How to calculate modulus of 5^55 modulus 221 without much use of calculator?
I guess there are some simple principles in number theory in cryptography to calculate such things.
Okay, so you want to calculate a^b mod m. First we'll take a naive approach and then see how we can refine it.
First, reduce a mod m. That means, find a number a1 so that 0 <= a1 < m and a = a1 mod m. Then repeatedly in a loop multiply by a1 and reduce again mod m. Thus, in pseudocode:
a1 = a reduced mod m
p = 1
for(int i = 1; i <= b; i++) {
p *= a1
p = p reduced mod m
}
By doing this, we avoid numbers larger than m^2. This is the key. The reason we avoid numbers larger than m^2 is because at every step 0 <= p < m and 0 <= a1 < m.
As an example, let's compute 5^55 mod 221. First, 5 is already reduced mod 221.
1 * 5 = 5 mod 221
5 * 5 = 25 mod 221
25 * 5 = 125 mod 221
125 * 5 = 183 mod 221
183 * 5 = 31 mod 221
31 * 5 = 155 mod 221
155 * 5 = 112 mod 221
112 * 5 = 118 mod 221
118 * 5 = 148 mod 221
148 * 5 = 77 mod 221
77 * 5 = 164 mod 221
164 * 5 = 157 mod 221
157 * 5 = 122 mod 221
122 * 5 = 168 mod 221
168 * 5 = 177 mod 221
177 * 5 = 1 mod 221
1 * 5 = 5 mod 221
5 * 5 = 25 mod 221
25 * 5 = 125 mod 221
125 * 5 = 183 mod 221
183 * 5 = 31 mod 221
31 * 5 = 155 mod 221
155 * 5 = 112 mod 221
112 * 5 = 118 mod 221
118 * 5 = 148 mod 221
148 * 5 = 77 mod 221
77 * 5 = 164 mod 221
164 * 5 = 157 mod 221
157 * 5 = 122 mod 221
122 * 5 = 168 mod 221
168 * 5 = 177 mod 221
177 * 5 = 1 mod 221
1 * 5 = 5 mod 221
5 * 5 = 25 mod 221
25 * 5 = 125 mod 221
125 * 5 = 183 mod 221
183 * 5 = 31 mod 221
31 * 5 = 155 mod 221
155 * 5 = 112 mod 221
112 * 5 = 118 mod 221
118 * 5 = 148 mod 221
148 * 5 = 77 mod 221
77 * 5 = 164 mod 221
164 * 5 = 157 mod 221
157 * 5 = 122 mod 221
122 * 5 = 168 mod 221
168 * 5 = 177 mod 221
177 * 5 = 1 mod 221
1 * 5 = 5 mod 221
5 * 5 = 25 mod 221
25 * 5 = 125 mod 221
125 * 5 = 183 mod 221
183 * 5 = 31 mod 221
31 * 5 = 155 mod 221
155 * 5 = 112 mod 221
Therefore, 5^55 = 112 mod 221.
Now, we can improve this by using exponentiation by squaring; this is the famous trick wherein we reduce exponentiation to requiring only log b multiplications instead of b. Note that with the algorithm that I described above, the exponentiation by squaring improvement, you end up with the right-to-left binary method.
a1 = a reduced mod m
p = 1
while (b > 0) {
if (b is odd) {
p *= a1
p = p reduced mod m
}
b /= 2
a1 = (a1 * a1) reduced mod m
}
Thus, since 55 = 110111 in binary
1 * (5^1 mod 221) = 5 mod 221
5 * (5^2 mod 221) = 125 mod 221
125 * (5^4 mod 221) = 112 mod 221
112 * (5^16 mod 221) = 112 mod 221
112 * (5^32 mod 221) = 112 mod 221
Therefore the answer is 5^55 = 112 mod 221. The reason this works is because
55 = 1 + 2 + 4 + 16 + 32
so that
5^55 = 5^(1 + 2 + 4 + 16 + 32) mod 221
= 5^1 * 5^2 * 5^4 * 5^16 * 5^32 mod 221
= 5 * 25 * 183 * 1 * 1 mod 221
= 22875 mod 221
= 112 mod 221
In the step where we calculate 5^1 mod 221, 5^2 mod 221, etc. we note that 5^(2^k) = 5^(2^(k-1)) * 5^(2^(k-1)) because 2^k = 2^(k-1) + 2^(k-1) so that we can first compute 5^1 and reduce mod 221, then square this and reduce mod 221 to obtain 5^2 mod 221, etc.
The above algorithm formalizes this idea.
To add to Jason's answer:
You can speed the process up (which might be helpful for very large exponents) using the binary expansion of the exponent. First calculate 5, 5^2, 5^4, 5^8 mod 221 - you do this by repeated squaring:
5^1 = 5(mod 221)
5^2 = 5^2 (mod 221) = 25(mod 221)
5^4 = (5^2)^2 = 25^2(mod 221) = 625 (mod 221) = 183(mod221)
5^8 = (5^4)^2 = 183^2(mod 221) = 33489 (mod 221) = 118(mod 221)
5^16 = (5^8)^2 = 118^2(mod 221) = 13924 (mod 221) = 1(mod 221)
5^32 = (5^16)^2 = 1^2(mod 221) = 1(mod 221)
Now we can write
55 = 1 + 2 + 4 + 16 + 32
so 5^55 = 5^1 * 5^2 * 5^4 * 5^16 * 5^32
= 5 * 25 * 625 * 1 * 1 (mod 221)
= 125 * 625 (mod 221)
= 125 * 183 (mod 183) - because 625 = 183 (mod 221)
= 22875 ( mod 221)
= 112 (mod 221)
You can see how for very large exponents this will be much faster (I believe it's log as opposed to linear in b, but not certain.)
/* The algorithm is from the book "Discrete Mathematics and Its
Applications 5th Edition" by Kenneth H. Rosen.
(base^exp)%mod
*/
int modular(int base, unsigned int exp, unsigned int mod)
{
int x = 1;
int power = base % mod;
for (int i = 0; i < sizeof(int) * 8; i++) {
int least_sig_bit = 0x00000001 & (exp >> i);
if (least_sig_bit)
x = (x * power) % mod;
power = (power * power) % mod;
}
return x;
}
5^55 mod221
= ( 5^10 * 5^10 * 5^10 * 5^10 * 5^10 * 5^5) mod221
= ( ( 5^10) mod221 * 5^10 * 5^10 * 5^10 * 5^10 * 5^5) mod221
= ( 77 * 5^10 * 5^10 * 5^10 * 5^10 * 5^5) mod221
= ( ( 77 * 5^10) mod221 * 5^10 * 5^10 * 5^10 * 5^5) mod221
= ( 183 * 5^10 * 5^10 * 5^10 * 5^5) mod221
= ( ( 183 * 5^10) mod221 * 5^10 * 5^10 * 5^5) mod221
= ( 168 * 5^10 * 5^10 * 5^5) mod221
= ( ( 168 * 5^10) mod 221 * 5^10 * 5^5) mod221
= ( 118 * 5^10 * 5^5) mod221
= ( ( 118 * 5^10) mod 221 * 5^5) mod221
= ( 25 * 5^5) mod221
= 112
What you're looking for is modular exponentiation, specifically modular binary exponentiation. This wikipedia link has pseudocode.
Chinese Remainder Theorem comes to mind as an initial point as 221 = 13 * 17. So, break this down into 2 parts that get combined in the end, one for mod 13 and one for mod 17. Second, I believe there is some proof of a^(p-1) = 1 mod p for all non zero a which also helps reduce your problem as 5^55 becomes 5^3 for the mod 13 case as 13*4=52. If you look under the subject of "Finite Fields" you may find some good results on how to solve this.
EDIT: The reason I mention the factors is that this creates a way to factor zero into non-zero elements as if you tried something like 13^2 * 17^4 mod 221, the answer is zero since 13*17=221. A lot of large numbers aren't going to be prime, though there are ways to find large primes as they are used a lot in cryptography and other areas within Mathematics.
This is part of code I made for IBAN validation. Feel free to use.
static void Main(string[] args)
{
int modulo = 97;
string input = Reverse("100020778788920323232343433");
int result = 0;
int lastRowValue = 1;
for (int i = 0; i < input.Length; i++)
{
// Calculating the modulus of a large number Wikipedia http://en.wikipedia.org/wiki/International_Bank_Account_Number
if (i > 0)
{
lastRowValue = ModuloByDigits(lastRowValue, modulo);
}
result += lastRowValue * int.Parse(input[i].ToString());
}
result = result % modulo;
Console.WriteLine(string.Format("Result: {0}", result));
}
public static int ModuloByDigits(int previousValue, int modulo)
{
// Calculating the modulus of a large number Wikipedia http://en.wikipedia.org/wiki/International_Bank_Account_Number
return ((previousValue * 10) % modulo);
}
public static string Reverse(string input)
{
char[] arr = input.ToCharArray();
Array.Reverse(arr);
return new string(arr);
}
Jason's answer in Java (note i < exp).
private static void testModulus() {
int bse = 5, exp = 55, mod = 221;
int a1 = bse % mod;
int p = 1;
System.out.println("1. " + (p % mod) + " * " + bse + " = " + (p % mod) * bse + " mod " + mod);
for (int i = 1; i < exp; i++) {
p *= a1;
System.out.println((i + 1) + ". " + (p % mod) + " * " + bse + " = " + ((p % mod) * bse) % mod + " mod " + mod);
p = (p % mod);
}
}
Just provide another implementation of Jason's answer by C.
After discussing with my classmates, based on Jason's explanation, I like the recursive version more if you don't care about the performance very much:
For example:
#include<stdio.h>
int mypow( int base, int pow, int mod ){
if( pow == 0 ) return 1;
if( pow % 2 == 0 ){
int tmp = mypow( base, pow >> 1, mod );
return tmp * tmp % mod;
}
else{
return base * mypow( base, pow - 1, mod ) % mod;
}
}
int main(){
printf("%d", mypow(5,55,221));
return 0;
}
This is called modular exponentiation(https://en.wikipedia.org/wiki/Modular_exponentiation).
Let's assume you have the following expression:
19 ^ 3 mod 7
Instead of powering 19 directly you can do the following:
(((19 mod 7) * 19) mod 7) * 19) mod 7
But this can take also a long time due to a lot of sequential multipliations and so you can multiply on squared values:
x mod N -> x ^ 2 mod N -> x ^ 4 mod -> ... x ^ 2 |log y| mod N
Modular exponentiation algorithm makes assumptions that:
x ^ y == (x ^ |y/2|) ^ 2 if y is even
x ^ y == x * ((x ^ |y/2|) ^ 2) if y is odd
And so recursive modular exponentiation algorithm will look like this in java:
/**
* Modular exponentiation algorithm
* #param x Assumption: x >= 0
* #param y Assumption: y >= 0
* #param N Assumption: N > 0
* #return x ^ y mod N
*/
public static long modExp(long x, long y, long N) {
if(y == 0)
return 1 % N;
long z = modExp(x, Math.abs(y/2), N);
if(y % 2 == 0)
return (long) ((Math.pow(z, 2)) % N);
return (long) ((x * Math.pow(z, 2)) % N);
}
Special thanks to #chux for found mistake with incorrect return value in case of y and 0 comparison.
Related
I have the following function with the variables:
b <- 1 ; d_uhpc <- 4
L_joint <- 8 ; A_bar <- 0.31
A_s <- (A_bar/L_joint)*12
L_unb <- 16 ; f_t <- 1.2
E_s <- 29000 ; E_uhpc <- 8000
ec <- function(x){
theta <- x[3]
eci <- seq(10^-3,1,10^-3)
while (TRUE) {
fc <- eci * E_uhpc
c <- (sqrt(A_s^2 * E_s^2 * eci^2 + fc * A_s * E_s * b * d_uhpc *eci + b^2 * f_t^2 * d_uhpc^2) +
b * f_t * d_uhpc - A_s * E_s *eci)/(b * fc + 2 * b * f_t)
ec <- (-2*theta*c)/L_unb
if (eci > abs(ec)) return("Error") else return(ec)
}
}
# sample rows
strain.analysis <- read.table(text="
L S theta
1 60 6 0.3876484
2 70 6 0.3650651
3 80 6 0.3619457
4 90 6 0.3089947
5 100 6 0.3131211
6 110 6 0.3479024", header=TRUE)
strain.analysis1 <- cbind(strain.analysis, vars = t(apply(strain.analysis,1,ec)))
The function does not understand the conditions correctly and just returns ec for all of the eci values regardless of the condition that it should only return ec when eci < abs(ec)
Below is an example of what I am trying to recreate in R.
Here's what I am thinking: your return statements in the while loop are incorrect as they exit the function ec and not only the while loop. I think your are after break. When I ran your code, with the eci as vector fixed, it only performed one loop for each row in the dataset.
Also, as mentioned before, eci cannot be a vector of length 1000. It has to start at 1E-6 and increment every loop with 1E-6 (per the figure). Your exit criteria are complicated and as we do not know what 0in is it cannot be correctly reconstructed.
Finally, in apply you can call a user-defined function but you have to pass it something.
Having said that, is this what you are after?
b <- 1
d_uhpc <- 4
L_joint <- 8
A_bar <- 0.31
A_s <- (A_bar/L_joint)*12
L_unb <- 16
f_t <- 1.2
E_s <- 29000
E_uhpc <- 8000
eci <- 1E-6
ec <- function(x){
#browser()
theta <- x[3]
while (TRUE) {
fc <- eci * E_uhpc
c <- (sqrt(A_s^2 * E_s^2 * eci^2 + fc * A_s * E_s * b * d_uhpc *eci + b^2 * f_t^2 * d_uhpc^2) +
b * f_t * d_uhpc - A_s * E_s *eci)/(b * fc + 2 * b * f_t)
ec <- (-2*theta*c)/L_unb
if (eci > abs(ec)) break
eci <- eci + 1E-6
}
if (c > d_uhpc | ((max(c(abs(ec), eci), na.rm = TRUE))/(min(c(abs(ec), eci), na.rm = TRUE))) - 1 > 0.05) return(NA_real_) else return(ec)
}
v <- apply(strain.analysis, 1, function(x) ec(x))
strain.analysis1 <- cbind(strain.analysis, v)
The code loops many times for each row of strain,analysis until the break condition is met. Based on the value of ec and eci the function returns either a value of NA_real_.
Here's how my output looks like:
> strain.analysis1
L S theta v
1 60 6 0.3876484 -0.06845568
2 70 6 0.3650651 -0.06447493
3 80 6 0.3619457 -0.06392507
4 90 6 0.3089947 -0.05459143
5 100 6 0.3131211 -0.05531879
6 110 6 0.3479024 -0.06144967
This is a more vectorized approach - it calculates all ec at once and then determines which ec meets the criteria. This means it is fast although it can use up more memory than #Paul van Oppen's solution.
eci <- seq(10^-6,1,10^-6)
fc <- eci * E_uhpc
const <- (sqrt(A_s^2 * E_s^2 * eci^2 + fc * A_s * E_s * b * d_uhpc *eci + b^2 * f_t^2 * d_uhpc^2) +
b * f_t * d_uhpc - A_s * E_s *eci)/(b * fc + 2 * b * f_t)
ec <- function(x){
theta = x[3]
ec = (-2*theta*const)/L_unb
comp = eci > abs(ec)
if (any(comp)) { ## at least one of our eci > abs(ec)
wm = which.max(comp)
if (comp[wm] == FALSE) ##which.max(c(FALSE, FALSE)) will still return something. We need NA_real_ in this case
return (NA_real_)
else
return(ec[wm])
}
else
return(ec[length(x)])
}
cbind(strain.analysis, V = apply(strain.analysis,1,ec))
#> L S theta V
#> 1 60 6 0.3876484 -0.06845568
#> 2 70 6 0.3650651 -0.06447493
#> 3 80 6 0.3619457 -0.06392507
#> 4 90 6 0.3089947 -0.05459143
#> 5 100 6 0.3131211 -0.05531879
#> 6 110 6 0.3479024 -0.06144967
For a couple of months now, I am trying to solve an operational research model with glpk on a mac. The thing is that I have searched throughout the internet and also I have tried to find help in universities based in Athens Greece but it seems that noone has a clue regarding the specific program. So, my MODEL is the following:
#PARAMETERS
#
param P;
param D;
param S;
param R;
param B;
param LS;
param CLNGs;
param Tp;
param H;
param Frest;
param MA;
param f;
param Cvr {1..B};
param Cvf {1..B};
param Vv {1..B};
param Qv {1..B};
param Demand {1..R};
param d {1..P, 1..D};
# VARIABLES
#
var z{1..B} >=0, integer;
var x{1..P,1..R,1..B,1..S,1..D} >=0, continuous;
var y{1..P,1..B,1..R,1..S,1..D} >=0, integer;
# OBJECTIVE FUNCTION
#
minimize F {p<>m}: sum {p in 1..P} sum {m in 1..D}
sum {v in 1..B}Cf[v]*d[p,m]*y[p,m,v] + H* sum {v in 1..B} Cr[v]*z[v] +
sum {s in 1..S} sum {r in 1..R} sum {v in 1..B} CLNG[s]*Q[v]*x[s,r,v];
# CONSTRAINTS
#
s.t. constr1 {r in 1..R, r<>p, r<>m }: sum {p in 1..P}
sum {v in 1..B}Q[v]*x[p,r,v] - sum{m in 1..D}
sum{v in 1..B}Q[v]*x[r,m,v] >=Demand[r];
s.t. constr2 {p in 1..P, r in 1..R, v in 1..B}: y[p,r,v] >= x[p,r,v];
s.t. constr3 { r in 1..R, v in 1..B}:
sum {p in 1..P} x[p,r,v] >= sum {m in 1..D} x[r,m,v];
s.t. constr4 { p in 1..P, v in 1..B, p<>m}:
sum {m in 1..D} y[m,p,v] = sum {m in 1..D} y[p,m,v];
s.t. constr5 {v in 1..B, p<>m}: H*z[v] >= 1/V[v] *sum{p in 1..P}
sum{m in 1..D} d[p,m]*y[p,m,v] +
sum{p in 1..P}*(T[p]*sum{m in 1..D}y[p,m,v]);
s.t. constr6 {p in 1..P, r in 1..R, v in 1..B}:
Q[v]*y[p,r,v] <= Frest*y[p,r,v];
s.t. constr7 {v in 1..LS, i<>j}: sum {i in 1..R}
sum {j in 1..R} x[i,j,v] = 0;
s.t. constr8 {s in 1..S}: sum {r in 1..R}
sum {v in 1..V} Q[v]*x[s,r,v] <= MA[s];
s.t. constr9 {s in 1..S, r in 1..R}: x[s,r,v] >= f*y[s,r,v];
end;
And my DATA:
#test comments
param P := 7;
param D := 7;
param S := 3;
param R := 4;
param B := 4;
param LS := 2;
param CLNGs := 200;
param Tp := 24;
param H := 30;
param Frest := 1000000000
param MA := 10000000000
param f :=0.8
param Cvr : 1 :=
1 510000
2 600000
3 700000
4 900000
;
param Cvf : 1 :=
1 22
2 26
3 32
4 42
;
param Vv : 1 :=
1 24
2 26
3 28
4 30
;
param Qv : 1 :=
1 5000
2 7500
3 10000
4 15000
;
param Demand : 1 :=
1 2595.9
2 3781.2
3 1668
4 372.9
;
param d: 1 2 3 4 5 6 7:=
1 0 1663 996 306 291 333 372
2 1663 0 2413 1867 1905 1856 2050
3 996 2413 0 924 938 761 1103
4 306 1867 924 0 107 202 110
5 291 1905 938 107 0 122 184
6 333 1856 761 202 122 0 302
7 372 1050 1103 110 184 302 0
;
end;
When I am trying to run this i get always the following error:
thesis.mod:29: syntax error in variable statement
Context: ..... P , 1 .. R , 1 .. B , 1 .. S , 1 .. D } >= 0 ,continuous
MathProg model processing error
Do you have any ideas on how to solve this?
There is no 'continuous' attribute defined in the var statement of the GMPL language. Variables are by default continuous.
Please, read doc/gmpl.pdf that comes with GLPK.
I want to calculate number of items waiting or queued over. Let's say, I have fixed capacity of 102 item/hour and different incoming items for 9 hours.
as data table:
dt<-data.table(hour = c(1,2,3,4,5,6,7,8,9),
incoming = c(78,102,115,117,105,99,91,80,71),
capacity = rep(102,9))
I want to calculate queued items in each period.
In 1 and 2 capacity is enough and queue is 0.
In 3, 13 items are queued
In 4, 15+13 backlogged items are queued.
In 6, there were 31 backlogged items and 3 items are deducted so 28 were queued.
I have tried several options but could not figure out how to calculate.
Result should be:
Explicit looping in R won't get you far, and I don't see a vectorized solution for this, but this is trivial to solve using Rcpp:
library(Rcpp)
cppFunction("NumericVector queue(NumericVector x) {
NumericVector res(x.size());
res[0] = std::max<double>(0, x[0]);
for (int i = 1, size = x.size(); i < size; ++i) {
res[i] = std::max<double>(0, res[i-1] + x[i]);
}
return res;
}")
dt[, queued := queue(incoming - capacity)][]
# hour incoming capacity queued
#1: 1 78 102 0
#2: 2 102 102 0
#3: 3 115 102 13
#4: 4 117 102 28
#5: 5 105 102 31
#6: 6 99 102 28
#7: 7 91 102 17
#8: 8 80 102 0
#9: 9 71 102 0
I'd create a separate function to get queued number like #sebastian-c did, but with #R.S. 's logic. Like this
get_queue <- function(x){
n <- length(x)
y <- c(max(0, x[[1]]), rep(0, n - 1))
for(i in 2:n){
y[i] <- max(0, y[i - 1] + x[i])
}
y
}
And then
dt[,incoming_capacity := incoming - capacity]
dt[,queued := get_queue(incoming_capacity)]
Another alternative:
require(data.table)
dt<-data.table(hour = c(1,2,3,4,5,6,7,8,9),
incoming = c(78,102,115,117,105,99,91,80,71),
capacity = rep(102,9))
dt$incoming_capactity<- dt$incoming-dt$capacity
dt$carriedover<- 0
dt$carriedover[1]<- max(0,dt$incoming_capactity[1]) #added
for( i in 2:length(dt$carriedover)) {
dt$carriedover[i]<- max(0,dt$incoming_capactity[i] + dt$carriedover[i-1])
}
dt
I have an OpenCL code where i invoke clEnqueueWriteBuffer and clEnqueueNDRangeKernel inside a loop multiple time. I measure the data transfer time and the kernel execution time of each loop using GetLocalTime function. The issue I am facing is that the clEnqueueWriteBuffer and clEnqueueNDRangeKernel in the first iteration takes much longer to complete than the ones in the second iteration. Why does this happen?
I am working on a system with ARM A10 APU. My opencl loop code is :
for(j = 0; j < PARTITION_COUNT; j++){
//Writing to input buffers
GetLocalTime(&start);
clEnqueueWriteBuffer(queue[0], buf_A, CL_TRUE, 0, PARTITION_SIZE * sizeof(int), input_A + (PARTITION_SIZE * j), 0, NULL, &eventList[0]);
checkErr(cl_err, "clEnqueueWriteBuffer : buf_A");
clEnqueueWriteBuffer(queue[1], buf_B, CL_TRUE, 0, PARTITION_SIZE * sizeof(int), input_B + (PARTITION_SIZE * j), 0, NULL, &eventList[1]);
checkErr(cl_err, "clEnqueueWriteBuffer : buf_B");
clEnqueueWriteBuffer(queue[2], buf_C, CL_TRUE, 0, PARTITION_SIZE * sizeof(int), input_C + (PARTITION_SIZE * j), 0, NULL, &eventList[2]);
checkErr(cl_err, "clEnqueueWriteBuffer : buf_C");
clEnqueueWriteBuffer(queue[3], buf_D, CL_TRUE, 0, PARTITION_SIZE * sizeof(int), input_D + (PARTITION_SIZE * j), 0, NULL, &eventList[3]);
checkErr(cl_err, "clEnqueueWriteBuffer : buf_D");
clFinish(queue[0]);
clFinish(queue[1]);
clFinish(queue[2]);
clFinish(queue[3]);
//getting end time
GetLocalTime(&end);
//displaying final time
cout<<"\nTime : "<<start.wMinute<<" "<<start.wSecond<<" "<<start.wMilliseconds;
cout<<"\nTime : "<<end.wMinute<<" "<<end.wSecond<<" "<<end.wMilliseconds;
GetLocalTime(&start);
cl_err = clEnqueueNDRangeKernel(queue[4],kernel[Q6_PROGRAM_ID][FILTER1_KERNEL],1,NULL,&globalSize,&localSize,4,eventList,&eventList[4]);
checkErr(cl_err, "clEnqueueNDRangeKernel : filter1_kernel");
//clFinish(queue[4]);
//Invoking the second filter kernel
cl_err = clEnqueueNDRangeKernel(queue[5],kernel[Q6_PROGRAM_ID][FILTER2_KERNEL],1,NULL,&globalSize,&localSize,1,eventList + 4,&eventList[5]);
checkErr(cl_err, "clEnqueueNDRangeKernel : filter2_kernel");
//clFinish(queue[5]);
//Invoking the third filter kernel
cl_err = clEnqueueNDRangeKernel(queue[6],kernel[Q6_PROGRAM_ID][FILTER3_KERNEL],1,NULL,&globalSize,&localSize,1,eventList + 5,&eventList[6]);
checkErr(cl_err, "clEnqueueNDRangeKernel : filter3_kernel");
//clFinish(queue[6]);
//Invoking the aggregate kernel
cl_err = clEnqueueNDRangeKernel(queue[8],kernel[Q6_PROGRAM_ID][AGGREGATE_KERNEL],1,NULL,&globalSize,&localSize,1,eventList + 6,&eventList[7]);
checkErr(cl_err, "clEnqueueNDRangeKernel : aggregate kernel");
output_A = (int *)clEnqueueMapBuffer(queue[9],output_buf_A,CL_TRUE, CL_MAP_READ, 0, rLen * sizeof(int), 1, eventList + 7, &eventList[8], &cl_err);
checkErr(cl_err, "clEnqueueReadBuffer : output_A");
for(i = 0; i < rLen; i++){
if(output_A[i] > 0){
//cout<<"\n"<<output_A[i];
sum += output_A[i];
}
}
clFinish(queue[4]);
clFinish(queue[5]);
clFinish(queue[6]);
clFinish(queue[8]);
clFinish(queue[9]);
GetLocalTime(&end);
//displaying final time
cout<<"\nTime1 : "<<start.wMinute<<" "<<start.wSecond<<" "<<start.wMilliseconds;
cout<<"\nTime1 : "<<end.wMinute<<" "<<end.wSecond<<" "<<end.wMilliseconds;
}
GetLocalTime(&end1);
//displaying final time
cout<<"\nTime2 : "<<start1.wMinute<<" "<<start1.wSecond<<" "<<start1.wMilliseconds;
cout<<"\nTime2 : "<<end1.wMinute<<" "<<end1.wSecond<<" "<<end1.wMilliseconds;
my output is :
Time : 27 30 404
Time : 27 30 466
Time1 : 27 30 474
Time1 : 27 30 547
Time : 27 30 551
Time : 27 30 555
Time1 : 27 30 561
Time1 : 27 30 582
Time : 27 30 587
Time : 27 30 591
Time1 : 27 30 597
Time1 : 27 30 617
Time : 27 30 622
Time : 27 30 627
Time1 : 27 30 638
Time1 : 27 30 659
Time : 27 30 670
Time : 27 30 675
Time1 : 27 30 679
Time1 : 27 30 699
Time : 27 30 706
Time : 27 30 711
Time1 : 27 30 718
Time1 : 27 30 737
Time2 : 27 30 404
Time2 : 27 30 743
PROGRAM EXECUTION OVER
I am solving an example problem, RSA algorithm
I have been given two prime numbers 7 and 11. Let's say p=7 and q=11
I have to calculate the decryption key, d, for some encryption key, e.
Firstly I calculated n=p*q which implies that n=77.
Suppose that e=13,
to calculate d I used the formula d*e = 1 mod fi,
where fi=(p-1)(q-1), and so fi=60
The final equation becomes 13*d = 1 mod fi
According to some solved example
d is calculated to be 37, how is this result obtained?
Any help would be appreciated.
i think this is what you are looking for
Verifying the answer is easy, finding it in the first place, a little more work.
Verification:
13 * 37 = 481
481 = 8 * 60 + 1
Hence if you divide 13 * 37 by 60 you have remainder 1.
Alternate answers:
Any integer of the form (37 + 60 k) where k is any integer is also a solution. (97, -23, etc.)
To find the solution you can proceed as follows:
Solve:
13 d = 1 + 60 k
mod 13:
0 = 1 + 8k (mod 13)
8k = -1 (mod 13)
Add 13's until a multiple of 8 is found:
8k = 12 or 25 or 38 or 51 or 64 .... aha a multiple of 8!
k = 64 / 8 = 8
Substitute k = 8 back into 13 d = 1 + 60 k
13 d = 1 + 8 * 60 = 481
481 /13 = 37
and that is the answer.
Use the extended Euclidean algorithm to compute integers x and y such that
13*x+60*y=1
Then x is the answer you're looking for, mod 60.