 | 1 | initial version |
I have an algorithm that I need to speed up. I would like to parallelize computations
the main part I need to speed up is the following
for r2 in Subsets(V2,k-4): check2(fuse(v,r2),k)
this gives as output three values b1, b2, b3 that are global and for any input check2 can either leave them unchanged or increase them by 1. I tried looking into @parallel but it doens't seem to fit my requirements.
How can I parallelize that (I can use up to 16 cores)?
I have an algorithm that I need to speed up. I would like to parallelize computations
the main part I need to speed up is the following
for r2 in Subsets(V2,k-4): check2(fuse(v,r2),k)
this gives as output three values b1, b2, b3 that are global and for any input check2 can either leave them unchanged or increase them by 1. I tried looking into @parallel but it doens't seem to fit my requirements.
How can I parallelize that (I can use up to 16 cores)?
This is the whole code:
I have an algorithm that I need to speed up. I would like to parallelize computations
the main part I need to speed up is the following
for r2 in Subsets(V2,k-4): check2(fuse(v,r2),k)
this gives as output three values b1, b2, b3 that are global and for any input check2 can either leave them unchanged or increase them by 1. I tried looking into @parallel but it doens't seem to fit my requirements.
How can I parallelize that (I can use up to 16 cores)?
This is the whole code:code (minus all the parts that do, I can't publish it all):
P2=[]
def P_2(q,n,S): %creates the set P2
I2R=[]
I2S=[]
I3=[]
I3R=[]
I6=[]
I8=[]
def IG(k): %creates a set of indeces vectors
def coll(x,y,z): %checks if 3 given tuples are collinear
A=[x,y,z]
M = matrix(A)
if M.determinant()==0:
return 'true'
def cone(v): %defines a function to be used in conic()
def conic(x1,x2,x3,x4,x5,x6): %checks if 6 given tuples are on a conic
A=[cone(x1),cone(x2),cone(x3),cone(x4),cone(x5),cone(x6)]
M = matrix(A)
if M.determinant()==0:
return 'true'
def cube(v): %defines a function to be used in SIcubic()
def dxcube(v): %defines a function to be used in SIcubic()
def dycube(v): %defines a function to be used in SIcubic()
def dzcube(v): %defines a function to be used in SIcubic()
def SIcubic(x1,x2,x3,x4,x5,x6,x7,x8): %checks if 8 given tuples are on a special cubic
v1='false'
v=[x1,x2,x3,x4,x5,x6,x7,x8]
for a in range(8):
x=v[a]
A=[]
A.append(dxcube(x))
A.append(dycube(x))
A.append(dzcube(x))
for t in range(8):
if t!=a:
y=v[t]
A.append(cube(y))
M = matrix(A)
if M.determinant()==0:
v1='true'
return v1
b1=0
b2=0
b3=0
def check(s,k): %checks a vector s of lenght k
global b1
global b2
global b3
IG(k)
q1=0
q2=0
q3=0
for i3 in I3:
a0=i3[0]
a1=i3[1]
a2=i3[2]
if coll(s[a0],s[a1],s[a2])=='true':
q1=1
q2=1
q3=1
break
if q2==0:
if k>5:
if q==0:
for i6 in I6:
a0=i6[0]
a1=i6[1]
a2=i6[2]
a3=i6[3]
a4=i6[4]
a5=i6[5]
if conic(s[a0],s[a1],s[a2],s[a3],s[a4],s[a5])=='true':
q2=1
q3=1
break
if q3==0:
if k>7:
for i8 in I8:
a0=i8[0]
a1=i8[1]
a2=i8[2]
a3=i8[3]
a4=i8[4]
a5=i8[5]
a6=i8[6]
a7=i8[7]
if SIcubic(s[a0],s[a1],s[a2],s[a3],s[a4],s[a5],s[a6],s[a7])=='true':
q3=1
break
b1=b1+q1
b2=b2+q2
b3=b3+q3
return b1
return b2
return b3
b1=0
def check1(s,a): %checks a vector s and an element a
global b1
q1=0
for i2 in I2S:
a0=i2[0]
a1=i2[1]
if coll(s[a0],s[a1],a)=='true':
q1=1
break
b1=b1+q1
return b1
b1=0
b2=0
b3=0
def check2(s,k): %checks a vector s of lenght k
global b1
global b2
global b3
q1=0
q2=0
q3=0
for i3 in I3R:
a0=i3[0]
a1=i3[1]
a2=i3[2]
if coll(s[a0],s[a1],s[a2])=='true':
q1=1
q2=1
q3=1
break
if q2==0:
if k>5:
for i6 in I6:
a0=i6[0]
a1=i6[1]
a2=i6[2]
a3=i6[3]
a4=i6[4]
a5=i6[5]
if conic(s[a0],s[a1],s[a2],s[a3],s[a4],s[a5])=='true':
q2=1
q3=1
break
if q3==0:
if k>7:
for i8 in I8:
a0=i8[0]
a1=i8[1]
a2=i8[2]
a3=i8[3]
a4=i8[4]
a5=i8[5]
a6=i8[6]
a7=i8[7]
if SIcubic(s[a0],s[a1],s[a2],s[a3],s[a4],s[a5],s[a6],s[a7])=='true':
q3=1
break
b1=b1+q1
b2=b2+q2
b3=b3+q3
return b1
return b2
return b3
t=1
def TOT(P,q,n,k): %computes a polynomial depending on q,n,k,P
def fuse(s,t): %fuses two vectors into 1 (see repsonse to answere)
sn=[]
def Count(S,q,n,k): %main function
IG(k)
global b1
global b2
global b3
l1=0
l2=0
l3=0
y=q^n
s=len(S)
v=[(0,0,1),(0,1,0),(1,0,0),(1,1,1)]
V1=copy(S)
V1.remove((1,0,0))
V1.remove((0,1,0))
V1.remove((0,0,1))
V1.remove((1,1,1))
if k<4:
b1=0
for s in Subsets(S,k):
check(s,k)
l1=b1*factorial(k)
if k>4:
V2=copy(V1)
for r1 in V1:
b1=0
b2=0
b3=0
check1(v,r1)
if b1==1:
V2.remove(r1)
s2=len(V2)
l1=l1+binomial(s-4,k-4)-binomial(s2,k-4)
l2=l2+binomial(s-4,k-4)-binomial(s2,k-4)
l3=l3+binomial(s-4,k-4)-binomial(s2,k-4)
b1=0
b2=0
b3=0
for r2 in Subsets(V2,k-4):
check2(fuse(v,r2),k)
l1=l1+b1
l2=l2+b2
l3=l3+b3
l1=l1*(y^2+y+1)*(y^3-y)*(y^3-y^2)*factorial(k-4)
l2=l2*(y^2+y+1)*(y^3-y)*(y^3-y^2)*factorial(k-4)
l3=l3*(y^2+y+1)*(y^3-y)*(y^3-y^2)*factorial(k-4)
g1=t-l1
g2=t-l2
g3=t-l3
print('There are ', g1/factorial(k) , ' unordered', k,'-tuples in general linear position')
print('There are ', g1 , ' ordered', k ,'-tuples in general linear position')
print('There are ', g2/factorial(k) , ' unordered', k,'-tuples in general linear and conic position')
print('There are ', g2 , ' ordered', k ,'-tuples in general linear and conic position')
print('There are ', g3/factorial(k) , ' unordered', k,'-tuples in general position')
print('There are ', g3 , ' ordered', k ,'-tuples in general position')
def Active(q,n,k): %every fucntion I need to run reduced into 1
P2=[]
P_2(q,n,P2)
TOT(P2,q,n,k)
Count(P2,q,n,k)
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