Added some optimizations

This commit is contained in:
vub 2017-04-23 08:10:21 -04:00
parent 9cf2a9f889
commit 3397bd982e
4 changed files with 217 additions and 28 deletions

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@ -36,19 +36,21 @@ int eval_poly_at(vector<int> poly, int x) {
}
vector<int> lagrange_interp(vector<int> ys, vector<int> xs) {
deque<int> root;
root.push_back(1);
for (int i = 0; i < xs.size(); i++) {
int xs_size = xs.size();
vector<int> root(xs_size + 1);
root[xs_size] = 1;
for (int i = 0; i < xs_size; i++) {
int logx = glogtable[xs[i]];
root.push_front(0);
for (int j = 0; j < root.size() - 1; j++) {
if (root[j + 1] and xs[i])
root[j] ^= gexptable[glogtable[root[j+1]] + logx];
int offset = xs_size - i - 1;
root[offset] = 0;
for (int j = 0; j < i + 1; j++) {
if (root[j + 1 + offset] and xs[i])
root[j + offset] ^= gexptable[glogtable[root[j+1 + offset]] + logx];
}
}
vector<vector<int> > nums;
for (int i = 0; i < xs.size(); i++) {
vector<int> b(xs_size);
vector<int> output(root.size() - 1);
for (int i = 0; i < xs_size; i++) {
output[root.size() - 2] = 1;
int logx = glogtable[xs[i]];
for (int j = root.size() - 2; j > 0; j--) {
@ -57,18 +59,11 @@ vector<int> lagrange_interp(vector<int> ys, vector<int> xs) {
else
output[j - 1] = root[j];
}
nums.push_back(output);
}
vector<int> denoms;
for (int i = 0; i < xs.size(); i++) {
denoms.push_back(eval_poly_at(nums[i], xs[i]));
}
vector<int> b(xs.size());
for (int i = 0; i < xs.size(); i++) {
int log_yslice = glogtable[ys[i]] - glogtable[denoms[i]] + 65535;
for (int j = 0; j < xs.size(); j++) {
if(nums[i][j] and ys[i])
b[j] ^= gexptable[glogtable[nums[i][j]] + log_yslice];
int denom = eval_poly_at(output, xs[i]);
int log_yslice = glogtable[ys[i]] - glogtable[denom] + 65535;
for (int j = 0; j < xs_size; j++) {
if(output[j] and ys[i])
b[j] ^= gexptable[glogtable[output[j]] + log_yslice];
}
}
return b;

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@ -70,8 +70,9 @@ def lagrange_interp(pieces, xs):
for j in range(len(root)-1):
if root[j+1] and x:
root[j] ^= gexptable[glogtable[root[j+1]] + logx]
#print(root)
assert len(root) == len(pieces) + 1
print(root)
# print(root)
# Generate per-value numerator polynomials, eg. for x=x2,
# (x - x1) * (x - x3) * ... * (x - xn), by dividing the master
# polynomial back by each x coordinate
@ -86,7 +87,7 @@ def lagrange_interp(pieces, xs):
output[j-1] = root[j]
assert len(output) == len(pieces)
nums.append(output)
print(nums)
#print(nums)
# Generate denominators by evaluating numerator polys at each x
denoms = [eval_poly_at(nums[i], xs[i]) for i in range(len(xs))]
# Generate output polynomial, which is the sum of the per-value numerator

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@ -0,0 +1,193 @@
modulus_poly = [1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 1,
1]
modulus_poly_as_int = sum([(v << i) for i, v in enumerate(modulus_poly)])
degree = len(modulus_poly) - 1
two_to_the_degree = 2**degree
two_to_the_degree_m1 = 2**degree - 1
def galoistpl(a):
# 2 is not a primitive root, so we have to use 3 as our logarithm base
if a * 2 < two_to_the_degree:
return (a * 2) ^ a
else:
return (a * 2) ^ a ^ modulus_poly_as_int
# Precomputing a log table for increased speed of addition and multiplication
glogtable = [0] * (two_to_the_degree)
gexptable = []
v = 1
for i in range(two_to_the_degree_m1):
glogtable[v] = i
gexptable.append(v)
v = galoistpl(v)
gexptable += gexptable + gexptable
# Add two values in the Galois field
def galois_add(x, y):
return x ^ y
# In binary fields, addition and subtraction are the same thing
galois_sub = galois_add
# Multiply two values in the Galois field
def galois_mul(x, y):
return 0 if x*y == 0 else gexptable[glogtable[x] + glogtable[y]]
# Divide two values in the Galois field
def galois_div(x, y):
return 0 if x == 0 else gexptable[(glogtable[x] - glogtable[y]) % two_to_the_degree_m1]
# Evaluate a polynomial at a point
def eval_poly_at(p, x):
if x == 0:
return p[0]
y = 0
logx = glogtable[x]
for i, p_coeff in enumerate(p):
if p_coeff:
# Add x**i * coeff
y ^= gexptable[(logx * i + glogtable[p_coeff]) % two_to_the_degree_m1]
return y
# Given p+1 y values and x values with no errors, recovers the original
# p+1 degree polynomial.
# Lagrange interpolation works roughly in the following way.
# 1. Suppose you have a set of points, eg. x = [1, 2, 3], y = [2, 5, 10]
# 2. For each x, generate a polynomial which equals its corresponding
# y coordinate at that point and 0 at all other points provided.
# 3. Add these polynomials together.
def lagrange_interp(pieces, xs):
# Generate master numerator polynomial, eg. (x - x1) * (x - x2) * ... * (x - xn)
root = mk_root_2(xs)
#print(root)
assert len(root) == len(pieces) + 1
# print(root)
# Generate the derivative
d = derivative(root)
# Generate denominators by evaluating numerator polys at each x
denoms = multi_eval_2(d, xs)
# denoms = [eval_poly_at(d, xs[i]) for i in range(len(xs))]
# Generate output polynomial, which is the sum of the per-value numerator
# polynomials rescaled to have the right y values
return multi_root_derive(xs, [galois_div(p, d) for p, d in zip(pieces, denoms)])
def multi_root_derive(xs, muls):
if len(xs) == 1:
return [muls[0]]
R1 = mk_root_2(xs[:len(xs) // 2])
R2 = mk_root_2(xs[len(xs) // 2:])
x1 = karatsuba_mul(R1, multi_root_derive(xs[len(xs) // 2:], muls[len(muls) // 2:]) + [0])
x2 = karatsuba_mul(R2, multi_root_derive(xs[:len(xs) // 2], muls[:len(muls) // 2]) + [0])
o = [v1 ^ v2 for v1, v2 in zip(x1, x2)][:len(xs)]
# print(len(R1), len(x1), len(xs), len(o))
return o
def multi_root_derive_1(xs, muls):
o = [0] * len(xs)
for i in range(len(xs)):
_xs = xs[:i] + xs[(i+1):]
root = mk_root_2(_xs)
for j in range(len(root)):
o[j] ^= galois_mul(root[j], muls[i])
return o
a = 124
b = 8932
c = 12415
assert galois_mul(galois_add(a, b), c) == galois_add(galois_mul(a, c), galois_mul(b, c))
def karatsuba_mul(p1, p2):
L = len(p1)
# assert L == len(p2)
if L <= 16:
o = [0] * (L * 2)
for i, v1 in enumerate(p1):
for j, v2 in enumerate(p2):
if v1 and v2:
o[i + j] ^= gexptable[glogtable[v1] + glogtable[v2]]
return o
if L % 2:
p1 = p1 + [0]
p2 = p2 + [0]
L += 1
halflen = L // 2
low1 = p1[:halflen]
high1 = p1[halflen:]
sum1 = [l ^ h for l, h in zip(low1, high1)]
low2 = p2[:halflen]
high2 = p2[halflen:]
sum2 = [l ^ h for l, h in zip(low2, high2)]
z2 = karatsuba_mul(high1, high2)
z0 = karatsuba_mul(low1, low2)
z1 = [m ^ _z0 ^ _z2 for m, _z0, _z2 in zip(karatsuba_mul(sum1, sum2), z0, z2)]
o = z0[:halflen] + \
[a ^ b for a, b in zip(z0[halflen:], z1[:halflen])] + \
[a ^ b for a, b in zip(z2[:halflen], z1[halflen:])] + \
z2[halflen:]
return o
def mk_root_1(xs):
root = [1]
for x in xs:
logx = glogtable[x]
root.insert(0, 0)
for j in range(len(root)-1):
if root[j+1] and x:
root[j] ^= gexptable[glogtable[root[j+1]] + logx]
return root
def mk_root_2(xs):
if len(xs) >= 128:
return karatsuba_mul(mk_root_2(xs[:len(xs) // 2]), mk_root_2(xs[len(xs) // 2:]))[:len(xs) + 1]
root = [1]
for x in xs:
logx = glogtable[x]
root.insert(0, 0)
for j in range(len(root)-1):
if root[j+1] and x:
root[j] ^= gexptable[glogtable[root[j+1]] + logx]
return root
def derivative(root):
return [0 if i % 2 else r for i, r in enumerate(root[1:])]
# Credit to http://people.csail.mit.edu/madhu/ST12/scribe/lect06.pdf for the algorithm
def xn_mod_poly(p):
if len(p) == 1:
return [galois_div(1, p[0])]
halflen = len(p) // 2
lowinv = xn_mod_poly(p[:halflen])
submod_high = karatsuba_mul(lowinv, p[:halflen])[halflen:]
med = karatsuba_mul(p[halflen:], lowinv)[:halflen]
med_plus_high = [x ^ y for x, y in zip(med, submod_high)]
highinv = karatsuba_mul(med_plus_high, lowinv)
o = (lowinv + highinv)[:len(p)]
# assert karatsuba_mul(o, p)[:len(p)] == [1] + [0] * (len(p) - 1)
return o
def mod(a, b):
assert len(a) == 2 * (len(b) - 1)
L = len(b)
inv_rev_b = xn_mod_poly(b[::-1] + [0] * (len(a) - L))[:L]
quot = karatsuba_mul(inv_rev_b, a[::-1][:L])[:L-1][::-1]
subt = karatsuba_mul(b, quot + [0])[:-1]
o = [x ^ y for x, y in zip(a[:L-1], subt[:L-1])]
# assert [x^y for x, y in zip(karatsuba_mul(quot + [0], b), o)] == a
return o
def multi_eval_1(poly, xs):
return [eval_poly_at(poly, x) for x in xs]
def multi_eval_2(poly, xs):
if len(xs) <= 1024:
return [eval_poly_at(poly, x) for x in xs]
halflen = len(xs) // 2
return multi_eval_2(mod(poly, mk_root_2(xs[:halflen])), xs[:halflen]) + \
multi_eval_2(mod(poly, mk_root_2(xs[halflen:])), xs[halflen:])
# [eval_poly_at(poly, xs[-2]), eval_poly_at(poly, xs[-1])]

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@ -1,12 +1,12 @@
import random
import datetime
diffs = [257.74 * 10**12]
hashpower = diffs[0] / 14.7
times = [1491920186]
diffs = [286.86 * 10**12]
hashpower = diffs[0] / 15.0
times = [1492650109]
for i in range(3517029, 6010000):
for i in range(3566076, 6010000):
blocktime = random.expovariate(hashpower / diffs[-1])
adjfac = max(1 - int(blocktime / 10), -99) / 2048.
newdiff = diffs[-1] * (1 + adjfac)