(sys.argv[2])
db_length = int(sys.argv[3])
step = int(sys.argv[4])
targets = np.ufunc_random(np.empty((ndims, db_length), dtype=float, dist=DIST))
base = targets #np.ufunc_random(np.empty((ndims,db_length), dtype=float, dist=DIST))
tmp1 = np.empty((ndims, step, step), dtype=float, dist=DIST)
tmp2 = np.empty((step, step), dtype=float, dist=DIST)
np.timer_reset()
np.evalflush()
t1 = time.time()
for i in xrange(0, db_length, step):
for j in xrange(0, db_length, step):
compute_targets(base[:, i:i + step], targets[:, j:j + step], tmp1,
tmp2)
np.evalflush()
t2 = time.time()
target = target[:,:,np.newaxis]
t = base - target
#print t.shape, tmp1.shape
np.subtract(base, target, tmp1)
tmp1 **= 2
np.add.reduce(tmp1, out=tmp2)
np.sqrt(tmp2, tmp2)
r = np.max(tmp2, axis=0)
return r
DIST = int(sys.argv[1])
ndims = int(sys.argv[2])
db_length = int(sys.argv[3])
step = int(sys.argv[4])
targets = np.ufunc_random(np.empty((ndims,db_length), dtype=float, dist=DIST))
base = targets #np.ufunc_random(np.empty((ndims,db_length), dtype=float, dist=DIST))
tmp1 = np.empty((ndims,step,step), dtype=float, dist=DIST)
tmp2 = np.empty((step,step), dtype=float, dist=DIST)
np.timer_reset()
np.evalflush()
t1 = time.time()
for i in xrange(0, db_length, step):
for j in xrange(0, db_length, step):
compute_targets(base[:,i:i+step], targets[:,j:j+step], tmp1, tmp2)
np.evalflush()
t2 = time.time()
print "ndims: %d, dbsize: %d, step: %d - time: %f sec."\
import numpy as np import sys import time d = int(sys.argv[1]) #Distributed n = int(sys.argv[2]) #Number of bodies k = int(sys.argv[3]) #Number of iterations G = 1 #Gravitational constant dT = 0.01 #Time increment M = np.empty((n, 1), dist=d) np.ufunc_random(M, M) MT = np.empty((1, n), dist=d) np.ufunc_random(MT, MT) Px = np.empty((n, 1), dist=d) np.ufunc_random(Px, Px) Py = np.empty((n, 1), dist=d) np.ufunc_random(Py, Py) Pz = np.empty((n, 1), dist=d) np.ufunc_random(Pz, Pz) PxT = np.empty((1, n), dist=d) np.ufunc_random(PxT, PxT) PyT = np.empty((1, n), dist=d) np.ufunc_random(PyT, PyT) PzT = np.empty((1, n), dist=d) np.ufunc_random(PzT, PzT) Vx = np.empty((n, 1), dist=d) np.ufunc_random(Vx, Vx) Vy = np.empty((n, 1), dist=d) np.ufunc_random(Vy, Vy)
# Black Sholes Function
def BlackScholes(CallPutFlag,S,X,T,r,v):
d1 = (np.log(S/X)+(r+v*v/2.)*T)/(v*np.sqrt(T))
d2 = d1-v*np.sqrt(T)
if CallPutFlag=='c':
return S*CND(d1)-X*np.exp(-r*T)*CND(d2)
else:
return X*np.exp(-r*T)*CND(-d2)-S*CND(-d1)
DIST=int(sys.argv[1])
N=int(sys.argv[2])
S = np.empty((N), dtype=float, dist=DIST)
np.ufunc_random(S,S)
S = S*4.0-2.0 + 60.0 #Price is 58-62
X=65.0
#T=0.25 Moved to loop
r=0.08
v=0.3
year=int(sys.argv[3])
day=1.0/year
T=day
np.timer_reset()
np.evalflush()
stime = time.time()
for t in xrange(year):
work += left
work += right
work += down
work *= 0.2
parser = util.Parsing()
DIST=parser.dist
nonaligned = bool(eval(parser.argv[0]))
N = int(parser.argv[1])
iters=int(parser.argv[2])
BS=1
full = np.empty((N+BS*2,N+BS*2), dtype=np.double, dist=DIST)
align = np.empty((N,N), dtype=np.double, dist=DIST)
work = np.zeros((N,N), dtype=np.double, dist=DIST)
np.ufunc_random(full,full)
np.ufunc_random(align,align)
center = full[1*BS:-1*BS, 1*BS:-1*BS]
up = full[1*BS:-1*BS, :-2*BS]
left = full[:-2*BS , 1*BS:-1*BS]
right = full[2*BS: , 1*BS:-1*BS]
down = full[1*BS:-1*BS, 2*BS: ]
np.timer_reset()
if nonaligned:
stencil(iters, work, center, up, left, right, down)
else:
stencil(iters, work, align, align, align, align, align)
timing = np.timer_getdict()
if jp != j: #Do full pivot!!!
temp[:]=A[jp,:]
A[jp,:]=A[j,:]
A[j,:]=temp[:]
if j < M-1:
recp = 1.0 / A[j][j]
A[j:,j] *= recp
if (j < minMN-1):
t1 = A[j+1:,j+1:] - A[j+1:,j] * A[j,j+1:]
A[j+1:,j+1:] = t1
return A, pivot
A = np.ufunc_random(np.empty((N,N), dtype=float, dist=DIST))
np.timer_reset()
np.evalflush()
t1 = time.time()
LU_factor(A)
np.evalflush()
t1 = time.time() - t1
print 'Iter: ', I, ' size: ', N,' time: ', t1,
if A.dist():
print "(Dist) notes: %s"%sys.argv[4]
else:
print "(Non-Dist) notes: %s"%sys.argv[4]
def gen_matrix(SIZE,dist):
A = np.zeros((SIZE,SIZE), dtype=float, dist=dist);
np.ufunc_random(A,A)
for i in xrange(SIZE):
A[i,i] *= 2**30
return A
